|
Обратите внимание, что наш сайт не размещает какие-либо файлы из списка. Вы не можете скачать
эти файлы или скачать torrent-файл.
|
| [TGx]Downloaded from torrentgalaxy.org.txt |
524б |
| 007. When do MLPs (not) work well .html |
7.39Кб |
| 008. Mini Project Training an MLP on MNIST.html |
10.34Кб |
| 01. Additional NLP Lessons.html |
5.28Кб |
| 01. AIND NLP L2 HS 04 Modeling V2-RGrGi-eKhOQ.en.vtt |
1.14Кб |
| 01. AIND NLP L2 HS 04 Modeling V2-RGrGi-eKhOQ.mp4 |
2.86Мб |
| 01. AIND NLP L2 HS 04 Modeling V2-RGrGi-eKhOQ.zh-CN.vtt |
1.03Кб |
| 01. Feature Extraction.html |
4.72Кб |
| 01. Feature Extraction-Bd6TJB8eVLQ.en.vtt |
1.10Кб |
| 01. Feature Extraction-Bd6TJB8eVLQ.mp4 |
2.26Мб |
| 01. Feature Extraction-Bd6TJB8eVLQ.zh-CN.vtt |
970б |
| 01. Intro.html |
7.64Кб |
| 01. Intro.html |
5.69Кб |
| 01. Intro.html |
6.15Кб |
| 01. Introducing Alexis.html |
7.24Кб |
| 01. Introducing Alexis-38ExGpdyvJI.en.vtt |
694б |
| 01. Introducing Alexis-38ExGpdyvJI.mp4 |
2.05Мб |
| 01. Introducing Alexis-38ExGpdyvJI.pt-BR.vtt |
599б |
| 01. Introducing Alexis-38ExGpdyvJI.zh-CN.vtt |
615б |
| 01. Introducing Jay.html |
5.29Кб |
| 01. Introducing Jay Alammar.html |
5.86Кб |
| 01. Introducing Jeremy.html |
8.53Кб |
| 01. Introducing Jeremy-U7RmFpVevis.en.vtt |
875б |
| 01. Introducing Jeremy-U7RmFpVevis.mp4 |
2.52Мб |
| 01. Introducing Jeremy-U7RmFpVevis.zh-CN.vtt |
804б |
| 01. Introduction.html |
9.15Кб |
| 01. Introduction-ZWRZvOXiC28.en.vtt |
3.53Кб |
| 01. Introduction-ZWRZvOXiC28.mp4 |
12.77Мб |
| 01. Introduction-ZWRZvOXiC28.zh-CN.vtt |
3.10Кб |
| 01. Intro to LSTM.html |
5.90Кб |
| 01. Modeling.html |
4.67Кб |
| 01. Natural Language Processing-UQBxJzoCp-I.en.vtt |
1.17Кб |
| 01. Natural Language Processing-UQBxJzoCp-I.mp4 |
3.39Мб |
| 01. Natural Language Processing-UQBxJzoCp-I.zh-CN.vtt |
1.03Кб |
| 01. NLP and Pipelines.html |
4.44Кб |
| 01. NLP Overview.html |
5.34Кб |
| 01. Overview.html |
4.84Кб |
| 01. Overview.html |
5.07Кб |
| 01. Text Processing.html |
5.17Кб |
| 01. Text Processing-6LO6I5M18PQ.en.vtt |
1.18Кб |
| 01. Text Processing-6LO6I5M18PQ.mp4 |
1.77Мб |
| 01. Text Processing-6LO6I5M18PQ.zh-CN.vtt |
1.06Кб |
| 01. Welcome to NLP-g-AlFF61p0I.en.vtt |
1.64Кб |
| 01. Welcome to NLP-g-AlFF61p0I.mp4 |
4.97Мб |
| 01. Welcome to NLP-g-AlFF61p0I.zh-CN.vtt |
1.49Кб |
| 02. Applications of CNNs.html |
12.37Кб |
| 02. Applications of CNNs-HrYNL_1SV2Y.en.vtt |
5.37Кб |
| 02. Applications of CNNs-HrYNL_1SV2Y.mp4 |
17.70Мб |
| 02. Applications of CNNs-HrYNL_1SV2Y.pt-BR.vtt |
5.66Кб |
| 02. Applications of CNNs-HrYNL_1SV2Y.zh-CN.vtt |
4.70Кб |
| 02. Bag of Words.html |
4.69Кб |
| 02. Bag Of Words-A7M1z8yLl0w.en.vtt |
4.72Кб |
| 02. Bag Of Words-A7M1z8yLl0w.mp4 |
4.01Мб |
| 02. Bag Of Words-A7M1z8yLl0w.zh-CN.vtt |
4.13Кб |
| 02. Classification Problems 1.html |
10.51Кб |
| 02. Classsification Example-Dh625piH7Z0.en.vtt |
2.70Кб |
| 02. Classsification Example-Dh625piH7Z0.mp4 |
2.07Мб |
| 02. Classsification Example-Dh625piH7Z0.pt-BR.vtt |
2.51Кб |
| 02. Classsification Example-Dh625piH7Z0.zh-CN.vtt |
2.37Кб |
| 02. Coding Exercises.html |
5.57Кб |
| 02. Embeddings Intro.html |
6.56Кб |
| 02. Getting Started.html |
4.58Кб |
| 02. How NLP Pipelines Work.html |
4.44Кб |
| 02. Installing TensorFlow.html |
8.10Кб |
| 02. Introduction.html |
5.03Кб |
| 02. Introduction-erwnzFD7AeE.en.vtt |
2.34Кб |
| 02. Introduction-erwnzFD7AeE.mp4 |
2.22Мб |
| 02. Introduction-erwnzFD7AeE.pt-BR.vtt |
2.17Кб |
| 02. Introduction-erwnzFD7AeE.zh-CN.vtt |
2.01Кб |
| 02. Introduction to GPU Workspaces.html |
15.13Кб |
| 02. IntroToRNNs Render-64HSG6HAfEI.en-US.vtt |
4.46Кб |
| 02. IntroToRNNs Render-64HSG6HAfEI.mp4 |
3.99Мб |
| 02. IntroToRNNs Render-64HSG6HAfEI.pt.vtt |
4.38Кб |
| 02. IntroToRNNs Render-64HSG6HAfEI.zh-CN.vtt |
3.88Кб |
| 02. Jay's Introduction-HPOzAlXhuxQ.en.vtt |
5.72Кб |
| 02. Jay's Introduction-HPOzAlXhuxQ.mp4 |
5.81Мб |
| 02. Jay's Introduction-HPOzAlXhuxQ.pt-BR.vtt |
5.58Кб |
| 02. Jay's Introduction-HPOzAlXhuxQ.zh-CN.vtt |
5.15Кб |
| 02. Jay Introduction.html |
5.08Кб |
| 02. Language Model.html |
173.52Кб |
| 02. NLP M1-L1 01 NLP Pipeline-vJx6oKlu_MM.en.vtt |
1.74Кб |
| 02. NLP M1-L1 01 NLP Pipeline-vJx6oKlu_MM.mp4 |
1.28Мб |
| 02. NLP M1-L1 01 NLP Pipeline-vJx6oKlu_MM.zh-CN.vtt |
1.53Кб |
| 02. Review of RNNs.html |
5.87Кб |
| 02. RNN vs LSTM.html |
5.12Кб |
| 02. RNN Vs LSTM-70MgF-IwAr8.en.vtt |
4.71Кб |
| 02. RNN Vs LSTM-70MgF-IwAr8.mp4 |
3.58Мб |
| 02. RNN Vs LSTM-70MgF-IwAr8.pt-BR.vtt |
4.24Кб |
| 02. RNN Vs LSTM-70MgF-IwAr8.zh-CN.vtt |
4.22Кб |
| 02. Section 1 Introduction-_zo6RiwmDCk.en.vtt |
2.16Кб |
| 02. Section 1 Introduction-_zo6RiwmDCk.mp4 |
4.72Мб |
| 02. Section 1 Introduction-_zo6RiwmDCk.zh-CN.vtt |
1.78Кб |
| 02. Section 1 Motivation for RNNs.html |
8.35Кб |
| 02. Sentiment Prediction-uGN3rZJRiMY.en.vtt |
8.66Кб |
| 02. Sentiment Prediction-uGN3rZJRiMY.mp4 |
11.25Мб |
| 02. Sentiment Prediction-uGN3rZJRiMY.pt-BR.vtt |
7.33Кб |
| 02. Sentiment Prediction-uGN3rZJRiMY.zh-CN.vtt |
8.00Кб |
| 02. Sentiment RNN.html |
4.70Кб |
| 02. Structured Languages.html |
8.40Кб |
| 02. Structured Languages-NsmqUIHlk6U.en.vtt |
1.79Кб |
| 02. Structured Languages-NsmqUIHlk6U.mp4 |
5.36Мб |
| 02. Structured Languages-NsmqUIHlk6U.zh-CN.vtt |
1.51Кб |
| 02-guide-how-transfer-learning-v3-01.png |
251.26Кб |
| 02-guide-how-transfer-learning-v3-02.png |
219.27Кб |
| 02-guide-how-transfer-learning-v3-03.png |
228.93Кб |
| 02-guide-how-transfer-learning-v3-04.png |
255.16Кб |
| 02-guide-how-transfer-learning-v3-05.png |
232.52Кб |
| 02-guide-how-transfer-learning-v3-06.png |
259.12Кб |
| 02-guide-how-transfer-learning-v3-07.png |
233.30Кб |
| 02-guide-how-transfer-learning-v3-08.png |
241.57Кб |
| 02-guide-how-transfer-learning-v3-09.png |
228.05Кб |
| 02-guide-how-transfer-learning-v3-10.png |
241.76Кб |
| 03. Applications.html |
5.06Кб |
| 03. Applications seq2seq-tDJBDwriJYQ.en.vtt |
2.63Кб |
| 03. Applications seq2seq-tDJBDwriJYQ.mp4 |
2.48Мб |
| 03. Applications seq2seq-tDJBDwriJYQ.pt-BR.vtt |
2.50Кб |
| 03. Applications seq2seq-tDJBDwriJYQ.zh-CN.vtt |
2.41Кб |
| 03. Basics of LSTM.html |
5.13Кб |
| 03. Capturing Text Data.html |
5.84Кб |
| 03. Capturing Text Data-Z4mnMN1ApG4.en.vtt |
1.77Кб |
| 03. Capturing Text Data-Z4mnMN1ApG4.mp4 |
2.37Мб |
| 03. Capturing Text Data-Z4mnMN1ApG4.zh-CN.vtt |
1.57Кб |
| 03. Classification Example-46PywnGa_cQ.en.vtt |
1.76Кб |
| 03. Classification Example-46PywnGa_cQ.mp4 |
1.62Мб |
| 03. Classification Example-46PywnGa_cQ.pt-BR.vtt |
1.60Кб |
| 03. Classification Example-46PywnGa_cQ.zh-CN.vtt |
1.65Кб |
| 03. Classification Problems 2.html |
9.34Кб |
| 03. Data Preprocessing.html |
4.70Кб |
| 03. Data Preprocessing-h4-LwZU9_k8.en.vtt |
5.33Кб |
| 03. Data Preprocessing-h4-LwZU9_k8.mp4 |
6.86Мб |
| 03. Data Preprocessing-h4-LwZU9_k8.pt-BR.vtt |
4.56Кб |
| 03. Data Preprocessing-h4-LwZU9_k8.zh-CN.vtt |
4.45Кб |
| 03. Grammar.html |
7.11Кб |
| 03. Grammar-Jw3dA7xmoQ4.en.vtt |
727б |
| 03. Grammar-Jw3dA7xmoQ4.mp4 |
2.14Мб |
| 03. Grammar-Jw3dA7xmoQ4.zh-CN.vtt |
617б |
| 03. Hello, Tensor World!.html |
8.23Кб |
| 03. How Computers Interpret Images.html |
8.48Кб |
| 03. How Computers Interpret Images-V4f6p6uRhu8.en.vtt |
5.52Кб |
| 03. How Computers Interpret Images-V4f6p6uRhu8.mp4 |
6.18Мб |
| 03. How Computers Interpret Images-V4f6p6uRhu8.pt-BR.vtt |
5.95Кб |
| 03. How Computers Interpret Images-V4f6p6uRhu8.zh-CN.vtt |
4.91Кб |
| 03. Implementing Word2Vec.html |
5.08Кб |
| 03. Implementing Word2Vec-7M431_f9HgE.en.vtt |
16.86Кб |
| 03. Implementing Word2Vec-7M431_f9HgE.mp4 |
23.33Мб |
| 03. Implementing Word2Vec-7M431_f9HgE.pt-BR.vtt |
17.23Кб |
| 03. Implementing Word2Vec-7M431_f9HgE.zh-CN.vtt |
