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Please note that this page does not hosts or makes available any of the listed filenames. You
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| 0. (1Hack.Us) Premium Tutorials-Guides-Articles _ Community based Forum.url |
377B |
| 01. 01 HS Intro Dan And Cezanne V2-2K8KFEUxNbw.en.vtt |
1.66KB |
| 01. 01 HS Intro Dan And Cezanne V2-2K8KFEUxNbw.mp4 |
6.00MB |
| 01. 01 HS Intro Dan And Cezanne V2-2K8KFEUxNbw.zh-CN.vtt |
1.46KB |
| 01. 04 How Does Amazon Decide Which Features To Work On-KYG_LWDhg4I.en.vtt |
7.88KB |
| 01. 04 How Does Amazon Decide Which Features To Work On-KYG_LWDhg4I.mp4 |
60.52MB |
| 01. 04 How Does Amazon Decide Which Features To Work On-KYG_LWDhg4I.zh-CN.vtt |
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| 01. 05 Can You Explain The Idea Behind The GitHub Respository-Hk9ChDtv_nQ.en.vtt |
8.57KB |
| 01. 05 Can You Explain The Idea Behind The GitHub Respository-Hk9ChDtv_nQ.mp4 |
64.46MB |
| 01. 05 Can You Explain The Idea Behind The GitHub Respository-Hk9ChDtv_nQ.zh-CN.vtt |
6.91KB |
| 01. 06 Does Sagemaker Work With Certain Products Or Use Cases-9HSJp_i9LFw.en.vtt |
4.04KB |
| 01. 06 Does Sagemaker Work With Certain Products Or Use Cases-9HSJp_i9LFw.mp4 |
40.97MB |
| 01. 06 Does Sagemaker Work With Certain Products Or Use Cases-9HSJp_i9LFw.zh-CN.vtt |
3.40KB |
| 01. 1 SentimentRNN Intro V1-bQWUuaMc9ZI.en.vtt |
2.17KB |
| 01. 1 SentimentRNN Intro V1-bQWUuaMc9ZI.mp4 |
2.20MB |
| 01. 1 SentimentRNN Intro V1-bQWUuaMc9ZI.zh-CN.vtt |
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| 01. 1 Weight Initialization V1-Ehc60si91Wg.en.vtt |
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| 01. 1 Weight Initialization V1-Ehc60si91Wg.mp4 |
11.60MB |
| 01. 1 Weight Initialization V1-Ehc60si91Wg.pt-BR.vtt |
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| 01. 1 Weight Initialization V1-Ehc60si91Wg.zh-CN.vtt |
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| 01. Adding Commits To A Repo - Intro-sLcOFQ4mGvo.ar.vtt |
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| 01. Adding Commits To A Repo - Intro-sLcOFQ4mGvo.en.vtt |
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| 01. Adding Commits To A Repo - Intro-sLcOFQ4mGvo.mp4 |
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| 01. Adding Commits To A Repo - Intro-sLcOFQ4mGvo.pt-BR.vtt |
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| 01. Adding Commits To A Repo - Intro-sLcOFQ4mGvo.zh-CN.vtt |
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| 01. Apresentando Alexis-38ExGpdyvJI.en.vtt |
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| 01. Apresentando Alexis-38ExGpdyvJI.mp4 |
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| 01. Apresentando Alexis-38ExGpdyvJI.pt-BR.vtt |
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| 01. Apresentando Alexis-38ExGpdyvJI.zh-CN.vtt |
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| 01. A Repository_s History - Intro-UBmg3syQS0E.ar.vtt |
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| 01. A Repository_s History - Intro-UBmg3syQS0E.en.vtt |
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| 01. A Repository_s History - Intro-UBmg3syQS0E.mp4 |
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| 01. A Repository_s History - Intro-UBmg3syQS0E.pt-BR.vtt |
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| 01. A Repository_s History - Intro-UBmg3syQS0E.zh-CN.vtt |
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| 01. Arvato Final Project-qBR6A0IQXEE.en.vtt |
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| 01. Arvato Final Project-qBR6A0IQXEE.mp4 |
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| 01. Arvato Final Project-qBR6A0IQXEE.pt-BR.vtt |
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| 01. Arvato Final Project-qBR6A0IQXEE.zh-CN.vtt |
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| 01. Autoencoders.html |
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| 01. Autoencoders 01 Autoencoders V2 RENDER V2-a5zHMWOq0fc.en.vtt |
3.87KB |
| 01. Autoencoders 01 Autoencoders V2 RENDER V2-a5zHMWOq0fc.mp4 |
5.61MB |
| 01. Autoencoders 01 Autoencoders V2 RENDER V2-a5zHMWOq0fc.pt-BR.vtt |
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| 01. AWS Overview.html |
8.52KB |
| 01. Capstone project.html |
5.16KB |
| 01. Capstone Proposal.html |
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| 01. Congratulations!.html |
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| 01. Creating New Repositories - Intro-KT163BkqIeg.ar.vtt |
2.38KB |
| 01. Creating New Repositories - Intro-KT163BkqIeg.en.vtt |
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| 01. Creating New Repositories - Intro-KT163BkqIeg.mp4 |
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| 01. Creating New Repositories - Intro-KT163BkqIeg.pt-BR.vtt |
1.91KB |
| 01. Creating New Repositories - Intro-KT163BkqIeg.zh-CN.vtt |
1.68KB |
| 01. Deploying a Model in SageMaker.html |
9.05KB |
| 01. Deploying A Model With Sagemakerv2 RENDER V1 V2-nJCc4_9-iAQ.en.vtt |
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| 01. Deploying A Model With Sagemakerv2 RENDER V1 V2-nJCc4_9-iAQ.mp4 |
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| 01. Deploying A Model With Sagemakerv2 RENDER V1 V2-nJCc4_9-iAQ.zh-CN.vtt |
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| 01. Deploying a Sentiment Analysis Model-LWcJtUKVkzo.en.vtt |
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| 01. Deploying a Sentiment Analysis Model-LWcJtUKVkzo.mp4 |
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| 01. Deploying a Sentiment Analysis Model-LWcJtUKVkzo.zh-CN.vtt |
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| 01. Deployment Project.html |
5.96KB |
| 01. FAQ.html |
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| 01. Fraud Detection.html |
7.69KB |
| 01. Get Opportunities with LinkedIn.html |
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| 01. Gitfinal L1 01 Welcome-lbR82UD5F0c.ar.vtt |
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| 01. Gitfinal L1 01 Welcome-lbR82UD5F0c.en.vtt |
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| 01. Gitfinal L1 01 Welcome-lbR82UD5F0c.mp4 |
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| 01. Gitfinal L1 01 Welcome-lbR82UD5F0c.pt-BR.vtt |
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| 01. Gitfinal L1 01 Welcome-lbR82UD5F0c.zh-CN.vtt |
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| 01. Gitfinal L1 03 Version Control Systems-b7TjsVoTo3Q.ar.vtt |
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| 01. Gitfinal L1 03 Version Control Systems-b7TjsVoTo3Q.en.vtt |
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| 01. Gitfinal L1 03 Version Control Systems-b7TjsVoTo3Q.mp4 |
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| 01. Gitfinal L1 03 Version Control Systems-b7TjsVoTo3Q.pt-BR.vtt |
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| 01. Gitfinal L1 03 Version Control Systems-b7TjsVoTo3Q.zh-CN.vtt |
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| 01. Hyperparameter Tuning.html |
7.98KB |
| 01. Implementing RNNs.html |
6.70KB |
| 01. Interview Segment Developing SageMaker.html |
8.19KB |
| 01. Intro.html |
5.96KB |
| 01. Intro.html |
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| 01. Intro.html |
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| 01. Intro.html |
5.53KB |
| 01. Intro.html |
5.37KB |
| 01. Introducing Alexis.html |
10.79KB |
| 01. Introducing Cezanne _ Dan.html |
9.16KB |
| 01. Introduction.html |
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| 01. Introduction.html |
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| 01. Introduction.html |
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| 01. Introduction.html |
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| 01. Introduction.html |
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| 01. Introduction.html |
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| 01. Introduction-5DfFaAl1Wmc.en.vtt |
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| 01. Introduction-5DfFaAl1Wmc.mp4 |
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| 01. Introduction-5DfFaAl1Wmc.pt-BR.vtt |
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| 01. Introduction-5DfFaAl1Wmc.zh-CN.vtt |
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| 01. Introduction to Amazon SageMaker.html |
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| 01. Introduction to GPU Workspaces.html |
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| 01. Introduction To Software Engineering-7kphieW4yl4.en.vtt |
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| 01. Introduction To Software Engineering-7kphieW4yl4.mp4 |
14.89MB |
| 01. Introduction To Software Engineering-7kphieW4yl4.pt-BR.vtt |
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| 01. Introduction To Software Engineering-7kphieW4yl4.zh-CN.vtt |
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| 01. L2 01 Fraud Detection V1 RENDER V2-zDnyR5Tci5M.en.vtt |
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| 01. L2 01 Fraud Detection V1 RENDER V2-zDnyR5Tci5M.mp4 |
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| 01. L2 01 Fraud Detection V1 RENDER V2-zDnyR5Tci5M.zh-CN.vtt |
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| 01. L2 01 Intro V1 V1-z7v7oa--W48.en.vtt |
