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| [TGx]Downloaded from torrentgalaxy.to .txt |
585B |
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| 1 |
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| 1.1 All course materials and links!.html |
114B |
| 1.1 All course materials and links (notebooks, code, data, slides) on GitHub.html |
114B |
| 1.1 All course materials and links (notebooks, code, data, slides) on GitHub.html |
119B |
| 1.1 All course materials and links (notebooks, code, data, slides) on GitHub.html |
119B |
| 1.1 All course materials and links (notebooks, code, data, slides) on GitHub.html |
114B |
| 1. Become An Alumni.html |
944B |
| 1. Course Outline.mp4 |
58.03MB |
| 1. Course Outline.srt |
7.97KB |
| 1. Introduction to Computer Vision with TensorFlow.mp4 |
75.01MB |
| 1. Introduction to Computer Vision with TensorFlow.srt |
15.00KB |
| 1. Introduction to Milestone Project 1 Food Vision Big™.mp4 |
42.32MB |
| 1. Introduction to Milestone Project 1 Food Vision Big™.srt |
9.17KB |
| 1. Introduction to neural network classification in TensorFlow.mp4 |
72.81MB |
| 1. Introduction to neural network classification in TensorFlow.srt |
12.76KB |
| 1. Introduction to Neural Network Regression with TensorFlow.mp4 |
60.06MB |
| 1. Introduction to Neural Network Regression with TensorFlow.srt |
11.41KB |
| 1. Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning.mp4 |
61.46MB |
| 1. Introduction to Transfer Learning in TensorFlow Part 2 Fine-tuning.srt |
9.78KB |
| 1. Introduction to Transfer Learning Part 3 Scaling Up.mp4 |
41.53MB |
| 1. Introduction to Transfer Learning Part 3 Scaling Up.srt |
10.12KB |
| 1. More Videos Coming Soon!.html |
41B |
| 1. More Videos Coming Soon!.html |
41B |
| 1. More Videos Coming Soon!.html |
41B |
| 1. More Videos Coming Soon!.html |
41B |
| 1. More Videos Coming Soon!.html |
41B |
| 1. Quick Note Upcoming Videos.html |
706B |
| 1. Quick Note Upcoming Videos.html |
706B |
| 1. Quick Note Upcoming Videos.html |
706B |
| 1. Quick Note Upcoming Videos.html |
706B |
| 1. Special Bonus Lecture.html |
3.65KB |
| 1. What is and why use transfer learning.mp4 |
65.81MB |
| 1. What is and why use transfer learning.srt |
15.94KB |
| 1. What is deep learning.mp4 |
34.17MB |
| 1. What is deep learning.srt |
6.80KB |
| 10 |
124.10KB |
| 10.1 car-sales-missing-data.csv |
287B |
| 10.1 httpswww.mathsisfun.comdatastandard-deviation.html.html |
116B |
| 10.2 httpsjakevdp.github.ioPythonDataScienceHandbook03.00-introduction-to-pandas.html.html |
146B |
| 10. Comparing Our Model's Results.mp4 |
143.93MB |
| 10. Comparing Our Model's Results.srt |
21.56KB |
| 10. Creating your first tensors with TensorFlow and tf.constant().mp4 |
134.83MB |
| 10. Creating your first tensors with TensorFlow and tf.constant().srt |
24.75KB |
| 10. Downloading and preparing the data for Model 1 (1 percent of training data).mp4 |
97.80MB |
| 10. Downloading and preparing the data for Model 1 (1 percent of training data).srt |
12.98KB |
| 10. Downloading a pretrained model to make and evaluate predictions with.mp4 |
78.69MB |
| 10. Downloading a pretrained model to make and evaluate predictions with.srt |
8.91KB |
| 10. Evaluating a TensorFlow model part 2 (the three datasets).mp4 |
81.56MB |
| 10. Evaluating a TensorFlow model part 2 (the three datasets).srt |
14.05KB |
| 10. Improving our non-CNN model by adding more layers.mp4 |
106.47MB |
| 10. Improving our non-CNN model by adding more layers.srt |
13.98KB |
| 10. Make our poor classification model work for a regression dataset.mp4 |
123.01MB |
| 10. Make our poor classification model work for a regression dataset.srt |
16.33KB |
| 10. Manipulating Arrays 2.mp4 |
67.91MB |
| 10. Manipulating Arrays 2.srt |