14.27Кб |
| 03. Learning Rate.html |
5.58Кб |
| 03. Learning Rate-HLMjeDez7ps.en.vtt |
11.68Кб |
| 03. Learning Rate-HLMjeDez7ps.mp4 |
9.62Мб |
| 03. Learning Rate-HLMjeDez7ps.pt-BR.vtt |
10.29Кб |
| 03. Learning Rate-HLMjeDez7ps.zh-CN.vtt |
10.11Кб |
| 03. LSTM Basics-gjb68a4XsqE.en.vtt |
5.21Кб |
| 03. LSTM Basics-gjb68a4XsqE.mp4 |
4.03Мб |
| 03. LSTM Basics-gjb68a4XsqE.pt-BR.vtt |
5.06Кб |
| 03. LSTM Basics-gjb68a4XsqE.zh-CN.vtt |
4.59Кб |
| 03. LSTMs-RYbSHogZetc.en.vtt |
7.07Кб |
| 03. LSTMs-RYbSHogZetc.mp4 |
6.87Мб |
| 03. LSTMs-RYbSHogZetc.pt.vtt |
7.39Кб |
| 03. LSTMs-RYbSHogZetc.zh-CN.vtt |
6.46Кб |
| 03. Motivation for RNNs.html |
9.36Кб |
| 03. Motivation for RNNs-vPhD02WxMk8.en.vtt |
6.16Кб |
| 03. Motivation for RNNs-vPhD02WxMk8.mp4 |
5.73Мб |
| 03. Motivation for RNNs-vPhD02WxMk8.zh-CN.vtt |
5.51Кб |
| 03. NLP Machine Translation Workspace.html |
5.07Кб |
| 03. Review of LSTMs.html |
5.55Кб |
| 03. Sentiment Analysis.html |
8.35Кб |
| 03. Tasks.html |
7.34Кб |
| 03. Text Processing.html |
4.63Кб |
| 03. Text Processing-pqheVyctkNQ.en.vtt |
2.63Кб |
| 03. Text Processing-pqheVyctkNQ.mp4 |
2.96Мб |
| 03. Text Processing-pqheVyctkNQ.zh-CN.vtt |
2.30Кб |
| 03. TF-IDF.html |
4.65Кб |
| 03. TF-IDF-XZBiBIRcACE.en.vtt |
2.38Кб |
| 03. TF-IDF-XZBiBIRcACE.mp4 |
2.05Мб |
| 03. TF-IDF-XZBiBIRcACE.zh-CN.vtt |
2.07Кб |
| 04. Architecture encoder decoder-dkHdEAJnV_w.en.vtt |
5.45Кб |
| 04. Architecture encoder decoder-dkHdEAJnV_w.mp4 |
4.49Мб |
| 04. Architecture encoder decoder-dkHdEAJnV_w.pt-BR.vtt |
4.92Кб |
| 04. Architecture encoder decoder-dkHdEAJnV_w.zh-CN.vtt |
4.82Кб |
| 04. Architecture of LSTM.html |
5.17Кб |
| 04. Architectures.html |
5.10Кб |
| 04. Bookworm (Optional).html |
4.37Кб |
| 04. Character-Wise RNN-dXl3eWCGLdU.en.vtt |
3.33Кб |
| 04. Character-Wise RNN-dXl3eWCGLdU.mp4 |
2.88Мб |
| 04. Character-Wise RNN-dXl3eWCGLdU.pt-BR.vtt |
3.66Кб |
| 04. Character-Wise RNN-dXl3eWCGLdU.zh-CN.vtt |
3.04Кб |
| 04. Character-wise RNNs.html |
5.63Кб |
| 04. Creating Testing Sets.html |
5.04Кб |
| 04. Creating Testing Sets-BRBbrNLz1ho.en.vtt |
2.02Кб |
| 04. Creating Testing Sets-BRBbrNLz1ho.mp4 |
2.13Мб |
| 04. Creating Testing Sets-BRBbrNLz1ho.pt-BR.vtt |
1.74Кб |
| 04. Creating Testing Sets-BRBbrNLz1ho.zh-CN.vtt |
1.68Кб |
| 04. Feature Extraction.html |
4.68Кб |
| 04. Feature Extraction-UgENzCmfFWE.en.vtt |
3.82Кб |
| 04. Feature Extraction-UgENzCmfFWE.mp4 |
3.47Мб |
| 04. Feature Extraction-UgENzCmfFWE.zh-CN.vtt |
3.34Кб |
| 04. Learning Rate.html |
7.09Кб |
| 04. Linear Boundaries.html |
10.04Кб |
| 04. Linear Boundaries-X-uMlsBi07k.en.vtt |
3.85Кб |
| 04. Linear Boundaries-X-uMlsBi07k.mp4 |
3.85Мб |
| 04. Linear Boundaries-X-uMlsBi07k.pt-BR.vtt |
3.67Кб |
| 04. Linear Boundaries-X-uMlsBi07k.zh-CN.vtt |
3.36Кб |
| 04. LSTM Architecture-ycwthhdx8ws.en.vtt |
1.49Кб |
| 04. LSTM Architecture-ycwthhdx8ws.mp4 |
1.07Мб |
| 04. LSTM Architecture-ycwthhdx8ws.pt-BR.vtt |
1.46Кб |
| 04. LSTM Architecture-ycwthhdx8ws.zh-CN.vtt |
1.34Кб |
| 04. MLPs for Image Classification.html |
7.75Кб |
| 04. MLPs For Image Classification-TIFStebu530.en.vtt |
3.82Кб |
| 04. MLPs For Image Classification-TIFStebu530.mp4 |
4.40Мб |
| 04. MLPs For Image Classification-TIFStebu530.pt-BR.vtt |
4.06Кб |
| 04. MLPs For Image Classification-TIFStebu530.zh-CN.vtt |
3.42Кб |
| 04. One-Hot Encoding.html |
4.71Кб |
| 04. One-Hot Encoding-a0j1CDXFYZI.en.vtt |
1.40Кб |
| 04. One-Hot Encoding-a0j1CDXFYZI.mp4 |
1.08Мб |
| 04. One-Hot Encoding-a0j1CDXFYZI.zh-CN.vtt |
1.21Кб |
| 04. Quiz Read Text Files.html |
7.32Кб |
| 04. Quiz TensorFlow Linear Function.html |
21.13Кб |
| 04. Subsampling Solution.html |
5.08Кб |
| 04. Subsampling Solution-MAUM_mV_lj8.en.vtt |
5.99Кб |
| 04. Subsampling Solution-MAUM_mV_lj8.mp4 |
9.65Мб |
| 04. Subsampling Solution-MAUM_mV_lj8.pt-BR.vtt |
6.27Кб |
| 04. Subsampling Solution-MAUM_mV_lj8.zh-CN.vtt |
5.04Кб |
| 04. Topic Modeling.html |
17.52Кб |
| 04. Unstructured Text.html |
7.66Кб |
| 04. Unstructured Text-OmwSdaec5vU.en.vtt |
1.74Кб |
| 04. Unstructured Text-OmwSdaec5vU.mp4 |
4.63Мб |
| 04. Unstructured Text-OmwSdaec5vU.zh-CN.vtt |
1.55Кб |
| 04. Vanilla learning and structured input-oAt0eYD5_Tc.en.vtt |
6.63Кб |
| 04. Vanilla learning and structured input-oAt0eYD5_Tc.mp4 |
5.78Мб |
| 04. Vanilla learning and structured input-oAt0eYD5_Tc.zh-CN.vtt |
5.92Кб |
| 04. Vanilla supervised learners and structured input.html |
8.44Кб |
| 05. 09 Higher Dimensions-eBHunImDmWw.en.vtt |
2.95Кб |
| 05. 09 Higher Dimensions-eBHunImDmWw.mp4 |
2.59Мб |
| 05. 09 Higher Dimensions-eBHunImDmWw.pt-BR.vtt |
2.66Кб |
| 05. 09 Higher Dimensions-eBHunImDmWw.zh-CN.vtt |
2.38Кб |
| 05. Architecture in More Depth-rdAo4MqLbEk.en.vtt |
5.17Кб |
| 05. Architecture in More Depth-rdAo4MqLbEk.mp4 |
5.35Мб |
| 05. Architecture in More Depth-rdAo4MqLbEk.pt-BR.vtt |
5.02Кб |
| 05. Architecture in More Depth-rdAo4MqLbEk.zh-CN.vtt |
4.56Кб |
| 05. Architectures in More Depth.html |
5.12Кб |
| 05. Building the RNN.html |
4.89Кб |
| 05. Building The RNN 1-XTD6slf64fM.en.vtt |
13.87Кб |
| 05. Building The RNN 1-XTD6slf64fM.mp4 |
19.11Мб |
| 05. Building The RNN 1-XTD6slf64fM.pt-BR.vtt |
12.30Кб |
| 05. Building The RNN 1-XTD6slf64fM.zh-CN.vtt |
12.07Кб |
| 05. Categorical Cross-Entropy.html |
8.57Кб |
| 05. Categorical Cross-Entropy-3sDYifgjFck.en.vtt |
4.82Кб |
| 05. Categorical Cross-Entropy-3sDYifgjFck.mp4 |
5.42Мб |
| 05. Categorical Cross-Entropy-3sDYifgjFck.pt-BR.vtt |
5.13Кб |
| 05. Categorical Cross-Entropy-3sDYifgjFck.zh-CN.vtt |
4.24Кб |
| 05. Cleaning.html |
5.62Кб |
| 05. Cleaning-qawXp9DPV6I.en.vtt |
8.29Кб |
| 05. Cleaning-qawXp9DPV6I.mp4 |
19.59Мб |
| 05. Cleaning-qawXp9DPV6I.zh-CN.vtt |
7.47Кб |
| 05. Counting Words.html |
8.85Кб |
| 05. Higher Dimensions.html |
10.51Кб |
| 05. Learn Gate-aVHVI7ovbHY.en.vtt |
2.63Кб |
| 05. Learn Gate-aVHVI7ovbHY.mp4 |
2.22Мб |
| 05. Learn Gate-aVHVI7ovbHY.pt-BR.vtt |
2.66Кб |
| 05. Learn Gate-aVHVI7ovbHY.zh-CN.vtt |
2.51Кб |
| 05. Making Batches.html |
5.04Кб |
| 05. Making Batches-jx7qwdw-94k.en.vtt |
4.50Кб |
| 05. Making Batches-jx7qwdw-94k.mp4 |
7.73Мб |
| 05. Making Batches-jx7qwdw-94k.pt-BR.vtt |
4.51Кб |
| 05. Making Batches-jx7qwdw-94k.zh-CN.vtt |
3.77Кб |
| 05. Minibatch Size.html |
5.30Кб |
| 05. Minibatch Size-GrrO1NFxaW8.en.vtt |
5.36Кб |
| 05. Minibatch Size-GrrO1NFxaW8.mp4 |
4.78Мб |
| 05. Minibatch Size-GrrO1NFxaW8.pt-BR.vtt |
4.77Кб |
| 05. Minibatch Size-GrrO1NFxaW8.zh-CN.vtt |
4.77Кб |
| 05. Modeling.html |
4.35Кб |
| 05. Modeling-P4w_2rkxBvE.en.vtt |
1.29Кб |
| 05. Modeling-P4w_2rkxBvE.mp4 |
1.19Мб |
| 05. Modeling-P4w_2rkxBvE.zh-CN.vtt |
1.09Кб |
| 05. Quiz TensorFlow Softmax.html |
8.42Кб |
| 05. Search and Ranking.html |
6.27Кб |
| 05. Section 2 Introduction-behe53793wo.en.vtt |
606б |
| 05. Section 2 Introduction-behe53793wo.mp4 |
1.36Мб |
| 05. Section 2 Introduction-behe53793wo.zh-CN.vtt |
528б |
| 05. Section 2 Motivating and Modelling Recursive Sequences.html |
8.40Кб |
| 05. Sequence Batching.html |
5.63Кб |
| 05. Sequence-Batching-Z4OiyU0Cldg.en.vtt |
2.09Кб |
| 05. Sequence-Batching-Z4OiyU0Cldg.mp4 |
2.29Мб |
| 05. Sequence-Batching-Z4OiyU0Cldg.pt-BR.vtt |
2.33Кб |
| 05. Sequence-Batching-Z4OiyU0Cldg.zh-CN.vtt |
1.92Кб |
| 05. The Learn Gate.html |
5.72Кб |
| 05. Word Embeddings.html |
4.70Кб |
| 05. Word Embeddings-4mM_S9L2_JQ.en.vtt |
1.55Кб |
| 05. Word Embeddings-4mM_S9L2_JQ.mp4 |
1.22Мб |
| 05. Word Embeddings-4mM_S9L2_JQ.zh-CN.vtt |
1.26Кб |
| 06. Batches Solution.html |
5.05Кб |
| 06. Batches Solution-DdfR0RjSC-Q.en.vtt |
2.64Кб |
| 06. Batches Solution-DdfR0RjSC-Q.mp4 |
3.88Мб |
| 06. Batches Solution-DdfR0RjSC-Q.pt-BR.vtt |
2.50Кб |