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| 01. L2 01 Intro V1 V1-z7v7oa--W48.mp4 |
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| 01. L2 01 Intro V1 V1-z7v7oa--W48.pt-BR.vtt |
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| 01. L2 01 Intro V1 V1-z7v7oa--W48.zh-CN.vtt |
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| 01. L2 2 01 Intro V1 V2-QO2GYq8q92E.en.vtt |
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| 01. L3 00 Intro V2-g_GYZpcVcFE.en.vtt |
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| 01. L3 00 Intro V2-g_GYZpcVcFE.mp4 |
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| 01. L3 00 Intro V2-g_GYZpcVcFE.zh-CN.vtt |
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| 01. L3 03 Time Series Forecasting-U8k2Fl2zgJ8.en.vtt |
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| 01. L3 03 Time Series Forecasting-U8k2Fl2zgJ8.mp4 |
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| 01. L4 00 Intro V2-ohVX3RUTghg.en.vtt |
988B |
| 01. L4 00 Intro V2-ohVX3RUTghg.mp4 |
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| 01. L4 Intro V2--PGMIIXFCgg.en.vtt |
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| 01. L4 Intro V2--PGMIIXFCgg.mp4 |
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| 01. L4 Intro V2--PGMIIXFCgg.pt-BR.vtt |
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| 01. L5 00 Intro V2-7wI168JzBiU.en.vtt |
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| 01. L5 00 Intro V2-7wI168JzBiU.mp4 |
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| 01. L5 00 Intro V2-7wI168JzBiU.zh-CN.vtt |
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| 01. M4L31 HSA Implementing RNNs V2 RENDERv1 V2-BHoiwB61ays.en.vtt |
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| 01. M4L31 HSA Implementing RNNs V2 RENDERv1 V2-BHoiwB61ays.mp4 |
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| 01. M4L31 HSA Implementing RNNs V2 RENDERv1 V2-BHoiwB61ays.pt-BR.vtt |
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| 01. M4L31 HSA Implementing RNNs V2 RENDERv1 V2-BHoiwB61ays.zh-CN.vtt |
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| 01. Natural Language Processing-UQBxJzoCp-I.en.vtt |
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| 01. Natural Language Processing-UQBxJzoCp-I.mp4 |
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| 01. Natural Language Processing-UQBxJzoCp-I.pt-BR.vtt |
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| 01. Natural Language Processing-UQBxJzoCp-I.zh-CN.vtt |
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| 01. NLP and Pipelines.html |
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| 01. Pre-Notebook Custom Models _ Moon Data.html |
9.71KB |
| 01. Project Overview.html |
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| 01. Project Overview.html |
8.72KB |
| 01. Prove Your Skills With GitHub.html |
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| 01. Sentiment RNN, Introduction.html |
6.75KB |
| 01. Tagging, Branching, And Merging - Intro-sMf_r4_z-Ls.mp4 |
6.40MB |
| 01. Time-Series Forecasting.html |
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| 01. Transfer Learning.html |
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| 01. Transfer Learning-yfPEROi3SPU.en.vtt |
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| 01. Transfer Learning-yfPEROi3SPU.mp4 |
5.70MB |
| 01. Transfer Learning-yfPEROi3SPU.pt-BR.vtt |
2.41KB |
| 01. Transfer Learning-yfPEROi3SPU.zh-CN.vtt |
2.27KB |
| 01. Updating a Model.html |
7.94KB |
| 01. Weight Initialization.html |
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| 01. Welcome!.html |
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| 01. Welcome.html |
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| 01. Welcome To Deployment-jQ2IZzga8Nw.en.vtt |
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| 01. Welcome To Deployment-jQ2IZzga8Nw.mp4 |
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| 01. Welcome To Deployment-jQ2IZzga8Nw.zh-CN.vtt |
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| 01. Welcome to the Machine Learning Engineer Program _ Projects.html |
9.54KB |
| 01. What is Version Control.html |
9.59KB |
| 01. Why Network-exjEm9Paszk.ar.vtt |
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| 01. Why Network-exjEm9Paszk.en.vtt |
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| 01. Why Network-exjEm9Paszk.es-MX.vtt |
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| 01. Why Network-exjEm9Paszk.ja-JP.vtt |
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| 01. Why Network-exjEm9Paszk.mp4 |
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| 01. Why Network-exjEm9Paszk.pt-BR.vtt |
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| 01. Why Network-exjEm9Paszk.zh-CN.vtt |
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| 02. 01 Time Series Notebook V2-OZJu6or8Fl0.en.vtt |
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| 02. 01 Time Series Notebook V2-OZJu6or8Fl0.mp4 |
9.75MB |
| 02. 01 What Is Amazon Sagemaker-JWRtWcd92E4.en.vtt |
4.14KB |
| 02. 01 What Is Amazon Sagemaker-JWRtWcd92E4.mp4 |
17.48MB |
| 02. 01 What Is Amazon Sagemaker-JWRtWcd92E4.zh-CN.vtt |
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| 02. 02 Time Series Prediction V2-xV5jHLFfJbQ.en.vtt |
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| 02. 02 Time Series Prediction V2-xV5jHLFfJbQ.mp4 |
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| 02. 02 Time Series Prediction V2-xV5jHLFfJbQ.pt-BR.vtt |
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| 02. 02 Time Series Prediction V2-xV5jHLFfJbQ.zh-CN.vtt |
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| 02. 02 What Applications Are Enabled By Amazon-iXN30g70PJ0.en.vtt |
2.95KB |
| 02. 02 What Applications Are Enabled By Amazon-iXN30g70PJ0.mp4 |
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| 02. 02 What Applications Are Enabled By Amazon-iXN30g70PJ0.zh-CN.vtt |
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| 02. 03 Why Should Students Gain Skills In Sagemaker And Cloud Services-Hp6qTdiqU3g.en.vtt |
6.29KB |
| 02. 03 Why Should Students Gain Skills In Sagemaker And Cloud Services-Hp6qTdiqU3g.mp4 |
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| 02. 03 Why Should Students Gain Skills In Sagemaker And Cloud Services-Hp6qTdiqU3g.zh-CN.vtt |
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| 02. 07 How Do You Label Data At Scale-G_E5N6k2knA.en.vtt |
3.92KB |
| 02. 07 How Do You Label Data At Scale-G_E5N6k2knA.mp4 |
35.77MB |
| 02. 07 How Do You Label Data At Scale-G_E5N6k2knA.zh-CN.vtt |
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| 02. 08 What_S Your Prediction Of What Sagemaker Will Prioritize In The Next 1-2 Years-git73JsQC1Y.en.vtt |
8.98KB |
| 02. 08 What_S Your Prediction Of What Sagemaker Will Prioritize In The Next 1-2 Years-git73JsQC1Y.mp4 |
71.01MB |
| 02. 08 What_S Your Prediction Of What Sagemaker Will Prioritize In The Next 1-2 Years-git73JsQC1Y.zh-CN.vtt |
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| 02. 18 Moon Data Custom Model V1-vb5ojq8Jw7k.en.vtt |
7.45KB |
| 02. 18 Moon Data Custom Model V1-vb5ojq8Jw7k.mp4 |
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| 02. 18 Moon Data Custom Model V1-vb5ojq8Jw7k.zh-CN.vtt |
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| 02. 2 Constant Weights V1-zR4fECgeZ7Y.en.vtt |
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| 02. 2 Constant Weights V1-zR4fECgeZ7Y.mp4 |
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| 02. 2 Constant Weights V1-zR4fECgeZ7Y.pt-BR.vtt |
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| 02. 2 Simple Autoencoder V2-KbmfyDNxL5U.en.vtt |
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| 02. 2 Simple Autoencoder V2-KbmfyDNxL5U.mp4 |
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| 02. 2 Simple Autoencoder V2-KbmfyDNxL5U.pt-BR.vtt |
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| 02. A Linear Autoencoder.html |
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| 02. Aplicações de CNNs-HrYNL_1SV2Y.en.vtt |
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| 02. Aplicações de CNNs-HrYNL_1SV2Y.mp4 |
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| 02. Aplicações de CNNs-HrYNL_1SV2Y.pt-BR.vtt |
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| 02. Aplicações de CNNs-HrYNL_1SV2Y.zh-CN.vtt |
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| 02. Applications of CNNs.html |
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| 02. AWS Setup Instructions for Regular account.html |
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| 02. AWS Setup Instructions for Regular account.html |
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| 02. Boston Housing Example - Deploying the Model.html |
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| 02. Building a Sentiment Analysis Model (XGBoost).html |
7.48KB |
| 02. Clean and Modular Code.html |
11.50KB |
| 02. Constant Weights.html |
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| 02. Containment.html |
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| 02. Course Overview.html |
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| 02. Create A Repo From Scratch.html |
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| 02. Deployment L3 C1 V1-0PBsV-SzSlo.mp4 |