12.01KB |
| 10. Manipulating Data.mp4 |
105.00MB |
| 10. Manipulating Data.srt |
18.56KB |
| 10. Modelling - Picking the Model.mp4 |
23.24MB |
| 10. Modelling - Picking the Model.srt |
6.23KB |
| 10. Section Review.mp4 |
5.56MB |
| 10. Section Review.srt |
2.20KB |
| 10. Turning on mixed precision training with TensorFlow.mp4 |
107.71MB |
| 10. Turning on mixed precision training with TensorFlow.srt |
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| 11 |
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| 11.1 httpswww.mathsisfun.comdatastandard-deviation.html.html |
116B |
| 11.1 pandas-anatomy-of-a-dataframe.png |
333.24KB |
| 11. Breaking our CNN model down part 1 Becoming one with the data.mp4 |
90.92MB |
| 11. Breaking our CNN model down part 1 Becoming one with the data.srt |
13.00KB |
| 11. Building a data augmentation layer to use inside our model.mp4 |
117.46MB |
| 11. Building a data augmentation layer to use inside our model.srt |
16.15KB |
| 11. Creating a feature extraction model capable of using mixed precision training.mp4 |
107.92MB |
| 11. Creating a feature extraction model capable of using mixed precision training.srt |
17.41KB |
| 11. Creating tensors with TensorFlow and tf.Variable().mp4 |
70.85MB |
| 11. Creating tensors with TensorFlow and tf.Variable().srt |
9.90KB |
| 11. Evaluating a TensorFlow model part 3 (getting a model summary).mp4 |
192.79MB |
| 11. Evaluating a TensorFlow model part 3 (getting a model summary).srt |
21.53KB |
| 11. Making predictions with our trained model on 25,250 test samples.mp4 |
115.59MB |
| 11. Making predictions with our trained model on 25,250 test samples.srt |
16.24KB |
| 11. Manipulating Data 2.mp4 |
86.56MB |
| 11. Manipulating Data 2.srt |
14.82KB |
| 11. Modelling - Tuning.mp4 |
15.98MB |
| 11. Modelling - Tuning.srt |
5.09KB |
| 11. Non-linearity part 1 Straight lines and non-straight lines.mp4 |
95.62MB |
| 11. Non-linearity part 1 Straight lines and non-straight lines.srt |
13.79KB |
| 11. Standard Deviation and Variance.mp4 |
51.13MB |
| 11. Standard Deviation and Variance.srt |
9.81KB |
| 11. TensorFlow Transfer Learning Part 1 challenge, exercises & extra-curriculum.html |
2.44KB |
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| 12 |
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| 12.1 Pandas video notes.html |
185B |
| 12.2 Pandas video code.html |
191B |
| 12. Breaking our CNN model down part 2 Preparing to load our data.mp4 |
109.48MB |
| 12. Breaking our CNN model down part 2 Preparing to load our data.srt |
16.51KB |
| 12. Checking to see if our model is using mixed precision training layer by layer.mp4 |
87.67MB |
| 12. Checking to see if our model is using mixed precision training layer by layer.srt |
10.27KB |
| 12. Creating random tensors with TensorFlow.mp4 |
88.45MB |
| 12. Creating random tensors with TensorFlow.srt |
13.03KB |
| 12. Evaluating a TensorFlow model part 4 (visualising a model's layers).mp4 |
70.28MB |
| 12. Evaluating a TensorFlow model part 4 (visualising a model's layers).srt |
9.23KB |
| 12. Manipulating Data 3.mp4 |
91.07MB |
| 12. Manipulating Data 3.srt |
14.00KB |
| 12. Modelling - Comparison.mp4 |
44.86MB |
| 12. Modelling - Comparison.srt |
13.32KB |
| 12. Non-linearity part 2 Building our first neural network with non-linearity.mp4 |
59.00MB |
| 12. Non-linearity part 2 Building our first neural network with non-linearity.srt |
7.58KB |
| 12. Reshape and Transpose.mp4 |
53.57MB |
| 12. Reshape and Transpose.srt |
9.68KB |
| 12. Unravelling our test dataset for comparing ground truth labels to predictions.mp4 |
43.81MB |
| 12. Unravelling our test dataset for comparing ground truth labels to predictions.srt |
7.72KB |
| 12. Visualising what happens when images pass through our data augmentation layer.mp4 |