| 06. Batches Solution-DdfR0RjSC-Q.zh-CN.vtt |
2.40Кб |
| 06. Character-wise RNN Notebook.html |
5.94Кб |
| 06. Context Is Everything.html |
5.84Кб |
| 06. Context-J-4pfu2w1C0.en.vtt |
2.12Кб |
| 06. Context-J-4pfu2w1C0.mp4 |
4.62Мб |
| 06. Context-J-4pfu2w1C0.zh-CN.vtt |
1.97Кб |
| 06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.en.vtt |
5.89Кб |
| 06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.mp4 |
5.13Мб |
| 06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.pt-BR.vtt |
5.61Кб |
| 06. DL 06 Perceptron Definition Fix V2-hImSxZyRiOw.zh-CN.vtt |
4.98Кб |
| 06. Forget Gate-iWxpfxLUPSU.en.vtt |
1.26Кб |
| 06. Forget Gate-iWxpfxLUPSU.mp4 |
1.04Мб |
| 06. Forget Gate-iWxpfxLUPSU.pt-BR.vtt |
1.33Кб |
| 06. Forget Gate-iWxpfxLUPSU.zh-CN.vtt |
1.12Кб |
| 06. How do we model an ordered sequence-LYcB8iV2bGE.en.vtt |
5.53Кб |
| 06. How do we model an ordered sequence-LYcB8iV2bGE.mp4 |
5.94Мб |
| 06. How do we model an ordered sequence-LYcB8iV2bGE.zh-CN.vtt |
4.97Кб |
| 06. Machine Translation.html |
19.97Кб |
| 06. Model Validation in Keras.html |
7.75Кб |
| 06. Model Validation in Keras-002jNXSM6CU.en.vtt |
5.51Кб |
| 06. Model Validation in Keras-002jNXSM6CU.mp4 |
5.20Мб |
| 06. Model Validation in Keras-002jNXSM6CU.pt-BR.vtt |
6.07Кб |
| 06. Model Validation in Keras-002jNXSM6CU.zh-CN.vtt |
4.74Кб |
| 06. Motivating and modeling recursive sequences.html |
8.42Кб |
| 06. Normalization.html |
5.89Кб |
| 06. Normalization-eOV2UUY8vtM.en.vtt |
3.24Кб |
| 06. Normalization-eOV2UUY8vtM.mp4 |
3.13Мб |
| 06. Normalization-eOV2UUY8vtM.zh-CN.vtt |
2.88Кб |
| 06. Number Of Iterations-TTdHpSb4DV8.en.vtt |
1.63Кб |
| 06. Number Of Iterations-TTdHpSb4DV8.mp4 |
1.46Мб |
| 06. Number Of Iterations-TTdHpSb4DV8.pt-BR.vtt |
1.53Кб |
| 06. Number Of Iterations-TTdHpSb4DV8.zh-CN.vtt |
1.50Кб |
| 06. Number of Training Iterations Epochs.html |
7.75Кб |
| 06. Perceptrons.html |
10.44Кб |
| 06. Preprocessing.html |
5.59Кб |
| 06. Preprocessing-ktQW6p9pOS4.en.vtt |
7.27Кб |
| 06. Preprocessing-ktQW6p9pOS4.mp4 |
8.88Мб |
| 06. Preprocessing-ktQW6p9pOS4.pt-BR.vtt |
6.81Кб |
| 06. Preprocessing-ktQW6p9pOS4.zh-CN.vtt |
6.40Кб |
| 06. Quiz TensorFlow Cross Entropy.html |
8.79Кб |
| 06. The Forget Gate.html |
5.65Кб |
| 06. Training the Network.html |
4.72Кб |
| 06. Training The Network-nknJ3Xu3ld0.en.vtt |
6.38Кб |
| 06. Training The Network-nknJ3Xu3ld0.mp4 |
8.41Мб |
| 06. Training The Network-nknJ3Xu3ld0.pt-BR.vtt |
5.70Кб |
| 06. Training The Network-nknJ3Xu3ld0.zh-CN.vtt |
5.42Кб |
| 06. Word2Vec.html |
4.66Кб |
| 06. Word2Vec-7jjappzGRe0.en.vtt |
3.42Кб |
| 06. Word2Vec-7jjappzGRe0.mp4 |
2.98Мб |
| 06. Word2Vec-7jjappzGRe0.zh-CN.vtt |
2.85Кб |
| 07. Building the Network.html |
5.08Кб |
| 07. Building The Network-fhSb5c6UX6M.en.vtt |
5.14Кб |
| 07. Building The Network-fhSb5c6UX6M.mp4 |
8.09Мб |
| 07. Building The Network-fhSb5c6UX6M.pt-BR.vtt |
5.00Кб |
| 07. Building The Network-fhSb5c6UX6M.zh-CN.vtt |
4.12Кб |
| 07. GloVe.html |
4.64Кб |
| 07. GloVe-KK3PMIiIn8o.en.vtt |
4.21Кб |
| 07. GloVe-KK3PMIiIn8o.mp4 |
3.81Мб |
| 07. GloVe-KK3PMIiIn8o.zh-CN.vtt |
3.60Кб |
| 07. Implementing a Character-wise RNN.html |
5.73Кб |
| 07. Implementing a Character-wise RNN-KPCMn_jg2oY.en.vtt |
9.94Кб |
| 07. Implementing a Character-wise RNN-KPCMn_jg2oY.mp4 |
13.63Мб |
| 07. Implementing a Character-wise RNN-KPCMn_jg2oY.pt-BR.vtt |
10.69Кб |
| 07. Implementing a Character-wise RNN-KPCMn_jg2oY.zh-CN.vtt |
8.71Кб |
| 07. Natural Language Processing-sQiURKPFXNM.en.vtt |
4.41Кб |
| 07. Natural Language Processing-sQiURKPFXNM.mp4 |
15.48Мб |
| 07. Natural Language Processing-sQiURKPFXNM.zh-CN.vtt |
4.03Кб |
| 07. NLP and IBM Watson.html |
5.41Кб |
| 07. NLP Resources.html |
5.56Кб |
| 07. Number of Hidden Units Layers.html |
5.96Кб |
| 07. Number Of Hidden Units Layers-IkGAIQH5wH8.en.vtt |
3.23Кб |
| 07. Number Of Hidden Units Layers-IkGAIQH5wH8.mp4 |
3.40Мб |
| 07. Number Of Hidden Units Layers-IkGAIQH5wH8.pt-BR.vtt |
3.02Кб |
| 07. Number Of Hidden Units Layers-IkGAIQH5wH8.zh-CN.vtt |
2.82Кб |
| 07. Quiz Mini-batch.html |
25.02Кб |
| 07. Remember Gate-0qlm86HaXuU.en.vtt |
734б |
| 07. Remember Gate-0qlm86HaXuU.mp4 |
676.91Кб |
| 07. Remember Gate-0qlm86HaXuU.pt-BR.vtt |
700б |
| 07. Remember Gate-0qlm86HaXuU.zh-CN.vtt |
632б |
| 07. Sentiment RNN 2-V9YGGjmoHS0.en.vtt |
17.68Кб |
| 07. Sentiment RNN 2-V9YGGjmoHS0.mp4 |
23.11Мб |
| 07. Sentiment RNN 2-V9YGGjmoHS0.pt-BR.vtt |
15.77Кб |
| 07. Sentiment RNN 2-V9YGGjmoHS0.zh-CN.vtt |
14.72Кб |
| 07. Sequence to sequence in TensorFlow.html |
8.01Кб |
| 07. Simple recursive examples.html |
8.58Кб |
| 07. Simple recursive examples-F4-CBqMsd_Y.en.vtt |
8.06Кб |
| 07. Simple recursive examples-F4-CBqMsd_Y.mp4 |
7.53Мб |
| 07. Simple recursive examples-F4-CBqMsd_Y.zh-CN.vtt |
7.15Кб |
| 07. Solutions.html |
4.67Кб |
| 07. The Remember Gate.html |
5.53Кб |
| 07. Tokenization.html |
5.45Кб |
| 07. Tokenization-4Ieotbeh4u8.en.vtt |
2.89Кб |
| 07. Tokenization-4Ieotbeh4u8.mp4 |
3.22Мб |
| 07. Tokenization-4Ieotbeh4u8.zh-CN.vtt |
2.59Кб |
| 07. When do MLPs (not) work well .html |
7.39Кб |
| 07. When do MLPs (not) work well-deMeuLdZN3Q.en.vtt |
3.61Кб |
| 07. When do MLPs (not) work well-deMeuLdZN3Q.mp4 |
5.54Мб |
| 07. When do MLPs (not) work well-deMeuLdZN3Q.pt-BR.vtt |
3.84Кб |
| 07. When do MLPs (not) work well-deMeuLdZN3Q.zh-CN.vtt |
3.11Кб |
| 07. Why Neural Networks.html |
9.33Кб |
| 07. Why Neural Networks-zAkzOZntK6Y.en.vtt |
1.38Кб |
| 07. Why Neural Networks-zAkzOZntK6Y.mp4 |
982.27Кб |
| 07. Why Neural Networks-zAkzOZntK6Y.pt-BR.vtt |
1.27Кб |
| 07. Why Neural Networks-zAkzOZntK6Y.zh-CN.vtt |
1.18Кб |
| 08. AND And OR Perceptrons-45K5N0P9wJk.en.vtt |
3.00Кб |
| 08. AND And OR Perceptrons-45K5N0P9wJk.mp4 |
2.68Мб |
| 08. AND And OR Perceptrons-45K5N0P9wJk.pt-BR.vtt |
3.15Кб |
| 08. AND And OR Perceptrons-45K5N0P9wJk.zh-CN.vtt |
2.48Кб |
| 08. Applications of NLP.html |
5.38Кб |
| 08. Applications of NLP-33dq-H6U4AI.en.vtt |
7.00Кб |
| 08. Applications of NLP-33dq-H6U4AI.mp4 |
27.97Мб |
| 08. Applications of NLP-33dq-H6U4AI.zh-CN.vtt |
6.12Кб |
| 08. Batching Data Solution.html |
5.66Кб |
| 08. Batching Data Solution-o3nBxHJLQcc.en.vtt |
4.31Кб |
| 08. Batching Data Solution-o3nBxHJLQcc.mp4 |
5.08Мб |
| 08. Batching Data Solution-o3nBxHJLQcc.pt-BR.vtt |
4.33Кб |
| 08. Batching Data Solution-o3nBxHJLQcc.zh-CN.vtt |
3.70Кб |
| 08. Embeddings for Deep Learning.html |
4.78Кб |
| 08. Embeddings For Deep Learning-gj8u1KG0H2w.en.vtt |
5.11Кб |
| 08. Embeddings For Deep Learning-gj8u1KG0H2w.mp4 |
4.70Мб |
| 08. Embeddings For Deep Learning-gj8u1KG0H2w.zh-CN.vtt |
4.70Кб |
| 08. Epochs.html |
12.70Кб |
| 08. Inputs.html |
15.30Кб |
| 08. LSTM 7 Use Gate-5Ifolm1jTdY.en.vtt |
1.75Кб |
| 08. LSTM 7 Use Gate-5Ifolm1jTdY.mp4 |
1.50Мб |
| 08. LSTM 7 Use Gate-5Ifolm1jTdY.pt-BR.vtt |
1.73Кб |
| 08. LSTM 7 Use Gate-5Ifolm1jTdY.zh-CN.vtt |
1.50Кб |
| 08. Mini Project Training an MLP on MNIST.html |
10.34Кб |
| 08. Negative Sampling.html |
5.06Кб |
| 08. Negative Sampling-gW17AHBKbHY.en.vtt |
2.55Кб |
| 08. Negative Sampling-gW17AHBKbHY.mp4 |
4.16Мб |
| 08. Negative Sampling-gW17AHBKbHY.pt-BR.vtt |
2.36Кб |
| 08. Negative Sampling-gW17AHBKbHY.zh-CN.vtt |
2.19Кб |
| 08. NLP Summary-B9ul8fsQYOA.en.vtt |
688б |
| 08. NLP Summary-B9ul8fsQYOA.mp4 |
1.38Мб |
| 08. NLP Summary-B9ul8fsQYOA.zh-CN.vtt |
624б |
| 08. Perceptrons as Logical Operators.html |
20.86Кб |
| 08. Quiz Split Sentences.html |
8.16Кб |
| 08. Recursive or not Part 1.html |
9.57Кб |
| 08. RNN Hyperparameters.html |
11.28Кб |
| 08. RNN Hyperparameters-yQvnv7l_aUo.en.vtt |
3.87Кб |
| 08. RNN Hyperparameters-yQvnv7l_aUo.mp4 |
4.12Мб |
| 08. RNN Hyperparameters-yQvnv7l_aUo.pt-BR.vtt |