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| 02. Deployment L5 C1 V1-dwRkA0ig3uU.zh-CN.vtt |
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| 02. Displaying A Repository_s Commits.html |
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| 02. Forecasting Energy Consumption, Notebook.html |
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| 02. Git Add.html |
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| 02. How NLP Pipelines Work.html |
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| 02. Interview Segment New Features.html |
6.93KB |
| 02. Interview Segment What is SageMaker and Why Learn It.html |
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| 02. Introduction.html |
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| 02. Introduction to Hyperparameter Tuning.html |
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| 02. Introduction-Vnj2VNQROtI.ar.vtt |
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| 02. Introduction-Vnj2VNQROtI.en.vtt |
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| 02. Introduction-Vnj2VNQROtI.zh-CN.vtt |
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| 02. L1 02 Course Overview V1 V4-v-DB0W_I2n8.en.vtt |
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| 02. Lesson Overview.html |
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| 02. Meet Chris-0ccflD9x5WU.ar.vtt |
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| 02. Modifying The Last Commit.html |
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| 02. Moon Data _ Custom Models.html |
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| 02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 11 Google Docs Revision History Walkthrough-GcvvbdKEchk.ar.vtt |
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| 02. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 11 Git Log Output Explained-xJfurQcVYfo.ar.vtt |
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| 02. NLP M1-L1 01 NLP Pipeline-vJx6oKlu_MM.en.vtt |
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| 02. Pre-Notebook Payment Fraud Detection.html |
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| 02. Pre-Notebook Sentiment RNN.html |
8.95KB |
| 02. Procedural vs. Object-Oriented Programming.html |
13.66KB |
| 02. Program Structure.html |
9.30KB |
| 02. Setting up a Notebook Instance.html |
9.42KB |
| 02. Software _ Data Requirements.html |
8.72KB |
| 02. Support.html |
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| 02. Tagging.html |
18.29KB |
| 02. Testing.html |
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| 02. Time-Series Prediction.html |
7.45KB |
| 02. Troubleshooting Possible Errors.html |
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| 02. Useful Layers.html |
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| 02. Use Your Story to Stand Out.html |
8.75KB |
| 02. Version Control In Daily Use.html |
10.95KB |
| 02. What_s Ahead.html |
9.58KB |
| 02. Workspace Playground.html |
5.78KB |
| 02. Workspace Portfolio Exercise.html |
6.71KB |
| 03. 01 Transaction Data V1-bF65I3J6aqQ.en.vtt |
5.50KB |
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15.11MB |
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| 03. 03 Fine Tuning V1 RENDER V2-XOyb315xYbw.en.vtt |
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| 03. 03 Training Memory V1-sx7T_KP5v9I.en.vtt |
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| 03. 09 Do You Have Advice For Someone Who Wants To Learn More-Wgq4eukacqE.en.vtt |
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| 03. 19 Uploading To S3 V1-Mz08Bac6h2Y.en.vtt |
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| 03. AWS SageMaker.html |
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| 03. Boston Housing Example - Tuning the Model.html |
8.72KB |
| 03. Boston Housing In-Depth - Deploying the Model.html |
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| 03. Branching.html |
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| 03. Building a Sentiment Analysis Model (Linear Learner).html |
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| 03. Changing How Git Log Displays Information.html |
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| 03. Class, Object, Method and Attribute.html |
13.21KB |
| 03. Clone An Existing Repo.html |
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| 03. ConNet 01 LessonOutline V1 V1-77LzWE1qQrc.en.vtt |
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| 03. Course Outline, Case Studies.html |
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| 03. Elevator Pitch-S-nAHPrkQrQ.ar.vtt |
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| 03. Exercise Payment Transaction Data.html |
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| 03. Fine-Tuning.html |
17.89KB |
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17.24KB |
| 03. Git and Version Control Terminology.html |
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| 03. Git Commit.html |
22.29KB |
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| 03. GitHub profile important items.html |
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| 03. GPU Workspace Playground.html |
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| 03. Interview Segment Further Learning.html |
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| 03. L1 03 Meet Andrew V1 V2-IPSwDqqk2Cc.en.vtt |
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| 03. L1 03 Meet Andrew V1 V2-IPSwDqqk2Cc.pt-BR.vtt |
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| 03. L1 03 Meet Andrew V1 V2-IPSwDqqk2Cc.zh-CN.vtt |
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| 03. L2 03 Clean Mod Code Vid 2 V1 V1-9bxtHpPvXE0.en.vtt |
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| 03. L2 03 Clean Mod Code Vid 2 V1 V1-9bxtHpPvXE0.pt-BR.vtt |
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| 03. L2 2 03 Testing Data Science V1 V4-AsnstNEMv1c.en.vtt |
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| 03. L3 03 Class Obj Methods Attributes V1 1 V2-yvVMJt09HuA.en.vtt |
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| 03. Lesson Outline.html |
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| 03. Machine Learning Workflow - Part 1 Introduction--ZtVV7RvGYY.en.vtt |
1.64KB |
| 03. Machine Learning Workflow - Part 1 Introduction--ZtVV7RvGYY.mp4 |
4.44MB |
| 03. Machine Learning Workflow - Part 1 Introduction--ZtVV7RvGYY.zh-CN.vtt |
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| 03. Meet Andrew.html |
5.78KB |
| 03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 15 Git The Big Picture-dVil8e0yptQ.ar.vtt |
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| 03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 18 Recap-xqD9ImXXXHk.ar.vtt |
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| 03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 25 Git Log Vs Git Log --Oneline Walkthru-rn6v_QgYFnU.ar.vtt |
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| 03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 25 Git Log Vs Git Log --Oneline Walkthru-rn6v_QgYFnU.en.vtt |
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1.22KB |
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| 03. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 27 Confession Corner-xtsugblSwrU.ar.vtt |
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| 03. Notebook Calculate Containment.html |
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| 03. Notebook Sentiment RNN.html |
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| 03. Possible Projects.html |
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| 03. Pre-Notebook Linear Autoencoder.html |
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| 03. Pre-Notebook Time-Series Forecasting.html |
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| 03. Problem Introduction.html |
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| 03. Random Uniform.html |
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| 03. Refactoring Code.html |
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| 03. Reverting A Commit.html |
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| 03. SageMaker Instance Utilization Limits.html |
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| 03. Testing and Data Science.html |
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| 03. Text Processing.html |
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| 03. Text Processing-pqheVyctkNQ.mp4 |
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| 03. The Web.html |
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| 03. The World Wide Web-Rxn-zCyg_iA.en.vtt |
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| 03. The World Wide Web-Rxn-zCyg_iA.zh-CN.vtt |
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| 03. Training _ Memory.html |
8.61KB |
| 03. Troubleshooting Possible Errors.html |
6.79KB |
| 03. Upload Data to S3.html |
6.77KB |
| 03. Why Use an Elevator Pitch.html |
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| 03. Workspace.html |
5.87KB |
| 04. 01 Writing Clean Code V1-wNaiahWCwkQ.en.vtt |
6.71KB |
| 04. 01 Writing Clean Code V1-wNaiahWCwkQ.mp4 |
15.42MB |
| 04. 01 Writing Clean Code V1-wNaiahWCwkQ.pt-BR.vtt |
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| 04. 02 Data Splitting Dist Solution V1-Cjn82LqTB00.en.vtt |
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| 04. 02 Data Splitting Dist Solution V1-Cjn82LqTB00.mp4 |
12.40MB |
| 04. 02 Data Splitting Dist Solution V1-Cjn82LqTB00.zh-CN.vtt |
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| 04. 02 Processing Energy Data V2-zxnoYK4sYgk.en.vtt |
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| 04. 06 Unit Tests V1-wb9jggHEvgI.en.vtt |
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| 04. 20 Custom PyTorch Model V1-kiZ22MJWSFU.en.vtt |
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| 04. 3 Data PreProcessing V1-Xw1MWmql7no.en.vtt |
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| 04. 5 General Rule V1-YKe9iOUMmsI.en.vtt |
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| 04. Arvato Final Project-qBR6A0IQXEE.en.vtt |
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| 04. BertelsmannArvato Project Overview.html |
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| 04. Character-wise RNNs.html |
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| 04. Combining the Models.html |
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| 04. Commit Messages.html |
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| 04. Components of a Web App.html |
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| 04. Congratulations.html |
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| 04. ConNet 021 MNISTClassification V1 V2-a7bvIGZpcnk.en.vtt |
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| 04. Create Your Elevator Pitch.html |
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| 04. Data Pre-Processing.html |
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| 04. Deploying and Using a Sentiment Analysis Model.html |
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| 04. Determine A Repo_s Status.html |
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| 04. Feature Extraction-Bd6TJB8eVLQ.mp4 |
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970B |
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| 04. L1 031 Unsupervised Vs Supervised Learning V1 RENDER V2-9M6T9Bx3oNA.en.vtt |
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| 04. L1 04 Meet Juno V1 V2-c4r2nGMogfM.en.vtt |
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| 04. L4 04 Longest Common Subsequence V1 V1-yxXXwBKeYvU.en.vtt |
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| 04. L4 Components Of A Web App V4-2aJf5sO2ox4.en.vtt |
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| 04. Longest Common Subsequence.html |
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| 04. Machine Learning Workflow.html |
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| 04. MacLinux Setup.html |
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| 04. Meet Juno.html |
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| 04. Mini-Project Tuning the Sentiment Analysis Model.html |
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| 04. MNIST Dataset.html |
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| 04. More Resources.html |
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| 04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 23 Configure Terminal-h00n9QLfbqU.ar.vtt |
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| 04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 33 Git Log Vs Git Log --Stat Walkthru-aOICKP_9xiY.ar.vtt |
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| 04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 33 Git Log Vs Git Log --Stat Walkthru-aOICKP_9xiY.pt-BR.vtt |
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| 04. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 33 Git Log Vs Git Log --Stat Walkthru-aOICKP_9xiY.zh-CN.vtt |
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| 04. Notebook Linear Autoencoder.html |
7.57KB |
| 04. Object Oriented Programming Syntax-Y8ZVw1LHI8E.en.vtt |
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| 04. Object Oriented Programming Syntax-Y8ZVw1LHI8E.mp4 |
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| 04. OOP Syntax.html |
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| 04. Pitching to a Recruiter-LxAdWaA-qTQ.ar.vtt |
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| 04. Pitching to a Recruiter-LxAdWaA-qTQ.en.vtt |
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| 04. Processing Energy Data.html |
6.84KB |
| 04. Resetting Commits.html |
23.13KB |
| 04. SageMaker Instance Utilization Limits.html |
16.52KB |
| 04. Skills that Set You Apart.html |
7.27KB |
| 04. Solution Data Distribution _ Splitting.html |
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| 04. Unit Tests.html |
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| 04. Unsupervised v Supervised Learning.html |
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| 04. VGG Classifier-fOiQFXItYe4.en.vtt |
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| 04. VGG Classifier-fOiQFXItYe4.mp4 |
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| 04. VGG Classifier-fOiQFXItYe4.pt-BR.vtt |
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| 04. VGG Classifier-fOiQFXItYe4.zh-CN.vtt |
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| 04. VGG Model _ Classifier.html |
6.19KB |
| 04. Viewing Modified Files.html |
14.02KB |
| 04. Writing Clean Code.html |
10.75KB |
| 05. 03 Creating Time Series V2-KMzVAmoa66k.en.vtt |
5.29KB |
| 05. 03 Creating Time Series V2-KMzVAmoa66k.mp4 |
10.52MB |
| 05. 03 LinearLearner V1-pjs5pP9OOMc.en.vtt |
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| 05. 03 LinearLearner V1-pjs5pP9OOMc.mp4 |
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| 05. 03 LinearLearner V1-pjs5pP9OOMc.zh-CN.vtt |
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| 05. 07 Unit Testing Tools V1-8bKhOyFbX_Y.en.vtt |
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| 05. 07 Unit Testing Tools V1-8bKhOyFbX_Y.mp4 |
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| 05. 07 Unit Testing Tools V1-8bKhOyFbX_Y.pt-BR.vtt |
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| 05. 07 Unit Testing Tools V1-8bKhOyFbX_Y.zh-CN.vtt |
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| 05. 22 Simple NN V1-FINTJpz1Yx0.en.vtt |
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| 05. 22 Simple NN V1-FINTJpz1Yx0.mp4 |
4.98MB |
| 05. 22 Simple NN V1-FINTJpz1Yx0.zh-CN.vtt |
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| 05. 3 Defining Training Autoenc V1-OWrlQUSGqyo.en.vtt |
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| 05. 3 Defining Training Autoenc V1-OWrlQUSGqyo.mp4 |
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| 05. 3 Defining Training Autoenc V1-OWrlQUSGqyo.pt-BR.vtt |
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| 05. 4 EncodingWords Sol V1-4RYyn3zv1Hg.en.vtt |
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| 05. 4 EncodingWords Sol V1-4RYyn3zv1Hg.mp4 |
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| 05. 4 EncodingWords Sol V1-4RYyn3zv1Hg.zh-CN.vtt |
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| 05. 6 Normal Distribution V1-xm43q4qD2tI.en.vtt |
3.54KB |
| 05. 6 Normal Distribution V1-xm43q4qD2tI.mp4 |
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| 05. 6 Normal Distribution V1-xm43q4qD2tI.pt-BR.vtt |
3.50KB |
| 05. 6 Normal Distribution V1-xm43q4qD2tI.zh-CN.vtt |
2.90KB |
| 05. APIs [advanced version].html |
10.39KB |
| 05. Arvato Terms and Conditions.html |
8.84KB |
| 05. Bag of Words.html |
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| 05. Bag Of Words-A7M1z8yLl0w.en.vtt |
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| 05. Bag Of Words-A7M1z8yLl0w.en.vtt |
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| 05. Bag Of Words-A7M1z8yLl0w.mp4 |
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| 05. Bag Of Words-A7M1z8yLl0w.mp4 |
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| 05. Bag Of Words-A7M1z8yLl0w.pt-BR.vtt |
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| 05. Bag Of Words-A7M1z8yLl0w.zh-CN.vtt |
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| 05. ConNet 022 How Computers Interpret Images V1-mEPfoM68Fx4.en.vtt |
3.84KB |
| 05. ConNet 022 How Computers Interpret Images V1-mEPfoM68Fx4.mp4 |
9.15MB |
| 05. ConNet 022 How Computers Interpret Images V1-mEPfoM68Fx4.pt-BR.vtt |
4.07KB |
| 05. ConNet 022 How Computers Interpret Images V1-mEPfoM68Fx4.zh-CN.vtt |
3.27KB |
| 05. Create A Repo - Outro-h7j4STDFCjs.ar.vtt |
959B |
| 05. Create A Repo - Outro-h7j4STDFCjs.en.vtt |
720B |
| 05. Create A Repo - Outro-h7j4STDFCjs.mp4 |
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| 05. Create A Repo - Outro-h7j4STDFCjs.pt-BR.vtt |
800B |
| 05. Create A Repo - Outro-h7j4STDFCjs.zh-CN.vtt |
664B |
| 05. Defining _ Training an Autoencoder.html |
7.10KB |
| 05. Deployment L2 C2 V2-TRUCNy5Eqjc.en.vtt |
3.60KB |
| 05. Deployment L2 C2 V2-TRUCNy5Eqjc.mp4 |
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| 05. Deployment L2 C2 V2-TRUCNy5Eqjc.zh-CN.vtt |
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| 05. Deployment L4 C4 V1-Q2Vthdca49I.en.vtt |
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| 05. Deployment L4 C4 V1-Q2Vthdca49I.mp4 |
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| 05. Deployment L4 C4 V1-Q2Vthdca49I.zh-CN.vtt |