3.71Кб |
| 08. RNN Hyperparameters-yQvnv7l_aUo.zh-CN.vtt |
3.62Кб |
| 08. Summary.html |
4.59Кб |
| 08. The Use Gate.html |
5.77Кб |
| 08. XOR Perceptron-TF83GfjYLdw.en.vtt |
1.01Кб |
| 08. XOR Perceptron-TF83GfjYLdw.mp4 |
947.00Кб |
| 08. XOR Perceptron-TF83GfjYLdw.pt-BR.vtt |
1.00Кб |
| 08. XOR Perceptron-TF83GfjYLdw.zh-CN.vtt |
1021б |
| 09. 07 Perceptron Algorithm Trick-lif_qPmXvWA.en.vtt |
4.11Кб |
| 09. 07 Perceptron Algorithm Trick-lif_qPmXvWA.mp4 |
3.66Мб |
| 09. 07 Perceptron Algorithm Trick-lif_qPmXvWA.pt-BR.vtt |
4.17Кб |
| 09. 07 Perceptron Algorithm Trick-lif_qPmXvWA.zh-CN.vtt |
3.50Кб |
| 09. Building the Network Solution.html |
5.14Кб |
| 09. Building The Network Solution-pkBAhQ2Ki-8.en.vtt |
4.06Кб |
| 09. Building The Network Solution-pkBAhQ2Ki-8.mp4 |
7.34Мб |
| 09. Building The Network Solution-pkBAhQ2Ki-8.pt-BR.vtt |
4.03Кб |
| 09. Building The Network Solution-pkBAhQ2Ki-8.zh-CN.vtt |
3.34Кб |
| 09. Challenges in NLP.html |
5.33Кб |
| 09. DL 10 S Perceptron Algorithm-fATmrG2hQzI.en.vtt |
420б |
| 09. DL 10 S Perceptron Algorithm-fATmrG2hQzI.mp4 |
260.01Кб |
| 09. DL 10 S Perceptron Algorithm-fATmrG2hQzI.pt-BR.vtt |
364б |
| 09. DL 10 S Perceptron Algorithm-fATmrG2hQzI.zh-CN.vtt |
390б |
| 09. Further Reading.html |
5.53Кб |
| 09. Lab TensorFlow Neural Network.html |
10.39Кб |
| 09. Local Connectivity.html |
7.05Кб |
| 09. Local Connectivity-z9wiDg0w-Dc.en.vtt |
8.95Кб |
| 09. Local Connectivity-z9wiDg0w-Dc.mp4 |
13.09Мб |
| 09. Local Connectivity-z9wiDg0w-Dc.pt-BR.vtt |
9.29Кб |
| 09. Local Connectivity-z9wiDg0w-Dc.zh-CN.vtt |
7.62Кб |
| 09. LSTM Cell.html |
5.57Кб |
| 09. LSTM Cell-ajC-5uWB8S4.en.vtt |
6.06Кб |
| 09. LSTM Cell-ajC-5uWB8S4.mp4 |
7.79Мб |
| 09. LSTM Cell-ajC-5uWB8S4.pt-BR.vtt |
6.21Кб |
| 09. LSTM Cell-ajC-5uWB8S4.zh-CN.vtt |
5.30Кб |
| 09. NLP H 8-LMvKyA3ZBZE.en.vtt |
8.38Кб |
| 09. NLP H 8-LMvKyA3ZBZE.mp4 |
30.44Мб |
| 09. NLP H 8-LMvKyA3ZBZE.zh-CN.vtt |
7.42Кб |
| 09. Perceptron Algorithm--zhTROHtscQ.en.vtt |
2.64Кб |
| 09. Perceptron Algorithm--zhTROHtscQ.mp4 |
1.92Мб |
| 09. Perceptron Algorithm--zhTROHtscQ.pt-BR.vtt |
2.41Кб |
| 09. Perceptron Algorithm--zhTROHtscQ.zh-CN.vtt |
2.35Кб |
| 09. Perceptron Trick.html |
13.12Кб |
| 09. Putting it All Together.html |
5.20Кб |
| 09. Putting It All Together-IF8FlKW-Zo0.en.vtt |
2.42Кб |
| 09. Putting It All Together-IF8FlKW-Zo0.mp4 |
1.58Мб |
| 09. Putting It All Together-IF8FlKW-Zo0.pt-BR.vtt |
2.36Кб |
| 09. Putting It All Together-IF8FlKW-Zo0.zh-CN.vtt |
2.13Кб |
| 09. Recursive or not Part 2.html |
9.72Кб |
| 09. RNN Hyperparameters.html |
6.26Кб |
| 09. Stop Word Removal.html |
5.18Кб |
| 09. Stop Word Removal-WAU_Ij0GJbw.en.vtt |
1.59Кб |
| 09. Stop Word Removal-WAU_Ij0GJbw.mp4 |
1.96Мб |
| 09. Stop Word Removal-WAU_Ij0GJbw.zh-CN.vtt |
1.40Кб |
| 09. t-SNE.html |
4.64Кб |
| 09. T-SNE-xxcK8oZ6_WE.en.vtt |
2.17Кб |
| 09. T-SNE-xxcK8oZ6_WE.mp4 |
5.56Мб |
| 09. T-SNE-xxcK8oZ6_WE.zh-CN.vtt |
1.84Кб |
| 10. Convolutional Layers (Part 1).html |
7.08Кб |
| 10. Convolutional Layers-h5R_JvdUrUI.en.vtt |
7.22Кб |
| 10. Convolutional Layers-h5R_JvdUrUI.mp4 |
8.04Мб |
| 10. Convolutional Layers-h5R_JvdUrUI.pt-BR.vtt |
7.57Кб |
| 10. Convolutional Layers-h5R_JvdUrUI.zh-CN.vtt |
6.10Кб |
| 10. LSTM Cell Solution.html |
5.63Кб |
| 10. LSTM Cell Solution-X4uA0dq_4jA.en.vtt |
3.13Кб |
| 10. LSTM Cell Solution-X4uA0dq_4jA.mp4 |
3.55Мб |
| 10. LSTM Cell Solution-X4uA0dq_4jA.pt-BR.vtt |
3.06Кб |
| 10. LSTM Cell Solution-X4uA0dq_4jA.zh-CN.vtt |
2.82Кб |
| 10. NLP Services.html |
7.66Кб |
| 10. NLP Services-vTupzLkpxJU.en.vtt |
5.00Кб |
| 10. NLP Services-vTupzLkpxJU.mp4 |
16.49Мб |
| 10. NLP Services-vTupzLkpxJU.zh-CN.vtt |
4.41Кб |
| 10. Part-of-Speech Tagging.html |
5.75Кб |
| 10. Part-of-Speech Tagging-WFEu8bXI5OA.en.vtt |
1.67Кб |
| 10. Part-of-Speech Tagging-WFEu8bXI5OA.mp4 |
2.15Мб |
| 10. Part-of-Speech Tagging-WFEu8bXI5OA.zh-CN.vtt |
1.48Кб |
| 10. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.en.vtt |
3.45Кб |
| 10. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.mp4 |
2.87Мб |
| 10. Perceptron Agorithm Pseudocode-p8Q3yu9YqYk.pt-BR.vtt |
3.27Кб |
| 10. Perceptron Algorithm.html |
16.71Кб |
| 10. Quiz.html |
6.95Кб |
| 10. Recursive or not Part 3.html |
9.54Кб |
| 10. Sequence to Sequence in TensorFlow.html |
5.24Кб |
| 10. Sources & References.html |
6.03Кб |
| 10. Training Results.html |
5.05Кб |
| 10. Training Results-uISA5ns47s8.en.vtt |
4.36Кб |
| 10. Training Results-uISA5ns47s8.mp4 |
9.78Мб |
| 10. Training Results-uISA5ns47s8.pt-BR.vtt |
4.38Кб |
| 10. Training Results-uISA5ns47s8.zh-CN.vtt |
3.83Кб |
| 10. Two-layer Neural Network.html |
7.40Кб |
| 11. Convolutional Layers (Part 2).html |
7.85Кб |
| 11. Convolutional Layers-RnM1D-XI--8.en.vtt |
9.99Кб |
| 11. Convolutional Layers-RnM1D-XI--8.mp4 |
19.81Мб |
| 11. Convolutional Layers-RnM1D-XI--8.pt-BR.vtt |
11.00Кб |
| 11. Convolutional Layers-RnM1D-XI--8.zh-CN.vtt |
8.71Кб |
| 11. Getting Started with Watson.html |
6.56Кб |
| 11. Getting Started with Watson-9LTS9JfGNzM.en.vtt |
13.99Кб |
| 11. Getting Started with Watson-9LTS9JfGNzM.mp4 |
21.81Мб |
| 11. Getting Started with Watson-9LTS9JfGNzM.zh-CN.vtt |
12.55Кб |
| 11. Graphical model representations of recursive sequences-OS9yQCTzCkg.en.vtt |
8.40Кб |
| 11. Graphical model representations of recursive sequences-OS9yQCTzCkg.mp4 |
7.63Мб |
| 11. Graphical model representations of recursive sequences-OS9yQCTzCkg.zh-CN.vtt |
7.57Кб |
| 11. Named Entity Recognition.html |
5.22Кб |
| 11. Named Entity Recognition-QUQu2nsE7vE.en.vtt |
1.10Кб |
| 11. Named Entity Recognition-QUQu2nsE7vE.mp4 |
1.17Мб |
| 11. Named Entity Recognition-QUQu2nsE7vE.zh-CN.vtt |
1.00Кб |
| 11. Non-Linear Regions.html |
9.31Кб |
| 11. Non-Linear Regions-B8UrWnHh1Wc.en.vtt |
1.77Кб |
| 11. Non-Linear Regions-B8UrWnHh1Wc.mp4 |
1.33Мб |
| 11. Non-Linear Regions-B8UrWnHh1Wc.pt-BR.vtt |
1.51Кб |
| 11. Non-Linear Regions-B8UrWnHh1Wc.zh-CN.vtt |
1.57Кб |
| 11. Other architectures.html |
5.57Кб |
| 11. Other Architectures-MsxFDuYlTuQ.en.vtt |
2.31Кб |
| 11. Other Architectures-MsxFDuYlTuQ.mp4 |
1.71Мб |
| 11. Other Architectures-MsxFDuYlTuQ.pt-BR.vtt |
2.45Кб |
| 11. Other Architectures-MsxFDuYlTuQ.zh-CN.vtt |
2.04Кб |
| 11. Quiz TensorFlow ReLUs.html |
9.04Кб |
| 11. RNN Output.html |
5.58Кб |
| 11. RNN Output-RkanDkcrTxs.en.vtt |
5.73Кб |
| 11. RNN Output-RkanDkcrTxs.mp4 |
8.92Мб |
| 11. RNN Output-RkanDkcrTxs.pt-BR.vtt |
5.64Кб |
| 11. RNN Output-RkanDkcrTxs.zh-CN.vtt |
5.14Кб |
| 11. Ways of thinking about recursivity.html |
8.48Кб |
| 12. A simple model for my savings account balance-JQ2Nzzxx5oQ.en.vtt |
9.61Кб |
| 12. A simple model for my savings account balance-JQ2Nzzxx5oQ.mp4 |
9.01Мб |
| 12. A simple model for my savings account balance-JQ2Nzzxx5oQ.zh-CN.vtt |
8.70Кб |
| 12. Deep Neural Network in TensorFlow.html |
11.26Кб |
| 12. Deploying a Bluemix Application.html |
5.96Кб |
| 12. Deploying a Bluemix Application-YF2SgUXzk9k.en.vtt |
1.21Кб |
| 12. Deploying a Bluemix Application-YF2SgUXzk9k.mp4 |
2.85Мб |
| 12. Deploying a Bluemix Application-YF2SgUXzk9k.zh-CN.vtt |
1.08Кб |
| 12. Driving a recursive sequence.html |
8.43Кб |
| 12. Error Functions.html |
9.29Кб |
| 12. Error Functions-YfUUunxWIJw.en.vtt |
790б |
| 12. Error Functions-YfUUunxWIJw.mp4 |
3.54Мб |
| 12. Error Functions-YfUUunxWIJw.pt-BR.vtt |
804б |
| 12. Error Functions-YfUUunxWIJw.zh-CN.vtt |
739б |
| 12. Network Loss.html |
5.59Кб |
| 12. Network Loss-itu-uNK4brc.en.vtt |
3.08Кб |
| 12. Network Loss-itu-uNK4brc.mp4 |
4.28Мб |
| 12. Network Loss-itu-uNK4brc.pt-BR.vtt |
3.16Кб |
| 12. Network Loss-itu-uNK4brc.zh-CN.vtt |
2.71Кб |
| 12. Outro LSTM.html |