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| 05. Deployment L5 C4 V1-v7dYwxuKXzI.en.vtt |
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| 05. Deployment L5 C4 V1-v7dYwxuKXzI.mp4 |
1.26MB |
| 05. Deployment L5 C4 V1-v7dYwxuKXzI.zh-CN.vtt |
915B |
| 05. Dynamic Programming.html |
6.26KB |
| 05. Encoding Words, Solution.html |
6.74KB |
| 05. Exercise Creating Time Series.html |
6.85KB |
| 05. Exercise OOP Syntax Practice - Part 1.html |
9.09KB |
| 05. Git Diff.html |
8.70KB |
| 05. How Computers Interpret Images.html |
12.68KB |
| 05. Interview with Art - Part 1.html |
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| 05. Interview with Art - Part 1-ClLYamtaO-Q.ar.vtt |
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| 05. Interview with Art - Part 1-ClLYamtaO-Q.en.vtt |
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| 05. Interview with Art - Part 1-ClLYamtaO-Q.ja-JP.vtt |
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| 05. Interview with Art - Part 1-ClLYamtaO-Q.mp4 |
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| 05. Interview with Art - Part 1-ClLYamtaO-Q.pt-BR.vtt |
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| 05. Interview with Art - Part 1-ClLYamtaO-Q.zh-CN.vtt |
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| 05. Knowledge.html |
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| 05. L1 032 Model Design V1 RENDER V2-zxNoSTZ3s90.en.vtt |
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| 05. L1 032 Model Design V1 RENDER V2-zxNoSTZ3s90.mp4 |
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| 05. L1 032 Model Design V1 RENDER V2-zxNoSTZ3s90.zh-CN.vtt |
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| 05. L4 05 Dynamic Programming V1 V1-vAwu-sW9GJE.en.vtt |
6.36KB |
| 05. L4 05 Dynamic Programming V1 V1-vAwu-sW9GJE.mp4 |
7.14MB |
| 05. L4 05 Dynamic Programming V1 V1-vAwu-sW9GJE.zh-CN.vtt |
5.55KB |
| 05. Launch an Instance.html |
13.40KB |
| 05. Lesson Outro.html |
5.37KB |
| 05. LinearLearner _ Class Imbalance.html |
7.68KB |
| 05. Machine Learning Workflow.html |
9.19KB |
| 05. Merging.html |
17.11KB |
| 05. Mini-Project Solution - Tuning the Model.html |
6.71KB |
| 05. Mini-Project Updating a Sentiment Analysis Model.html |
7.48KB |
| 05. Model Design.html |
8.65KB |
| 05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 30 Configure Terminal-CCYjHfBk9hw.ar.vtt |
3.28KB |
| 05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 30 Configure Terminal-CCYjHfBk9hw.en.vtt |
2.57KB |
| 05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 30 Configure Terminal-CCYjHfBk9hw.mp4 |
3.77MB |
| 05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 30 Configure Terminal-CCYjHfBk9hw.pt-BR.vtt |
2.76KB |
| 05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L1 30 Configure Terminal-CCYjHfBk9hw.zh-CN.vtt |
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| 05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 42 Git Log -P Output Walkthru-A8Kwocr-K8c.ar.vtt |
6.92KB |
| 05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 42 Git Log -P Output Walkthru-A8Kwocr-K8c.en.vtt |
5.22KB |
| 05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 42 Git Log -P Output Walkthru-A8Kwocr-K8c.mp4 |
6.19MB |
| 05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 42 Git Log -P Output Walkthru-A8Kwocr-K8c.pt-BR.vtt |
5.56KB |
| 05. Nd016 WebND Ud123 Gitcourse BETAMOJITO L3 42 Git Log -P Output Walkthru-A8Kwocr-K8c.zh-CN.vtt |
4.72KB |
| 05. Normal Distribution.html |
6.40KB |
| 05. Outro.html |
5.76KB |
| 05. Pre-Notebook Transfer Learning.html |
8.15KB |
| 05. Quiz Clean Code.html |
11.89KB |
| 05. Sequence Batching.html |
6.59KB |
| 05. Sequence-Batching-Z4OiyU0Cldg.en.vtt |
2.09KB |
| 05. Sequence-Batching-Z4OiyU0Cldg.mp4 |
2.29MB |
| 05. Sequence-Batching-Z4OiyU0Cldg.pt-BR.vtt |
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| 05. Sequence-Batching-Z4OiyU0Cldg.zh-CN.vtt |
1.92KB |
| 05. Setting up a Notebook Instance.html |
11.90KB |
| 05. Solution Simple Neural Network.html |
6.78KB |
| 05. Text Processing, Bag of Words.html |
9.65KB |
| 05. The Front-End.html |
8.47KB |
| 05. The Front End-CspuxLGFM4U.en.vtt |
1.88KB |
| 05. The Front End-CspuxLGFM4U.mp4 |
8.64MB |
| 05. The Front End-CspuxLGFM4U.pt-BR.vtt |
1.96KB |
| 05. The Front End-CspuxLGFM4U.zh-CN.vtt |
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| 05. Unit Testing Tools.html |
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| 05. Use Your Elevator Pitch on LinkedIn.html |
9.77KB |
| 05. Viewing File Changes.html |
17.36KB |
| 05. Windows Setup.html |
10.90KB |
| 06. 02 Writing Modular Code V2-qN6EOyNlSnk.en.vtt |
7.63KB |
| 06. 02 Writing Modular Code V2-qN6EOyNlSnk.mp4 |
7.71MB |
| 06. 02 Writing Modular Code V2-qN6EOyNlSnk.pt-BR.vtt |
8.52KB |
| 06. 02 Writing Modular Code V2-qN6EOyNlSnk.zh-CN.vtt |
6.75KB |
| 06. 23 Train Script V2-1cbvRmKvQIg.en.vtt |
8.56KB |
| 06. 23 Train Script V2-1cbvRmKvQIg.mp4 |
19.45MB |
| 06. 23 Train Script V2-1cbvRmKvQIg.zh-CN.vtt |
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| 06. 44 Accessing The API Through Web Address SC 44 V2-nygWkgUQNfo.en.vtt |
7.71KB |
| 06. 44 Accessing The API Through Web Address SC 44 V2-nygWkgUQNfo.mp4 |
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| 06. 44 Accessing The API Through Web Address SC 44 V2-nygWkgUQNfo.pt-BR.vtt |
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| 06. 44 Accessing The API Through Web Address SC 44 V2-nygWkgUQNfo.zh-CN.vtt |
6.93KB |
| 06. 4 A Simple Solution V2-Jh3mbomqpw8.en.vtt |
2.52KB |
| 06. 4 A Simple Solution V2-Jh3mbomqpw8.mp4 |
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| 06. 4 A Simple Solution V2-Jh3mbomqpw8.pt-BR.vtt |
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| 06. 5 GettingRid ZeroLength V1-Hs6ithuvDJg.en.vtt |
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| 06. 5 GettingRid ZeroLength V1-Hs6ithuvDJg.mp4 |
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| 06. 5 GettingRid ZeroLength V1-Hs6ithuvDJg.zh-CN.vtt |
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| 06. 6 Screencast HTML Code V2-G7fBus1JSc0.en.vtt |
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| 06. 6 Screencast HTML Code V2-G7fBus1JSc0.mp4 |
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| 06. 6 Screencast HTML Code V2-G7fBus1JSc0.pt-BR.vtt |
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| 06. 6 Screencast HTML Code V2-G7fBus1JSc0.zh-CN.vtt |
7.22KB |
| 06. A Couple of Notes about OOP.html |
15.48KB |
| 06. A Simple Solution.html |
7.02KB |
| 06. BertelsmannArvato Project Workspace.html |
7.05KB |
| 06. Building and Deploying the Model.html |
7.91KB |
| 06. Cloning the Deployment Notebooks.html |
9.58KB |
| 06. ConNet 03 MLPStructure_ClassScore V1 V1-fP0Odiai8sk.en.vtt |
3.11KB |
| 06. ConNet 03 MLPStructure_ClassScore V1 V1-fP0Odiai8sk.mp4 |
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| 06. ConNet 03 MLPStructure_ClassScore V1 V1-fP0Odiai8sk.pt-BR.vtt |
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| 06. ConNet 03 MLPStructure_ClassScore V1 V1-fP0Odiai8sk.zh-CN.vtt |
2.59KB |
| 06. Course Outro.html |
5.85KB |
| 06. Create Your Profile With SEO In Mind.html |
9.47KB |
| 06. Deployment L2 C3 V2-jqL74whe9yo.en.vtt |
1.81KB |
| 06. Deployment L2 C3 V2-jqL74whe9yo.mp4 |
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| 06. Deployment L2 C3 V2-jqL74whe9yo.zh-CN.vtt |
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| 06. Deployment L3 C4b V1-JCiQhhXbeuc.en.vtt |
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| 06. Deployment L3 C4b V1-JCiQhhXbeuc.mp4 |
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| 06. Deployment L3 C4b V1-JCiQhhXbeuc.zh-CN.vtt |
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| 06. Deployment L4 C5 V2-i-EjGkZ8z30.en.vtt |
4.58KB |
| 06. Deployment L4 C5 V2-i-EjGkZ8z30.mp4 |
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| 06. Deployment L4 C5 V2-i-EjGkZ8z30.zh-CN.vtt |
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| 06. Deployment L5 C5 V1-75RxW3R6674.en.vtt |
5.25KB |
| 06. Deployment L5 C5 V1-75RxW3R6674.mp4 |
8.06MB |
| 06. Deployment L5 C5 V1-75RxW3R6674.zh-CN.vtt |
4.42KB |
| 06. Exercise Define a LinearLearner.html |
8.89KB |
| 06. Exercise Training Script.html |
6.78KB |
| 06. Getting Rid of Zero-Length.html |
6.76KB |
| 06. Having Git Ignore Files.html |
14.02KB |
| 06. HTML.html |
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| 06. Identify fixes for example “bad” profile.html |
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457B |
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| 06. Identify fixes for example “bad” profile-ncFtwW5urHk.zh-CN.vtt |
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| 06. L1C05 HSV2 Population Segmentation With KMeans V1-3pXFLrnk7q0.en.vtt |
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| 06. L1C05 HSV2 Population Segmentation With KMeans V1-3pXFLrnk7q0.mp4 |
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| 07. Test Driven Development and Data Science.html |
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| 08. Boston Housing In-Depth - Monitoring the Tuning Job.html |
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| 08. Building a New Model.html |
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| 08. Commenting Object-Oriented Code.html |
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| 08. ConNet 05 Loss_Optimization V1 V3-BmPDtSXv18w.en.vtt |
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| 09. Writing READMEs with Walter-DQEfT2Zq5_o.ar.vtt |
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| 09. Writing READMEs with Walter-DQEfT2Zq5_o.en.vtt |
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| 09. Writing READMEs with Walter-DQEfT2Zq5_o.ja-JP.vtt |
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| 09. Writing READMEs with Walter-DQEfT2Zq5_o.mp4 |
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| 09. Writing READMEs with Walter-DQEfT2Zq5_o.pt-BR.vtt |
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| 09. Writing READMEs with Walter-DQEfT2Zq5_o.zh-CN.vtt |
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| 1. (FreeCoursesOnline.Me) Download Udacity, Masterclass, Lynda, PHLearn, Pluralsight Free.url |
286B |
| 10. 03 Optimizing Common Books V1-WF9n_19V08g.en.vtt |
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| 10. 03 Optimizing Common Books V1-WF9n_19V08g.mp4 |
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| 10. 03 Optimizing Common Books V1-WF9n_19V08g.pt-BR.vtt |
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| 10. 03 Optimizing Common Books V1-WF9n_19V08g.zh-CN.vtt |
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| 10. 06 Defining Model V2-_LWzyqq4hCY.en.vtt |
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| 10. 06 Defining Model V2-_LWzyqq4hCY.mp4 |
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| 10. 06 Defining Model V2-_LWzyqq4hCY.pt-BR.vtt |
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| 10. 07 Training The Network V1-904bfqibcCw.en.vtt |
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| 10. 07 Training The Network V1-904bfqibcCw.mp4 |
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| 10. 07 Training The Network V1-904bfqibcCw.pt-BR.vtt |
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| 10. 08 Complete Estimator Hyperparams V2-ah7muNBc3dI.en.vtt |
2.77KB |
| 10. 08 Complete Estimator Hyperparams V2-ah7muNBc3dI.mp4 |
5.99MB |
| 10. 9 DefiningModel V1-SpvIZl1YQRI.en.vtt |
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| 10. 9 DefiningModel V1-SpvIZl1YQRI.mp4 |
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| 10. 9 DefiningModel V1-SpvIZl1YQRI.zh-CN.vtt |
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| 10. Boston Housing Example - Testing the Model.html |
8.12KB |
| 10. Building an API.html |
7.78KB |
| 10. Cleaning Up Your AWS Account.html |
8.67KB |
| 10. Defining the Model.html |
7.08KB |
| 10. Defining the Model.html |
6.70KB |
| 10. Deployment L2 C6 V1-CZRKuS_qYtg.en.vtt |
6.21KB |
| 10. Deployment L2 C6 V1-CZRKuS_qYtg.mp4 |
10.05MB |
| 10. Deployment L2 C6 V1-CZRKuS_qYtg.zh-CN.vtt |
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| 10. Deployment L3 C7 V1-AzBQ-aDQSG4.en.vtt |
5.16KB |
| 10. Deployment L3 C7 V1-AzBQ-aDQSG4.mp4 |
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| 10. Deployment L3 C7 V1-AzBQ-aDQSG4.zh-CN.vtt |
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| 10. Deployment L5 C9 V1-8z24cb3EfMc.en.vtt |
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| 10. Deployment L5 C9 V1-8z24cb3EfMc.mp4 |
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| 10. Deployment L5 C9 V1-8z24cb3EfMc.zh-CN.vtt |
3.04KB |
| 10. Dog Project Workspace.html |
7.00KB |
| 10. Exercise Create a PyTorchModel _ Endpoint.html |
9.50KB |
| 10. Exercise HTML Div, Span, IDs, Classes.html |
8.84KB |
| 10. GloVe.html |
6.31KB |
| 10. GloVe-KK3PMIiIn8o.en.vtt |
4.21KB |
| 10. GloVe-KK3PMIiIn8o.mp4 |
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| 10. GloVe-KK3PMIiIn8o.pt-BR.vtt |
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| 10. GloVe-KK3PMIiIn8o.zh-CN.vtt |
3.60KB |
| 10. How the Gaussian Class Works.html |
8.88KB |
| 10. How The Gaussian Class Works-N-5I0d1zJHI.en.vtt |
5.25KB |
| 10. How The Gaussian Class Works-N-5I0d1zJHI.mp4 |
8.09MB |
| 10. How The Gaussian Class Works-N-5I0d1zJHI.pt-BR.vtt |
4.83KB |
| 10. How The Gaussian Class Works-N-5I0d1zJHI.zh-CN.vtt |
4.55KB |
| 10. Interview with Art - Part 2.html |
7.93KB |
| 10. Interview with Art - Part 2-Vvzl2J5K7-Y.ar.vtt |
2.82KB |
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| 10. Interview with Art - Part 2-Vvzl2J5K7-Y.zh-CN.vtt |
2.07KB |
| 10. Logging.html |
7.86KB |
| 10. Optimizing - Common Books.html |
8.32KB |
| 10. Paths to Deployment.html |
15.35KB |
| 10. Precision _ Recall, Overview.html |
8.60KB |
| 10. Pre-Notebook Convolutional Autoencoder.html |
9.25KB |
| 10. Pre-Notebook Population Segmentation.html |
10.57KB |
| 10. Reaching Out on LinkedIn.html |
9.36KB |
| 10. Solution Complete Estimator _ Hyperparameters.html |
6.93KB |
| 10. Summary.html |
7.59KB |
| 10. Training the Network.html |
12.43KB |
| 11. 07 CharRNN Solution V1-ed33qePHrJM.en.vtt |
11.40KB |
| 11. 07 CharRNN Solution V1-ed33qePHrJM.mp4 |
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| 11. 11 Making Predictions V2-BKOYIfgjsq8.en.vtt |
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| 11. 11 Making Predictions V2-BKOYIfgjsq8.mp4 |
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| 11. 28 PyTorch Deployment Evaluation V2-qZTyQqo9FWM.en.vtt |
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| 11. 28 PyTorch Deployment Evaluation V2-qZTyQqo9FWM.mp4 |
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| 11. Boost Your Visibility.html |
8.58KB |
| 11. Char-RNN, Solution.html |
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| 11. Code Review.html |
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| 11. Commit messages best practices.html |
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| 11. Complete Sentiment RNN.html |
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| 11. CSS.html |
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15.91MB |
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| 11. Deployment L2 C7 V1-ouLvRqMMbbY.en.vtt |
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| 11. Deployment L3 C8 V1-VgG41Q_a15I.en.vtt |
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| 11. Deployment L5 C10 V1-ilnX9rUlV_w.en.vtt |
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| 11. Embeddings for Deep Learning.html |
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| 11. Embeddings For Deep Learning-gj8u1KG0H2w.en.vtt |
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| 11. Embeddings For Deep Learning-gj8u1KG0H2w.mp4 |
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| 11. Embeddings For Deep Learning-gj8u1KG0H2w.pt-BR.vtt |
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| 11. Exercise Code the Gaussian Class.html |
9.07KB |
| 11. Exercise Data Loading _ Processing.html |
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| 11. Exercise Deploy Estimator.html |
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| 11. L1C4 DataLoading Processing 2 V2-YlG9T17KcbU.en.vtt |
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| 11. L1C4 DataLoading Processing 2 V2-YlG9T17KcbU.mp4 |
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| 11. L2 2 14 Code Review V1 V2-zAy1ffMFA-k.en.vtt |
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| 11. L2 2 14 Code Review V1 V2-zAy1ffMFA-k.zh-CN.vtt |
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| 11. Making Predictions.html |
6.82KB |
| 11. Mini-Project Building Your First Model.html |
8.92KB |
| 11. Notebook Convolutional Autoencoder.html |
7.58KB |
| 11. Paths to Deployment.html |
9.40KB |
| 11. Pre-Notebook MLP Classification, Exercise.html |
11.89KB |
| 11. Quiz Optimizing - Common Books.html |
8.36KB |
| 11. SageMaker Tips and Tricks.html |
7.43KB |
| 11. Selecting One Project.html |
7.50KB |
| 11. Solution PyTorchModel _ Evaluation.html |
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| 11. Using the Final Web Application.html |
8.29KB |
| 12. 08 Making Predictions V3-BhrpV3kwATo.en.vtt |
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| 12. 08 Making Predictions V3-BhrpV3kwATo.mp4 |
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| 12. 08 Making Predictions V3-BhrpV3kwATo.zh-CN.vtt |
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| 12. 092 Deployment Evaluation V1-ZknaWInjSa4.en.vtt |
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| 12. 092 Deployment Evaluation V1-ZknaWInjSa4.mp4 |
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| 12. 092 Deployment Evaluation V1-ZknaWInjSa4.zh-CN.vtt |
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| 12. 8 Conv Solution V1-2_Yw9LLomCo.en.vtt |
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| 12. 8 Conv Solution V1-2_Yw9LLomCo.mp4 |
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| 12. 8 Conv Solution V1-2_Yw9LLomCo.pt-BR.vtt |
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| 12. Clean Up All Resources.html |
10.95KB |
| 12. Convolutional Solution.html |
7.50KB |
| 12. Deployment L2 C8 V1-utUxiW-tZrY.en.vtt |
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| 12. Deployment L2 C8 V1-utUxiW-tZrY.mp4 |
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| 12. Deployment L2 C8 V1-utUxiW-tZrY.zh-CN.vtt |
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| 12. Exercise CSS.html |
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| 12. Exercise Predicting the Future.html |