5.10Кб |
| 12. Stemming and Lemmatization.html |
5.23Кб |
| 12. Stemming And Lemmatization-7Gjf81u5hmw.en.vtt |
4.77Кб |
| 12. Stemming And Lemmatization-7Gjf81u5hmw.mp4 |
4.93Мб |
| 12. Stemming And Lemmatization-7Gjf81u5hmw.zh-CN.vtt |
4.26Кб |
| 12. Stride and Padding.html |
7.05Кб |
| 12. Stride and Padding-0r9o8hprDXQ.en.vtt |
4.41Кб |
| 12. Stride and Padding-0r9o8hprDXQ.mp4 |
7.98Мб |
| 12. Stride and Padding-0r9o8hprDXQ.pt-BR.vtt |
4.55Кб |
| 12. Stride and Padding-0r9o8hprDXQ.zh-CN.vtt |
3.74Кб |
| 13. Convolutional Layers in Keras.html |
11.41Кб |
| 13. Error Functions-jfKShxGAbok.en.vtt |
9.45Кб |
| 13. Error Functions-jfKShxGAbok.mp4 |
7.21Мб |
| 13. Error Functions-jfKShxGAbok.pt-BR.vtt |
9.14Кб |
| 13. Error Functions-jfKShxGAbok.zh-CN.vtt |
8.35Кб |
| 13. Log-loss Error Function.html |
10.96Кб |
| 13. Output and Loss Solutions.html |
5.68Кб |
| 13. Output And Loss Solutions-CT8hcU7FmGc.en.vtt |
3.45Кб |
| 13. Output And Loss Solutions-CT8hcU7FmGc.mp4 |
4.69Мб |
| 13. Output And Loss Solutions-CT8hcU7FmGc.pt-BR.vtt |
3.21Кб |
| 13. Output And Loss Solutions-CT8hcU7FmGc.zh-CN.vtt |
3.05Кб |
| 13. Save and Restore TensorFlow Models.html |
14.36Кб |
| 13. Section summary.html |
8.32Кб |
| 13. Summarizing recursivity-l1H8dfGW2A4.en.vtt |
1.39Кб |
| 13. Summarizing recursivity-l1H8dfGW2A4.mp4 |
1.73Мб |
| 13. Summarizing recursivity-l1H8dfGW2A4.zh-CN.vtt |
1.27Кб |
| 13. Summary.html |
5.12Кб |
| 13. Summary-zKYEvRd2XmI.en.vtt |
1.11Кб |
| 13. Summary-zKYEvRd2XmI.mp4 |
977.95Кб |
| 13. Summary-zKYEvRd2XmI.zh-CN.vtt |
984б |
| 13. Towards Augmented Intelligence.html |
5.44Кб |
| 13. Towards Augmented Intelligence-6y2B8VKoGTw.en.vtt |
3.31Кб |
| 13. Towards Augmented Intelligence-6y2B8VKoGTw.mp4 |
12.37Мб |
| 13. Towards Augmented Intelligence-6y2B8VKoGTw.zh-CN.vtt |
3.02Кб |
| 14. [Preview] Project Bookworm.html |
11.13Кб |
| 14. Build the Network.html |
5.63Кб |
| 14. Build The Network-RVNjDlWTBQU.en.vtt |
4.09Кб |
| 14. Build The Network-RVNjDlWTBQU.mp4 |
7.10Мб |
| 14. Build The Network-RVNjDlWTBQU.pt-BR.vtt |
4.00Кб |
| 14. Build The Network-RVNjDlWTBQU.zh-CN.vtt |
3.45Кб |
| 14. Discrete vs. Continuous-Rm2KxFaPiJg.en.vtt |
5.70Кб |
| 14. Discrete vs. Continuous-Rm2KxFaPiJg.mp4 |
5.35Мб |
| 14. Discrete vs. Continuous-Rm2KxFaPiJg.pt-BR.vtt |
5.67Кб |
| 14. Discrete vs. Continuous-Rm2KxFaPiJg.zh-CN.vtt |
4.67Кб |
| 14. Discrete vs Continuous.html |
11.57Кб |
| 14. Discrete vs Continuous-rdP-RPDFkl0.en.vtt |
551б |
| 14. Discrete vs Continuous-rdP-RPDFkl0.mp4 |
2.26Мб |
| 14. Discrete vs Continuous-rdP-RPDFkl0.pt-BR.vtt |
584б |
| 14. Discrete vs Continuous-rdP-RPDFkl0.zh-CN.vtt |
481б |
| 14. Finetuning.html |
9.76Кб |
| 14. Quiz Dimensionality.html |
15.80Кб |
| 14. Section 3 Injecting recursivity into a learner (the lazy wa.html |
8.41Кб |
| 14. Section 3 Introduction-VyE6NcPbX9Q.en.vtt |
594б |
| 14. Section 3 Introduction-VyE6NcPbX9Q.mp4 |
1.49Мб |
| 14. Section 3 Introduction-VyE6NcPbX9Q.zh-CN.vtt |
510б |
| 15. AIND RNN 3 0-jXdIx18dIa0.en.vtt |
3.19Кб |
| 15. AIND RNN 3 0-jXdIx18dIa0.mp4 |
2.84Мб |
| 15. AIND RNN 3 0-jXdIx18dIa0.zh-CN.vtt |
2.92Кб |
| 15. Build The Network And Results-hu8iMMqajmQ.en.vtt |
7.81Кб |
| 15. Build The Network And Results-hu8iMMqajmQ.mp4 |
13.27Мб |
| 15. Build The Network And Results-hu8iMMqajmQ.pt-BR.vtt |
7.64Кб |
| 15. Build The Network And Results-hu8iMMqajmQ.zh-CN.vtt |
6.85Кб |
| 15. Build the Network Solution.html |
5.70Кб |
| 15. DL 18 Q Softmax V2-RC_A9Tu99y4.en.vtt |
5.37Кб |
| 15. DL 18 Q Softmax V2-RC_A9Tu99y4.mp4 |
4.01Мб |
| 15. DL 18 Q Softmax V2-RC_A9Tu99y4.pt-BR.vtt |
5.06Кб |
| 15. DL 18 Q Softmax V2-RC_A9Tu99y4.zh-CN.vtt |
4.37Кб |
| 15. DL 18 S Softmax-n8S-v_LCTms.en.vtt |
2.59Кб |
| 15. DL 18 S Softmax-n8S-v_LCTms.mp4 |
1.95Мб |
| 15. DL 18 S Softmax-n8S-v_LCTms.pt-BR.vtt |
2.52Кб |
| 15. DL 18 S Softmax-n8S-v_LCTms.zh-CN.vtt |
2.30Кб |
| 15. Injecting Recursivity into a Learner (the lazy way).html |
8.35Кб |
| 15. Pooling Layers.html |
7.31Кб |
| 15. Pooling Layers-OkkIZNs7Cyc.en.vtt |
5.40Кб |
| 15. Pooling Layers-OkkIZNs7Cyc.mp4 |
5.82Мб |
| 15. Pooling Layers-OkkIZNs7Cyc.pt-BR.vtt |
5.81Кб |
| 15. Pooling Layers-OkkIZNs7Cyc.zh-CN.vtt |
4.64Кб |
| 15. Quiz - Softmax-NNoezNnAMTY.en.vtt |
495б |
| 15. Quiz - Softmax-NNoezNnAMTY.mp4 |
1.73Мб |
| 15. Quiz - Softmax-NNoezNnAMTY.pt-BR.vtt |
501б |
| 15. Quiz - Softmax-NNoezNnAMTY.zh-CN.vtt |
548б |
| 15. Quiz TensorFlow Dropout.html |
13.34Кб |
| 15. Softmax.html |
13.57Кб |
| 16. A first example.html |
8.29Кб |
| 16. AIND RNN 3 1 1-NMZ4fU2CuHg.en.vtt |
3.70Кб |
| 16. AIND RNN 3 1 1-NMZ4fU2CuHg.mp4 |
3.75Мб |
| 16. AIND RNN 3 1 1-NMZ4fU2CuHg.zh-CN.vtt |
3.41Кб |
| 16. Max Pooling Layers in Keras.html |
10.22Кб |
| 16. One-Hot Encoding.html |
9.30Кб |
| 16. One-Hot Encoding-AePvjhyvsBo.en.vtt |
2.23Кб |
| 16. One-Hot Encoding-AePvjhyvsBo.mp4 |
1.65Мб |
| 16. One-Hot Encoding-AePvjhyvsBo.pt-BR.vtt |
2.03Кб |
| 16. One-Hot Encoding-AePvjhyvsBo.zh-CN.vtt |
2.02Кб |
| 16. RNN Resources.html |
5.85Кб |
| 17. 3 1 2 Introducing A Parameterized Formula-I72EOcAroFk.en.vtt |
3.64Кб |
| 17. 3 1 2 Introducing A Parameterized Formula-I72EOcAroFk.mp4 |
3.90Мб |
| 17. 3 1 2 Introducing A Parameterized Formula-I72EOcAroFk.zh-CN.vtt |
3.42Кб |
| 17. CNNs for Image Classification.html |
10.07Кб |
| 17. CNNs For Image Classification-l9vg_1YUlzg.en.vtt |
11.37Кб |
| 17. CNNs For Image Classification-l9vg_1YUlzg.mp4 |
18.16Мб |
| 17. CNNs For Image Classification-l9vg_1YUlzg.pt-BR.vtt |
12.21Кб |
| 17. CNNs For Image Classification-l9vg_1YUlzg.zh-CN.vtt |
9.72Кб |
| 17. Maximum Likelihood.html |
11.67Кб |
| 17. Maximum Likelihood 1-1yJx-QtlvNI.en.vtt |
1.64Кб |
| 17. Maximum Likelihood 1-1yJx-QtlvNI.mp4 |
5.75Мб |
| 17. Maximum Likelihood 1-1yJx-QtlvNI.pt-BR.vtt |
1.61Кб |
| 17. Maximum Likelihood 1-1yJx-QtlvNI.zh-CN.vtt |
1.43Кб |
| 17. Maximum Likelihood 2-6nUUeQ9AeUA.en.vtt |
4.41Кб |
| 17. Maximum Likelihood 2-6nUUeQ9AeUA.mp4 |
3.85Мб |
| 17. Maximum Likelihood 2-6nUUeQ9AeUA.pt-BR.vtt |
4.49Кб |
| 17. Maximum Likelihood 2-6nUUeQ9AeUA.zh-CN.vtt |
3.67Кб |
| 17. Setting up the example.html |
8.41Кб |
| 18. AIND RNN 3 1 3-R1T3JXi_jKY.en.vtt |
3.17Кб |
| 18. AIND RNN 3 1 3-R1T3JXi_jKY.mp4 |
3.26Мб |
| 18. AIND RNN 3 1 3-R1T3JXi_jKY.zh-CN.vtt |
2.88Кб |
| 18. CNNs in Keras Practical Example.html |
8.83Кб |
| 18. CNNs in Keras Practical Example-faFvmGDwXX0.en.vtt |
5.39Кб |
| 18. CNNs in Keras Practical Example-faFvmGDwXX0.mp4 |
8.71Мб |
| 18. CNNs in Keras Practical Example-faFvmGDwXX0.pt-BR.vtt |
6.12Кб |
| 18. CNNs in Keras Practical Example-faFvmGDwXX0.zh-CN.vtt |
4.78Кб |
| 18. Maximizing Probabilities.html |
11.14Кб |
| 18. Quiz - Cross 1--xxrisIvD0E.en.vtt |
918б |
| 18. Quiz - Cross 1--xxrisIvD0E.mp4 |
3.02Мб |
| 18. Quiz - Cross 1--xxrisIvD0E.pt-BR.vtt |
947б |
| 18. Quiz - Cross 1--xxrisIvD0E.zh-CN.vtt |
813б |
| 18. Quiz Cross Entropy-njq6bYrPqSU.en.vtt |
2.30Кб |
| 18. Quiz Cross Entropy-njq6bYrPqSU.mp4 |
1.86Мб |
| 18. Quiz Cross Entropy-njq6bYrPqSU.pt-BR.vtt |
2.28Кб |
| 18. Quiz Cross Entropy-njq6bYrPqSU.zh-CN.vtt |
2.07Кб |
| 18. Windowing the example sequence.html |
8.32Кб |
| 19. AIND RNN 3 1 4-XYljYztPvUs.en.vtt |
3.30Кб |
| 19. AIND RNN 3 1 4-XYljYztPvUs.mp4 |
3.35Мб |
| 19. AIND RNN 3 1 4-XYljYztPvUs.zh-CN.vtt |
3.04Кб |
| 19. Cross-Entropy 1.html |
9.54Кб |
| 19. Cross Entropy 1-iREoPUrpXvE.en.vtt |
4.81Кб |
| 19. Cross Entropy 1-iREoPUrpXvE.mp4 |
4.22Мб |
| 19. Cross Entropy 1-iREoPUrpXvE.pt-BR.vtt |
5.00Кб |
| 19. Cross Entropy 1-iREoPUrpXvE.zh-CN.vtt |
4.11Кб |
| 19. Mini Project CNNs in Keras.html |
7.85Кб |
| 19. Using Keras for fitting.html |
8.30Кб |
| 20. AIND RNN 3 1 5-6LgdU4avFSk.en.vtt |
2.63Кб |
| 20. AIND RNN 3 1 5-6LgdU4avFSk.mp4 |
2.61Мб |
| 20. AIND RNN 3 1 5-6LgdU4avFSk.zh-CN.vtt |
2.35Кб |
| 20. Cross-Entropy 2.html |
11.83Кб |
| 20. CrossEntropy V1-1BnhC6e0TFw.en.vtt |
8.03Кб |
| 20. CrossEntropy V1-1BnhC6e0TFw.mp4 |
6.61Мб |
| 20. CrossEntropy V1-1BnhC6e0TFw.pt-BR.vtt |
7.81Кб |
| 20. CrossEntropy V1-1BnhC6e0TFw.zh-CN.vtt |
6.66Кб |
| 20. Formula For Cross 1-qvr_ego_d6w.en.vtt |
607б |
| 20. Formula For Cross 1-qvr_ego_d6w.mp4 |
2.08Мб |
| 20. Formula For Cross 1-qvr_ego_d6w.pt-BR.vtt |
719б |
| 20. Formula For Cross 1-qvr_ego_d6w.zh-CN.vtt |
545б |
| 20. Image Augmentation in Keras.html |
8.88Кб |
| 20. Image Augmentation in Keras-odStujZq3GY.en.vtt |
8.22Кб |
| 20. Image Augmentation in Keras-odStujZq3GY.mp4 |
10.26Мб |
| 20. Image Augmentation in Keras-odStujZq3GY.pt-BR.vtt |
8.49Кб |
| 20. Image Augmentation in Keras-odStujZq3GY.zh-CN.vtt |
7.02Кб |
| 20. Using a regressor as a generative model.html |
8.51Кб |
| 21. 3 2 0 Example 2-2qYjlOV4Vi0.en.vtt |
1.09Кб |
| 21. 3 2 0 Example 2-2qYjlOV4Vi0.mp4 |
1.14Мб |
| 21. 3 2 0 Example 2-2qYjlOV4Vi0.zh-CN.vtt |
1016б |
| 21. A second example.html |
8.29Кб |
| 21. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.en.vtt |
4.72Кб |
| 21. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.mp4 |
4.14Мб |
| 21. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.pt-BR.vtt |
4.54Кб |
| 21. DL 27 Multi-Class Cross Entropy 2 Fix-keDswcqkees.zh-CN.vtt |
4.01Кб |
| 21. Mini Project Image Augmentation in Keras.html |
7.94Кб |
| 21. Multi-Class Cross Entropy.html |
10.50Кб |
| 22. AIND RNN 3 2 1-ZFWOCob2gZ8.en.vtt |
2.57Кб |
| 22. AIND RNN 3 2 1-ZFWOCob2gZ8.mp4 |
2.87Мб |
| 22. AIND RNN 3 2 1-ZFWOCob2gZ8.zh-CN.vtt |
2.43Кб |
| 22. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.en.vtt |
1.62Кб |
| 22. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.mp4 |
1.49Мб |
| 22. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.pt-BR.vtt |
1.42Кб |
| 22. DL 29 Logistic Regression-Minimizing The Error Function-KayqiYijlzc.zh-CN.vtt |
1.46Кб |
| 22. Error Function-V5kkHldUlVU.en.vtt |
4.87Кб |
| 22. Error Function-V5kkHldUlVU.mp4 |
4.84Мб |
| 22. Error Function-V5kkHldUlVU.pt-BR.vtt |
5.19Кб |
| 22. Error Function-V5kkHldUlVU.zh-CN.vtt |
4.15Кб |
| 22. Groundbreaking CNN Architectures.html |
8.29Кб |
| 22. Groundbreaking CNN Architectures-ddrB-mhMfkY.en.vtt |
3.94Кб |
| 22. Groundbreaking CNN Architectures-ddrB-mhMfkY.mp4 |
8.09Мб |
| 22. Groundbreaking CNN Architectures-ddrB-mhMfkY.pt-BR.vtt |
4.26Кб |
| 22. Groundbreaking CNN Architectures-ddrB-mhMfkY.zh-CN.vtt |
3.52Кб |
| 22. Logistic Regression.html |
10.98Кб |
| 22. Setting up the second example.html |
8.31Кб |
| 23. AIND RNN 3 2 2-R4ACff0v7Vk.en.vtt |
3.82Кб |
| 23. AIND RNN 3 2 2-R4ACff0v7Vk.mp4 |
3.76Мб |
| 23. AIND RNN 3 2 2-R4ACff0v7Vk.zh-CN.vtt |
3.49Кб |
| 23. Gradient Descent.html |
17.30Кб |
| 23. Gradient Descent-rhVIF-nigrY.en.vtt |
3.85Кб |
| 23. Gradient Descent-rhVIF-nigrY.mp4 |
3.76Мб |
| 23. Gradient Descent-rhVIF-nigrY.pt-BR.vtt |
3.98Кб |
| 23. Visualizing CNNs (Part 1).html |
9.27Кб |
| 23. Visualizing CNNs-mnqS_EhEZVg.en.vtt |
3.87Кб |
| 23. Visualizing CNNs-mnqS_EhEZVg.mp4 |
9.20Мб |
| 23. Visualizing CNNs-mnqS_EhEZVg.pt-BR.vtt |
3.83Кб |
| 23. Visualizing CNNs-mnqS_EhEZVg.zh-CN.vtt |
3.33Кб |
| 23. Wrapping up the second example.html |
8.32Кб |
| 24. 3 3 Twists On Example 2 (1)-Xf1oAaTd42w.en.vtt |
5.82Кб |
| 24. 3 3 Twists On Example 2 (1)-Xf1oAaTd42w.mp4 |
5.36Мб |
| 24. 3 3 Twists On Example 2 (1)-Xf1oAaTd42w.zh-CN.vtt |
5.34Кб |
| 24. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.en.vtt |
4.27Кб |
| 24. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.mp4 |
3.20Мб |
| 24. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.pt-BR.vtt |
4.24Кб |
| 24. Gradient Descent Vs Perceptron Algorithm-uL5LuRPivTA.zh-CN.vtt |
3.60Кб |
| 24. Interesting twists on the second example.html |
8.39Кб |
| 24. Perceptron vs Gradient Descent.html |
9.68Кб |
| 24. Visualizing CNNs (Part 2).html |
13.90Кб |
| 25. 3 4 Example 4-UfOUisfQPZc.en.vtt |
5.56Кб |
| 25. 3 4 Example 4-UfOUisfQPZc.mp4 |
4.46Мб |
| 25. 3 4 Example 4-UfOUisfQPZc.zh-CN.vtt |
5.01Кб |
| 25. Continuous Perceptrons.html |
9.34Кб |
| 25. Continuous Perceptrons-07-JJ-aGEfM.en.vtt |
1.33Кб |
| 25. Continuous Perceptrons-07-JJ-aGEfM.mp4 |
1.13Мб |
| 25. Continuous Perceptrons-07-JJ-aGEfM.pt-BR.vtt |
1.31Кб |
| 25. Continuous Perceptrons-07-JJ-aGEfM.zh-CN.vtt |
1.15Кб |
| 25. Real time series example.html |
8.30Кб |
| 25. Transfer Learning.html |
18.32Кб |
| 25. Transfer Learning-LHG5FltaR6I.en.vtt |
6.00Кб |
| 25. Transfer Learning-LHG5FltaR6I.mp4 |
13.32Мб |
| 25. Transfer Learning-LHG5FltaR6I.pt-BR.vtt |
6.51Кб |
| 25. Transfer Learning-LHG5FltaR6I.zh-CN.vtt |
5.39Кб |
| 26. 3 5 Summary-imv4cLtF38o.en.vtt |
1.97Кб |
| 26. 3 5 Summary-imv4cLtF38o.mp4 |
1.68Мб |
| 26. 3 5 Summary-imv4cLtF38o.zh-CN.vtt |
1.80Кб |
| 26. Non-linear Data.html |
9.29Кб |
| 26. Non-Linear Data-F7ZiE8PQiSc.en.vtt |
633б |
| 26. Non-Linear Data-F7ZiE8PQiSc.mp4 |
2.14Мб |
| 26. Non-Linear Data-F7ZiE8PQiSc.pt-BR.vtt |
600б |
| 26. Non-Linear Data-F7ZiE8PQiSc.zh-CN.vtt |
624б |
| 26. Section summary.html |
8.28Кб |
| 26. Transfer Learning in Keras.html |
8.53Кб |
| 26. Transfer Learning in Keras-HsIAznMM1LA.en.vtt |
6.11Кб |
| 26. Transfer Learning in Keras-HsIAznMM1LA.mp4 |
12.92Мб |
| 26. Transfer Learning in Keras-HsIAznMM1LA.pt-BR.vtt |
6.77Кб |
| 26. Transfer Learning in Keras-HsIAznMM1LA.zh-CN.vtt |
5.69Кб |
| 27. Introduction to Section 4-xx4PxKWVmHo.en.vtt |
869б |
| 27. Introduction to Section 4-xx4PxKWVmHo.mp4 |
1.99Мб |
| 27. Introduction to Section 4-xx4PxKWVmHo.zh-CN.vtt |
815б |
| 27. Non-Linear Models.html |
9.30Кб |
| 27. Non-Linear Models-HWuBKCZsCo8.en.vtt |
1.30Кб |
| 27. Non-Linear Models-HWuBKCZsCo8.mp4 |
1.13Мб |
| 27. Non-Linear Models-HWuBKCZsCo8.pt-BR.vtt |
1.39Кб |
| 27. Non-Linear Models-HWuBKCZsCo8.zh-CN.vtt |
1.12Кб |
| 27. Section 4 Injecting Recursivity into Learners the Smart Way.html |
8.42Кб |
| 28. 29 Neural Network Architecture 2-FWN3Sw5fFoM.en.vtt |
3.02Кб |
| 28. 29 Neural Network Architecture 2-FWN3Sw5fFoM.mp4 |
2.83Мб |
| 28. 29 Neural Network Architecture 2-FWN3Sw5fFoM.pt-BR.vtt |
3.34Кб |
| 28. 29 Neural Network Architecture 2-FWN3Sw5fFoM.zh-CN.vtt |
2.76Кб |
| 28. Coding up a crazy recursive sequence.html |
9.19Кб |
| 28. Combinando modelos-Boy3zHVrWB4.en.vtt |
5.29Кб |
| 28. Combinando modelos-Boy3zHVrWB4.mp4 |
4.73Мб |
| 28. Combinando modelos-Boy3zHVrWB4.pt-BR.vtt |
5.29Кб |
| 28. Combinando modelos-Boy3zHVrWB4.zh-CN.vtt |
4.61Кб |
| 28. Layers-pg99FkXYK0M.en.vtt |
3.40Кб |
| 28. Layers-pg99FkXYK0M.mp4 |
3.11Мб |
| 28. Layers-pg99FkXYK0M.pt-BR.vtt |
3.29Кб |
| 28. Layers-pg99FkXYK0M.zh-CN.vtt |
3.04Кб |
| 28. Multiclass Classification-uNTtvxwfox0.en.vtt |
2.08Кб |
| 28. Multiclass Classification-uNTtvxwfox0.mp4 |
1.88Мб |
| 28. Multiclass Classification-uNTtvxwfox0.pt-BR.vtt |
2.12Кб |
| 28. Multiclass Classification-uNTtvxwfox0.zh-CN.vtt |
1.82Кб |
| 28. Neural Network Architecture.html |
13.96Кб |
| 29. DL 41 Feedforward FIX V2-hVCuvMGOfyY.en.vtt |
6.17Кб |
| 29. DL 41 Feedforward FIX V2-hVCuvMGOfyY.mp4 |
5.33Мб |
| 29. DL 41 Feedforward FIX V2-hVCuvMGOfyY.pt-BR.vtt |
6.76Кб |
| 29. DL 41 Feedforward FIX V2-hVCuvMGOfyY.zh-CN.vtt |
5.33Кб |
| 29. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.en.vtt |
1.97Кб |
| 29. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.mp4 |
1.72Мб |
| 29. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.pt-BR.vtt |
2.12Кб |
| 29. DL 42 Neural Network Error Function (1)-SC1wEW7TtKs.zh-CN.vtt |
1.69Кб |
| 29. Feedforward.html |