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| 12. L1C5 Data PreProcessing Solution-2jUouM70A1I.en.vtt |
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| 12. L1C5 Data PreProcessing Solution-2jUouM70A1I.mp4 |
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| 12. L3 10 Magic M V1 V3-9dEsv1aNUEE.en.vtt |
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| 12. Magic Methods.html |
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| 12. Magic Methods in Code-oDuXThOqans.en.vtt |
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| 12. Magic Methods in Code-oDuXThOqans.pt-BR.vtt |
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| 12. Making Predictions.html |
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| 12. Mini-Project Solution.html |
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| 12. Modeling.html |
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| 12. Notebook MLP Classification, MNIST.html |
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| 12. Production Environment-BH23Me3bbF4.en.vtt |
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| 12. Production Environment-BH23Me3bbF4.mp4 |
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| 12. Production Environments.html |
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| 12. Questions to Ask Yourself When Conducting a Code Review.html |
8.34KB |
| 12. Reflect on your commit messages-_0AHmKkfjTo.ar.vtt |
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| 12. Reflect on your commit messages-_0AHmKkfjTo.en.vtt |
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| 12. Reflect on your commit messages.html |
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| 12. Solution Data Pre-Processing.html |
8.69KB |
| 12. Solution Deployment _ Evaluation.html |
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| 12. Solution Optimizing - Common Books.html |
8.37KB |
| 12. Summary.html |
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| 12. Training the Model.html |
13.30KB |
| 12. Up Next.html |
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| 13. 09 One Solution V2-7q37WPjQhDA.en.vtt |
7.93KB |
| 13. 09 One Solution V2-7q37WPjQhDA.mp4 |
11.59MB |
| 13. 09 One Solution V2-7q37WPjQhDA.pt-BR.vtt |
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| 13. 10 Model Improvements V1-JjZMuUnxKw4.en.vtt |
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| 13. 10 Model Improvements V1-JjZMuUnxKw4.mp4 |
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| 13. 13 Predicting Future Data V2-HT5xKDOgHYw.en.vtt |
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| 13. 13 Predicting Future Data V2-HT5xKDOgHYw.mp4 |
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| 13. 9 Upsampling Denoising V2-XX63da4EPN0.en.vtt |
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| 13. Bootstrap Library.html |
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| 13. Bootstrap Library-KsrqjguHWUI.mp4 |
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| 13. Bootstrap Library-KsrqjguHWUI.pt-BR.vtt |
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| 13. Boston Housing In-Depth - Data Preparation.html |
9.14KB |
| 13. Deployment L2 C9b V2-TA-Ms7djeL0.en.vtt |
4.90KB |
| 13. Deployment L2 C9b V2-TA-Ms7djeL0.mp4 |
7.57MB |
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| 13. Exercise Code Magic Methods.html |
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| 13. Exercise Normalization.html |
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| 13. Model Improvements.html |
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| 13. One Solution.html |
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| 13. Production Environments.html |
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| 13. Quiz Optimizing - Holiday Gifts.html |
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| 13. Solution Predicting Future Data.html |
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| 13. Summary of Skills.html |
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| 13. Testing.html |
10.57KB |
| 13. Tips for Conducting a Code Review.html |
11.01KB |
| 13. Upsampling _ Denoising.html |
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| 14. 10 Denoising V1-RIfEhKev24I.en.vtt |
3.96KB |
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6.00MB |
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| 14. 11 Model Tuning V1-bb7zG0TdtRM.en.vtt |
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| 14. 13 Inheritance Example V1-uWT-HIHBjv0.en.vtt |
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| 14. Boston Housing In-Depth - Creating a Training Job.html |
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| 14. Clean Up All Resources.html |
11.01KB |
| 14. Conclusion.html |
6.74KB |
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| 14. De-noising.html |
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| 14. Endpoints _ REST APIs.html |
16.98KB |
| 14. Exercise Bootstrap.html |
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| 14. Improvement, Model Tuning.html |
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| 14. Inference, Solution.html |
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| 14. Interview with Art - Part 3.html |
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| 14. L1C7 Normalization Solution V3-UDWwdG4e1a0.en.vtt |
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3.40MB |
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| 14. L2 2 16 Conclusion V1 V1-fDpQBbqd_kg.en.vtt |
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547B |
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10.61KB |
| 14. Solution Normalization.html |
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| 14. Solution Optimizing - Holiday Gifts.html |
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| 15. Boston Housing In-Depth - Building a Model.html |
7.56KB |
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| 15. JavaScript.html |
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| 15. L2 10 Documentation V1 V3-M45B2VbPgjo.en.vtt |
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| 15. L2 10 Documentation V1 V3-M45B2VbPgjo.mp4 |
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| 15. Participating in open source projects 2.html |
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| 15. PCA, Overview.html |
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| 15. PCA Toy Problem SC V1-uyl44T12yU8.en.vtt |
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| 15. PCA Toy Problem SC V1-uyl44T12yU8.mp4 |
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| 15. Pre-Notebook De-noising Autoencoder.html |
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| 15. Validation Loss.html |
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| 16. 04 Inline Comments V1--G6yg3Xhl8I.en.vtt |
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| 16. Boston Housing In-Depth - Creating a Batch Transform Job.html |
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| 16. ConNet 13 ImageClassification V1 V2-UHFBnitKraA.en.vtt |
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| 16. ConNet 13 ImageClassification V1 V2-UHFBnitKraA.mp4 |
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| 16. Deployment L2 C12 V1-JwPJMYRl3nw.en.vtt |
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| 16. Exercise JavaScript.html |
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| 16. Image Classification Steps.html |
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| 16. Notebook De-noising Autoencoder.html |
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| 16. PCA Estimator _ Training.html |
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| 16. Solution Accounting for Class Imbalance.html |
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| 17. 05 Docstrings V1-_gapemxsRJY.en.vtt |
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| 17. 18 Screencast Plotly V2-QsmOW1jNeio.en.vtt |
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| 17. ConNet 14 MLPvsCNN V1 V2-Q7CR3cCOtJQ.en.vtt |
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| 17. Containers.html |
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| 17. Demo Inheritance Probability Distributions.html |
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| 17. Docstrings.html |
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| 17. Exercise Define a Model w Specifications.html |
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| 17. Exercise PCA Model Attributes _ Variance.html |
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| 17. L1C9 PCA Attributes Variance V3-dumVafbS7pk.en.vtt |
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| 17. MLPs vs CNNs.html |
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| 17. Summary.html |
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| 18. Advanced OOP Topics.html |
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| 18. Containers - Straight From the Experts.html |
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| 18. Exercise Plotly.html |
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| 18. Jesse Swidler Interview on Containers-XimuK3WHOH4.en.vtt |
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| 18. Jesse Swidler Interview on Containers-XimuK3WHOH4.mp4 |