10.58Кб |
| 29. Flaws with the FNN approach.html |
8.36Кб |
| 29. Flaws with the FNN approach-sUXrzpAkF9A.en.vtt |
8.66Кб |
| 29. Flaws with the FNN approach-sUXrzpAkF9A.mp4 |
7.18Мб |
| 29. Flaws with the FNN approach-sUXrzpAkF9A.zh-CN.vtt |
7.88Кб |
| 2d-simplex.svg |
8.81Кб |
| 30. Backpropagation.html |
13.41Кб |
| 30. Backpropagation V2-1SmY3TZTyUk.en.vtt |
7.21Кб |
| 30. Backpropagation V2-1SmY3TZTyUk.mp4 |
6.52Мб |
| 30. Backpropagation V2-1SmY3TZTyUk.pt-BR.vtt |
7.17Кб |
| 30. Backpropagation V2-1SmY3TZTyUk.zh-CN.vtt |
6.39Кб |
| 30. Calculating The Gradient 1 -tVuZDbUrzzI.en.vtt |
3.41Кб |
| 30. Calculating The Gradient 1 -tVuZDbUrzzI.mp4 |
3.31Мб |
| 30. Calculating The Gradient 1 -tVuZDbUrzzI.pt-BR.vtt |
3.44Кб |
| 30. Calculating The Gradient 1 -tVuZDbUrzzI.zh-CN.vtt |
2.88Кб |
| 30. Chain Rule-YAhIBOnbt54.en.vtt |
1.65Кб |
| 30. Chain Rule-YAhIBOnbt54.mp4 |
1.46Мб |
| 30. Chain Rule-YAhIBOnbt54.pt-BR.vtt |
1.73Кб |
| 30. Chain Rule-YAhIBOnbt54.zh-CN.vtt |
1.42Кб |
| 30. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.en.vtt |
6.16Кб |
| 30. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.mp4 |
5.69Мб |
| 30. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.pt-BR.vtt |
6.50Кб |
| 30. DL 46 Calculating The Gradient 2 V2 (2)-7lidiTGIlN4.zh-CN.vtt |
5.05Кб |
| 30. RNN fundamental derivations.html |
8.37Кб |
| 30. RNNs 4 2 Basic RNN Derivation-Y3-YuSbhbQM.en.vtt |
11.00Кб |
| 30. RNNs 4 2 Basic RNN Derivation-Y3-YuSbhbQM.mp4 |
8.65Мб |
| 30. RNNs 4 2 Basic RNN Derivation-Y3-YuSbhbQM.zh-CN.vtt |
9.87Кб |
| 31. Formulating a Least Squares loss.html |
8.43Кб |
| 31. Keras.html |
19.43Кб |
| 31. RNNs 4 3 Formulating A Least Squares Loss-F5PVwVrEVHY.en.vtt |
3.51Кб |
| 31. RNNs 4 3 Formulating A Least Squares Loss-F5PVwVrEVHY.mp4 |
3.43Мб |
| 31. RNNs 4 3 Formulating A Least Squares Loss-F5PVwVrEVHY.zh-CN.vtt |
3.30Кб |
| 32. Mini Project Students Admissions in Keras.html |
15.19Кб |
| 32. RNNs 4 4 RNN Properties- Memory-0B8O2eNv2DY.en.vtt |
5.03Кб |
| 32. RNNs 4 4 RNN Properties- Memory-0B8O2eNv2DY.mp4 |
4.41Мб |
| 32. RNNs 4 4 RNN Properties- Memory-0B8O2eNv2DY.zh-CN.vtt |
4.47Кб |
| 32. RNNs and memory.html |
8.35Кб |
| 33. Lesson Plan Week 2.html |
9.13Кб |
| 33. RNNs 4 5 RNN Properties- Graphical Models-LON9wniFUiE.en.vtt |
2.52Кб |
| 33. RNNs 4 5 RNN Properties- Graphical Models-LON9wniFUiE.mp4 |
2.24Мб |
| 33. RNNs 4 5 RNN Properties- Graphical Models-LON9wniFUiE.zh-CN.vtt |
2.23Кб |
| 33. RNNs and graphical models.html |
8.41Кб |
| 34. RNN Technical Issues.html |
8.81Кб |
| 34. Technical Issues-6Bpu_XydW2k.en.vtt |
2.16Кб |
| 34. Technical Issues-6Bpu_XydW2k.mp4 |
1.88Мб |
| 34. Technical Issues-6Bpu_XydW2k.zh-CN.vtt |
1.93Кб |
| 34. Training Optimization.html |
9.33Кб |
| 34. Training Optimization-UiGKhx9pUYc.en.vtt |
824б |
| 34. Training Optimization-UiGKhx9pUYc.mp4 |
2.96Мб |
| 34. Training Optimization-UiGKhx9pUYc.pt-BR.vtt |
874б |
| 34. Training Optimization-UiGKhx9pUYc.zh-CN.vtt |
840б |
| 35. Batch vs Stochastic Gradient Descent.html |
9.92Кб |
| 35. Batch Vs Stochastic Gradient Descent-2p58rVgqsgo.en.vtt |
4.64Кб |
| 35. Batch Vs Stochastic Gradient Descent-2p58rVgqsgo.mp4 |
3.95Мб |
| 35. Batch Vs Stochastic Gradient Descent-2p58rVgqsgo.pt-BR.vtt |
4.63Кб |
| 35. Batch Vs Stochastic Gradient Descent-2p58rVgqsgo.zh-CN.vtt |
4.10Кб |
| 35. Section & Course Summary-gYCL4RBvzRM.en.vtt |
3.67Кб |
| 35. Section & Course Summary-gYCL4RBvzRM.mp4 |
3.02Мб |
| 35. Section & Course Summary-gYCL4RBvzRM.zh-CN.vtt |
3.37Кб |
| 35. Section and course summary .html |
8.37Кб |
| 36. Learning Rate Decay.html |
9.29Кб |
| 36. Learning Rate-TwJ8aSZoh2U.en.vtt |
1.12Кб |
| 36. Learning Rate-TwJ8aSZoh2U.mp4 |
927.05Кб |
| 36. Learning Rate-TwJ8aSZoh2U.pt-BR.vtt |
1.26Кб |
| 36. Learning Rate-TwJ8aSZoh2U.zh-CN.vtt |
1020б |
| 36. Outro.html |
8.23Кб |
| 36. Outro-LurBj_gmFwk.en.vtt |
1.06Кб |
| 36. Outro-LurBj_gmFwk.mp4 |
2.43Мб |
| 36. Outro-LurBj_gmFwk.zh-CN.vtt |
1.06Кб |
| 37. Testing.html |
9.63Кб |
| 37. Testing-EeBZpb-PSac.en.vtt |
2.41Кб |
| 37. Testing-EeBZpb-PSac.mp4 |
2.00Мб |
| 37. Testing-EeBZpb-PSac.pt-BR.vtt |
2.37Кб |
| 37. Testing-EeBZpb-PSac.zh-CN.vtt |
1.99Кб |
| 38. Overfitting and Underfitting.html |
9.38Кб |
| 38. Underfitting And Overfitting-xj4PlXMsN-Y.en.vtt |
7.49Кб |
| 38. Underfitting And Overfitting-xj4PlXMsN-Y.mp4 |
6.42Мб |
| 38. Underfitting And Overfitting-xj4PlXMsN-Y.pt-BR.vtt |
8.15Кб |
| 38. Underfitting And Overfitting-xj4PlXMsN-Y.zh-CN.vtt |
6.54Кб |
| 39. Early Stopping.html |
9.32Кб |
| 39. Model Complexity Graph-NnS0FJyVcDQ.en.vtt |
5.32Кб |
| 39. Model Complexity Graph-NnS0FJyVcDQ.mp4 |
4.90Мб |
| 39. Model Complexity Graph-NnS0FJyVcDQ.pt-BR.vtt |
5.52Кб |
| 39. Model Complexity Graph-NnS0FJyVcDQ.zh-CN.vtt |
4.65Кб |
| 40. DL 53 Q Regularization-KxROxcRsHL8.en.vtt |
1.15Кб |
| 40. DL 53 Q Regularization-KxROxcRsHL8.mp4 |
1.01Мб |
| 40. DL 53 Q Regularization-KxROxcRsHL8.pt-BR.vtt |
1.16Кб |
| 40. DL 53 Q Regularization-KxROxcRsHL8.zh-CN.vtt |
1.02Кб |
| 40. Regularization.html |
10.36Кб |
| 41. Regularization 2.html |
9.29Кб |
| 41. Regularization-ndYnUrx8xvs.en.vtt |
8.07Кб |
| 41. Regularization-ndYnUrx8xvs.mp4 |
7.57Мб |
| 41. Regularization-ndYnUrx8xvs.pt-BR.vtt |
8.78Кб |
| 41. Regularization-ndYnUrx8xvs.zh-CN.vtt |
6.96Кб |
| 42. Dropout.html |
9.81Кб |
| 42. Dropout-Ty6K6YiGdBs.en.vtt |
4.71Кб |
| 42. Dropout-Ty6K6YiGdBs.mp4 |
4.22Мб |
| 42. Dropout-Ty6K6YiGdBs.pt-BR.vtt |
4.66Кб |
| 42. Dropout-Ty6K6YiGdBs.zh-CN.vtt |
4.06Кб |
| 43. Vanishing Gradient.html |
9.31Кб |
| 43. Vanishing Gradient-W_JJm_5syFw.en.vtt |
1.46Кб |
| 43. Vanishing Gradient-W_JJm_5syFw.mp4 |
1.32Мб |
| 43. Vanishing Gradient-W_JJm_5syFw.pt-BR.vtt |
1.56Кб |
| 43. Vanishing Gradient-W_JJm_5syFw.zh-CN.vtt |
1.24Кб |
| 44. Other Activation Functions.html |
10.13Кб |
| 44. Other Activation Functions-kA-1vUt6cvQ.en.vtt |
2.68Кб |
| 44. Other Activation Functions-kA-1vUt6cvQ.mp4 |
2.30Мб |
| 44. Other Activation Functions-kA-1vUt6cvQ.pt-BR.vtt |
2.55Кб |
| 44. Other Activation Functions-kA-1vUt6cvQ.zh-CN.vtt |
2.34Кб |
| 45. Local Minima.html |
9.27Кб |
| 45. Local Minima-gF_sW_nY-xw.en.vtt |
1.14Кб |
| 45. Local Minima-gF_sW_nY-xw.mp4 |
819.86Кб |
| 45. Local Minima-gF_sW_nY-xw.pt-BR.vtt |
1.05Кб |
| 45. Local Minima-gF_sW_nY-xw.zh-CN.vtt |
1.01Кб |
| 46. Random Restart.html |
9.28Кб |
| 46. Random Restart-idyBBCzXiqg.en.vtt |
466б |
| 46. Random Restart-idyBBCzXiqg.mp4 |
394.99Кб |
| 46. Random Restart-idyBBCzXiqg.pt-BR.vtt |
478б |
| 46. Random Restart-idyBBCzXiqg.zh-CN.vtt |
419б |
| 47. Momentum.html |
9.24Кб |
| 47. Momentum-r-rYz_PEWC8.en.vtt |
2.50Кб |
| 47. Momentum-r-rYz_PEWC8.mp4 |
2.14Мб |
| 47. Momentum-r-rYz_PEWC8.pt-BR.vtt |
2.70Кб |
| 47. Momentum-r-rYz_PEWC8.zh-CN.vtt |
2.21Кб |
| 48. Optimizers in Keras.html |
10.08Кб |
| 49. Error Functions Around the World.html |
9.41Кб |
| 49. Error Functions Around the World-34AAcTECu2A.en.vtt |
1.17Кб |
| 49. Error Functions Around the World-34AAcTECu2A.mp4 |
1.73Мб |
| 49. Error Functions Around the World-34AAcTECu2A.pt-BR.vtt |
1.08Кб |
| 49. Error Functions Around the World-34AAcTECu2A.zh-CN.vtt |
1.06Кб |
| 50. Keras Lab-a50un22BsLI.en.vtt |
586б |
| 50. Keras Lab-a50un22BsLI.mp4 |
2.19Мб |
| 50. Keras Lab-a50un22BsLI.pt-BR.vtt |
574б |
| 50. Keras Lab-a50un22BsLI.zh-CN.vtt |
540б |
| 50. Mini Project Intro.html |
9.27Кб |
| 51. Mini Project IMDB Data in Keras.html |
13.11Кб |
| 52. Outro.html |
9.22Кб |
| 52. Outro-HZt6bf73bOo.en.vtt |
802б |
| 52. Outro-HZt6bf73bOo.mp4 |
3.02Мб |
| 52. Outro-HZt6bf73bOo.pt-BR.vtt |
857б |
| 52. Outro-HZt6bf73bOo.zh-CN.vtt |
731б |
| all-ranks.png |
308.47Кб |
| and-quiz.png |
265.78Кб |
| and-to-or.png |
606.14Кб |
| arpan-c-circle.png |
309.09Кб |
| arpan-c-circle.png |
309.09Кб |
| bookworm-hero2-1200x900.jpeg |
471.33Кб |
| bootstrap.min.css |
137.64Кб |
| bootstrap.min.js |
49.85Кб |
| codecogseqn-43.gif |
7.96Кб |
| codecogseqn-49.gif |
2.09Кб |
| codecogseqn-58.gif |
919б |
| codecogseqn-60-2.png |
8.94Кб |
| conv-dims.png |
28.55Кб |
| convolution-schematic.gif |
63.63Кб |
| Course Index.rar |
7.50Кб |
| cross-entropy-diagram.png |
62.67Кб |
| data.json |
38.04Кб |
| data.json |
9.21Кб |
| data.json |
159.79Кб |
| data.json |
98.41Кб |
| data.json |
115.44Кб |
| data.json |
43.75Кб |
| data.json |
21.88Кб |
| data.json |
17.77Кб |
| data.json |
26.76Кб |
| data.json |
10.34Кб |
| data.json |
5.89Кб |
| data.json |
22.73Кб |
| data.json |
9.36Кб |
| data.json |
215.16Кб |
| data.json |
23.37Кб |
| data.json |
14.00Кб |
| data.json |
26.57Кб |
| data.png |
49.54Кб |
| diagonal-line-1.png |
5.76Кб |
| diagonal-line-2.png |
6.62Кб |
| Discuss.FreeTutorials.Us.html |
165.68Кб |
| download-repo.png |
366.63Кб |
| dropout-node.jpeg |
62.69Кб |
| f3iwvmld-400x400.jpg |
26.43Кб |
| FreeCoursesOnline.Me.html |
108.30Кб |
| FreeTutorials.Eu.html |
102.23Кб |
| full-padding-no-strides-transposed.gif |
221.74Кб |
| grid-layer-1.png |
35.30Кб |
| index.html |
4.00Кб |
| index.html |
3.10Кб |
| index.html |
6.87Кб |
| index.html |
5.26Кб |
| index.html |
4.11Кб |
| index.html |
6.39Кб |
| index.html |
3.73Кб |
| index.html |
4.08Кб |
| index.html |
3.65Кб |
| index.html |
3.37Кб |
| index.html |
3.25Кб |
| index.html |
3.82Кб |
| index.html |
3.45Кб |
| index.html |
3.39Кб |
| index.html |
3.35Кб |
| index.html |
3.65Кб |
| index.html |
3.65Кб |
| jquery.mCustomScrollbar.concat.min.js |
44.41Кб |
| jquery.mCustomScrollbar.min.css |
41.83Кб |
| jquery-3.3.1.min.js |
84.89Кб |
| jupyter-logo.png |
5.78Кб |
| KaTeX_AMS-Regular.ttf |
69.75Кб |
| KaTeX_AMS-Regular.woff |
39.26Кб |
| KaTeX_AMS-Regular.woff2 |
32.43Кб |
| KaTeX_Caligraphic-Bold.ttf |
19.13Кб |
| KaTeX_Caligraphic-Bold.woff |
11.85Кб |
| KaTeX_Caligraphic-Bold.woff2 |
10.35Кб |
| KaTeX_Caligraphic-Regular.ttf |
18.52Кб |
| KaTeX_Caligraphic-Regular.woff |
11.59Кб |
| KaTeX_Caligraphic-Regular.woff2 |
10.17Кб |
| KaTeX_Fraktur-Bold.ttf |
35.13Кб |
| KaTeX_Fraktur-Bold.woff |
22.84Кб |
| KaTeX_Fraktur-Bold.woff2 |
20.01Кб |
| KaTeX_Fraktur-Regular.ttf |
33.84Кб |
| KaTeX_Fraktur-Regular.woff |
22.31Кб |
| KaTeX_Fraktur-Regular.woff2 |
19.39Кб |
| KaTeX_Main-Bold.ttf |
60.27Кб |
| KaTeX_Main-Bold.woff |
35.89Кб |
| KaTeX_Main-Bold.woff2 |
29.90Кб |
| KaTeX_Main-BoldItalic.ttf |
43.77Кб |
| KaTeX_Main-BoldItalic.woff |
25.61Кб |
| KaTeX_Main-BoldItalic.woff2 |
21.67Кб |
| KaTeX_Main-Italic.ttf |
46.83Кб |
| KaTeX_Main-Italic.woff |
26.56Кб |
| KaTeX_Main-Italic.woff2 |
22.52Кб |
| KaTeX_Main-Regular.ttf |
68.43Кб |
| KaTeX_Main-Regular.woff |
38.52Кб |
| KaTeX_Main-Regular.woff2 |
32.09Кб |
| KaTeX_Math-BoldItalic.ttf |
38.81Кб |
| KaTeX_Math-BoldItalic.woff |
22.65Кб |
| KaTeX_Math-BoldItalic.woff2 |
19.57Кб |
| KaTeX_Math-Italic.ttf |
40.48Кб |
| KaTeX_Math-Italic.woff |
23.26Кб |
| KaTeX_Math-Italic.woff2 |
19.95Кб |
| KaTeX_SansSerif-Bold.ttf |
33.23Кб |
| KaTeX_SansSerif-Bold.woff |
18.72Кб |
| KaTeX_SansSerif-Bold.woff2 |
15.62Кб |
| KaTeX_SansSerif-Italic.ttf |
30.57Кб |
| KaTeX_SansSerif-Italic.woff |
17.70Кб |
| KaTeX_SansSerif-Italic.woff2 |
14.86Кб |
| KaTeX_SansSerif-Regular.ttf |
29.45Кб |
| KaTeX_SansSerif-Regular.woff |
16.39Кб |
| KaTeX_SansSerif-Regular.woff2 |
13.70Кб |
| KaTeX_Script-Regular.ttf |
24.28Кб |
| KaTeX_Script-Regular.woff |
13.53Кб |
| KaTeX_Script-Regular.woff2 |
11.99Кб |
| KaTeX_Size1-Regular.ttf |
12.86Кб |
| KaTeX_Size1-Regular.woff |
6.82Кб |
| KaTeX_Size1-Regular.woff2 |
5.69Кб |
| KaTeX_Size2-Regular.ttf |
12.12Кб |
| KaTeX_Size2-Regular.woff |
6.53Кб |
| KaTeX_Size2-Regular.woff2 |
5.43Кб |
| KaTeX_Size3-Regular.ttf |
8.16Кб |
| KaTeX_Size3-Regular.woff |
4.66Кб |
| KaTeX_Size3-Regular.woff2 |
3.77Кб |
| KaTeX_Size4-Regular.ttf |
11.02Кб |
| KaTeX_Size4-Regular.woff |
6.30Кб |
| KaTeX_Size4-Regular.woff2 |
5.06Кб |
| KaTeX_Typewriter-Regular.ttf |
35.46Кб |
| KaTeX_Typewriter-Regular.woff |
20.43Кб |
| KaTeX_Typewriter-Regular.woff2 |
17.13Кб |
| katex.min.css |
21.56Кб |
| katex.min.js |
231.26Кб |
| layer-1-grid.png |
45.73Кб |
| layers.png |
286.10Кб |
| linear-equation.gif |
1.23Кб |
| linear-relationships.png |
112.35Кб |
| mat-headshot.png |
179.99Кб |
| mat-headshot.png |
179.99Кб |
| mat-headshot.png |
179.99Кб |
| maxpool.jpeg |
37.07Кб |
| meme.png |
209.05Кб |
| meme.png |
209.05Кб |
| mnist-012.png |
20.21Кб |
| multi-layer.png |
214.34Кб |
| nlp-m1-l4-language-model.002.png |
102.14Кб |
| nlp-m1-l4-language-model.005.png |
119.65Кб |
| nlp-m1-l4-language-model.006.png |
148.41Кб |
| nlp-m1-l4-language-model.007.png |
115.36Кб |
| nlp-m1-l4-machine-translation.002.png |
174.11Кб |
| nlp-m1-l4-machine-translation.003.png |
105.45Кб |
| nlp-m1-l4-machine-translation.004.png |
124.31Кб |
| nlp-m1-l4-machine-translation.005.png |
120.84Кб |
| nlp-m1-l4-machine-translation.006.png |
62.46Кб |
| nlp-m1-l4-machine-translation.007.png |
136.30Кб |
| nlp-m1-l4-machine-translation.008.png |
142.59Кб |
| nlp-m1-l4-machine-translation.009.png |
76.07Кб |
| nlp-m1-l4-search-and-ranking.002.png |
214.08Кб |
| nlp-m1-l4-search-and-ranking.003.png |
99.49Кб |
| nlp-m1-l4-sentiment-analysis.002.png |
93.39Кб |
| nlp-m1-l4-sentiment-analysis.003.png |
59.41Кб |
| nlp-m1-l4-sentiment-analysis.004-cropped.png |
69.94Кб |
| nlp-m1-l4-topic-modeling.002.png |
108.52Кб |
| nlp-m1-l4-topic-modeling.003.png |
218.87Кб |
| nlp-m1-l4-topic-modeling.004.png |
207.38Кб |
| nlp-m1-l4-topic-modeling.005.png |
153.18Кб |
| nlp-m1-l4-topic-modeling.006.png |
149.46Кб |
| nlp-m1-l4-topic-modeling.008.png |
137.49Кб |
| nmn.png |
54.15Кб |
| notmnist.png |
54.15Кб |
| or-quiz.png |
393.62Кб |
| perceptronquiz.png |
93.69Кб |
| plyr.css |
23.62Кб |
| plyr.polyfilled.min.js |
126.16Кб |
| points.png |
63.17Кб |
| pooling-dims.png |
29.17Кб |
| Presented By SaM.txt |
33б |
| Project Description - Machine Translation.html |
8.04Кб |
| Project Rubric - Machine Translation.html |
8.25Кб |
| regularization-quiz.png |
87.90Кб |
| relu-network.png |
31.09Кб |
| rnn9.png |
169.87Кб |
| rubric.json |
7.58Кб |
| screen-shot-2016-11-24-at-12.08.11-pm.png |
2.90Мб |
| screen-shot-2016-11-24-at-12.09.02-pm.png |
3.09Мб |
| screen-shot-2016-11-24-at-12.09.24-pm.png |
3.49Мб |
| screen-shot-2017-11-16-at-4.26.22-pm.png |
41.24Кб |
| screen-shot-2017-11-16-at-4.27.58-pm.png |
27.77Кб |
| screen-shot-2017-11-16-at-4.31.41-pm.png |
44.91Кб |
| screen-shot-2017-11-16-at-5.54.40-pm.png |
71.35Кб |
| sequence-to-sequence-embedding-encoder-decoder.png |
21.96Кб |
| sequence-to-sequence-high-level-encoder-decoder.png |
11.54Кб |
| sequence-to-sequence-unrolled-encoder-decoder.png |
22.50Кб |
| session.png |
30.85Кб |
| sigmoid-derivative.gif |
2.09Кб |
| softmax-input-output.png |
52.45Кб |
| student-acceptance.png |
20.47Кб |
| student-quiz.png |
748.98Кб |
| styles.css |
3.76Кб |
| summary.png |
93.72Кб |
| tensorflow.png |
85.28Кб |
| Torrent Downloaded From GloDls.to.txt |
84б |
| two-layer-network.png |
17.15Кб |
| udacimak.png |
461.07Кб |
| watson-logo.png |
190.30Кб |
| watson-logo.png |
190.30Кб |
| weights-0-1-2.png |
24.61Кб |
| workspaces-gpu.png |
145.50Кб |
| workspaces-jupyter.png |
83.54Кб |
| workspaces-menu.png |
93.96Кб |
| workspaces-new.png |
85.21Кб |
| workspaces-notebook.png |
142.90Кб |
| workspaces-submit.png |
146.20Кб |
| workspaces-terminal.png |
46.91Кб |
| xor.png |
214.95Кб |
| xor-quiz.png |
94.14Кб |