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| 18. Jesse Swidler Interview on Containers-XimuK3WHOH4.zh-CN.vtt |
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| 18. L1C10 Variance Solution V3-C-BRBjxlUuE.en.vtt |
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| 18. Local Connectivity.html |
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| 18. One Solution Tuned and Balanced LinearLearner.html |
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| 18. Project Documentation.html |
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| 18. Solution Variance.html |
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| 19. 15 Filters And Convo RENDER V2-x_dhnhUzFNo.en.vtt |
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| 19. Characteristics of Modeling _ Deployment.html |
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| 19. Component Makeup.html |
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| 19. Documentation.html |
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| 19. Filters and the Convolutional Layer.html |
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| 19. L1C11 Component Makeup V2-fiSr_Xjm3qI.en.vtt |
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| 19. L1C11 Component Makeup V2-fiSr_Xjm3qI.mp4 |
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| 19. L1C11 Component Makeup V2-fiSr_Xjm3qI.zh-CN.vtt |
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| 19. L2 03 Summary _ Improvements V2-VsjDz3agnhQ.en.vtt |
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| 19. L2 03 Summary _ Improvements V2-VsjDz3agnhQ.mp4 |
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| 19. L4 The Back End V2-Esl0NL63S2c.en.vtt |
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| 19. Organizing Code Into Modules-AARS10U5bbo.en.vtt |
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| 19. Organizing Code Into Modules-AARS10U5bbo.pt-BR.vtt |
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| 19. Organizing into Modules.html |
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| 19. Summary and Improvements.html |
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| 19. The Backend.html |
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| 20. 22 Screencast Flask V2-i_U3O-7cymk.en.vtt |
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| 20. Characteristics of Modeling _ Deployment.html |
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| 20. ConNet 16 FIlters _ Edges V2-hfqNqcEU6uI.en.vtt |
1.61KB |
| 20. ConNet 16 FIlters _ Edges V2-hfqNqcEU6uI.mp4 |
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| 20. Demo Modularized Code.html |
9.04KB |
| 20. Exercise PCA Deployment _ Data Transformation.html |
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| 20. Filters _ Edges.html |
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| 20. Flask.html |
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| 20. L1C12 PCA Deployment V1-qsnpHHuwbbA.en.vtt |
4.89KB |
| 20. L1C12 PCA Deployment V1-qsnpHHuwbbA.mp4 |
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| 20. L2 17 Version Control In Data Science V1 V1-EQzrLC88Bzk.en.vtt |
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| 20. Version Control in Data Science.html |
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| 21. 15 Making a Package v2-Hj2OBr1CGZM.en.vtt |
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| 21. 15 Making a Package v2-Hj2OBr1CGZM.mp4 |
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| 21. Comparing Cloud Providers.html |
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| 21. Exercise Flask.html |
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| 21. Frequency in Images.html |
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| 21. L1C13 Creating New Data Solution V4-4l2UHyyVV7Y.en.vtt |
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| 21. L1C13 Creating New Data Solution V4-4l2UHyyVV7Y.mp4 |
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| 21. L2 18 Version Control Git Branches V1 V2-C92YcuwjZOs.en.vtt |
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| 21. Making a Package.html |
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| 21. Scenario #1.html |
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| 21. Solution Creating Transformed Data.html |
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| 22. Comparing Cloud Providers.html |
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| 22. Exercise K-means Estimator _ Selecting K.html |
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| 22. Flask + Pandas.html |
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| 22. Flask and Pandas-L_M_8UVY42k.en.vtt |
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| 22. Flask and Pandas-L_M_8UVY42k.mp4 |
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| 22. High-pass Filters.html |
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| 22. High-pass Filters-OpcFn_H2V-Q.mp4 |
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| 22. L2 18 Version Control Git Commit Messages V1 V2-w1iHWpwOkMg.en.vtt |
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| 22. Scenario #2.html |
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| 22. Virtual Environments-f7rzxUiHOJ0.mp4 |
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| 23. Closing Remarks On Deployment-fXl_MCYzcOU.en.vtt |
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| 23. Closing Statements.html |
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| 23. Example Flask + Pandas.html |
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| 23. Exercise K-means Predictions (clusters).html |
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| 23. Exercise Making a Package and Pip Installing.html |
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| 23. L2 18 Version Control Merging Branches On A Team V1 V2-36DOnNzvT4A.en.vtt |
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| 23. Quiz Kernels.html |
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| 24. Flask+Plotly+Pandas Part 1.html |
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| 24. Flask Pandas Plotly Part 1-xg7P8MnItdI.en.vtt |
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| 24. Flask Pandas Plotly Part 1-xg7P8MnItdI.mp4 |
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| 24. Flask Pandas Plotly Part 1-xg7P8MnItdI.pt-BR.vtt |
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| 24. L1C15 Kmeans Solutioni V2-0xx2p2vnCg0.en.vtt |
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| 24. L1C15 Kmeans Solutioni V2-0xx2p2vnCg0.zh-CN.vtt |
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| 24. Model Versioning.html |
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| 24. OpenCV _ Creating Custom Filters.html |
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| 24. Solution K-means Predictor.html |
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| 24. Summary.html |
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| 25. [Optional] Cloud Computing Defined.html |
36.25KB |
| 25. 40 Screencast Flask Pandas Plotly Part2 V2-yx-DRzMsblI.en.vtt |
6.91KB |
| 25. 40 Screencast Flask Pandas Plotly Part2 V2-yx-DRzMsblI.mp4 |
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| 25. 40 Screencast Flask Pandas Plotly Part2 V2-yx-DRzMsblI.pt-BR.vtt |
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| 25. 40 Screencast Flask Pandas Plotly Part2 V2-yx-DRzMsblI.zh-CN.vtt |
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| 25. Conclusion.html |
7.91KB |
| 25. Exercise Binomial Class.html |
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| 25. Exercise Get the Model Attributes.html |
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| 25. Flask+Plotly+Pandas Part 2.html |
10.42KB |
| 25. L2 21 Conclusion V1 V1-anPnokWZOZQ.en.vtt |
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| 25. Notebook Finding Edges.html |
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| 26. [Optional] Cloud Computing Explained.html |
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| 26. Convolutional Layer.html |
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| 26. Flask+Plotly+Pandas Part 3.html |
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| 26. L1C17 Model Attributes Conclusions V2-VS-hVhsCBPw.en.vtt |
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| 26. L1C17 Model Attributes Conclusions V2-VS-hVhsCBPw.mp4 |
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| 26. L1C17 Model Attributes Conclusions V2-VS-hVhsCBPw.zh-CN.vtt |
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| 26. Scikit-learn Source Code.html |
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| 26. Solution Model Attributes.html |
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| 27. 20 Putting Code On PyPi V1-4uosDOKn5LI.en.vtt |
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| 27. 20 Putting Code On PyPi V1-4uosDOKn5LI.mp4 |
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| 27. 40 Screencast Flask Pandas Plotly Part4 V2-4IF2G9Fehb4.en.vtt |
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| 27. Camadas convolucionais-RnM1D-XI--8.en.vtt |
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| 27. Clean Up All Resources.html |
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