119.36MB |
| 12. Visualising what happens when images pass through our data augmentation layer.srt |
14.40KB |
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| 13 |
1.05MB |
| 13.1 httpswww.mathsisfun.comalgebramatrix-multiplying.html.html |
119B |
| 13. Assignment Pandas Practice.html |
2.05KB |
| 13. Breaking our CNN model down part 3 Loading our data with ImageDataGenerator.mp4 |
103.42MB |
| 13. Breaking our CNN model down part 3 Loading our data with ImageDataGenerator.srt |
13.45KB |
| 13. Building Model 1 (with a data augmentation layer and 1% of training data).mp4 |
152.95MB |
| 13. Building Model 1 (with a data augmentation layer and 1% of training data).srt |
22.42KB |
| 13. Confirming our model's predictions are in the same order as the test labels.mp4 |
50.54MB |
| 13. Confirming our model's predictions are in the same order as the test labels.srt |
6.77KB |
| 13. Dot Product vs Element Wise.mp4 |
83.80MB |
| 13. Dot Product vs Element Wise.srt |
15.89KB |
| 13. Evaluating a TensorFlow model part 5 (visualising a model's predictions).mp4 |
78.88MB |
| 13. Evaluating a TensorFlow model part 5 (visualising a model's predictions).srt |
11.92KB |
| 13. Non-linearity part 3 Upgrading our non-linear model with more layers.mp4 |
123.24MB |
| 13. Non-linearity part 3 Upgrading our non-linear model with more layers.srt |
14.34KB |
| 13. Overfitting and Underfitting Definitions.html |
1.97KB |
| 13. Shuffling the order of tensors.mp4 |
89.86MB |
| 13. Shuffling the order of tensors.srt |
12.63KB |
| 13. Training and evaluating a feature extraction model (Food Vision Big™).mp4 |
89.02MB |
| 13. Training and evaluating a feature extraction model (Food Vision Big™).srt |
14.12KB |
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| 14 |
389.08KB |
| 14.1 Course Notes.html |
108B |
| 14.2 httpscolab.research.google.com.html |
95B |
| 14. Breaking our CNN model down part 4 Building a baseline CNN model.mp4 |
85.30MB |
| 14. Breaking our CNN model down part 4 Building a baseline CNN model.srt |
11.22KB |
| 14. Building Model 2 (with a data augmentation layer and 10% of training data).mp4 |
159.77MB |
| 14. Building Model 2 (with a data augmentation layer and 10% of training data).srt |
23.45KB |
| 14. Creating a confusion matrix for our model's 101 different classes.mp4 |
156.60MB |
| 14. Creating a confusion matrix for our model's 101 different classes.srt |
17.49KB |
| 14. Creating tensors from NumPy arrays.mp4 |
101.34MB |
| 14. Creating tensors from NumPy arrays.srt |
15.03KB |
| 14. Evaluating a TensorFlow model part 6 (common regression evaluation metrics).mp4 |
70.37MB |
| 14. Evaluating a TensorFlow model part 6 (common regression evaluation metrics).srt |
11.16KB |
| 14. Exercise Nut Butter Store Sales.mp4 |
91.27MB |
| 14. Exercise Nut Butter Store Sales.srt |
17.41KB |
| 14. Experimentation.mp4 |
21.30MB |
| 14. Experimentation.srt |
5.09KB |
| 14. How To Download The Course Assignments.mp4 |
66.79MB |
| 14. How To Download The Course Assignments.srt |
11.24KB |
| 14. Introducing your Milestone Project 1 challenge build a model to beat DeepFood.mp4 |
89.12MB |
| 14. Introducing your Milestone Project 1 challenge build a model to beat DeepFood.srt |
11.24KB |
| 14. Non-linearity part 4 Modelling our non-linear data once and for all.mp4 |
96.62MB |
| 14. Non-linearity part 4 Modelling our non-linear data once and for all.srt |
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| 15 |
1.39MB |
| 15.1 CNN Explainer website.html |
102B |
| 15. Breaking our CNN model down part 5 Looking inside a Conv2D layer.mp4 |
186.04MB |
| 15. Breaking our CNN model down part 5 Looking inside a Conv2D layer.srt |
22.79KB |
| 15. Comparison Operators.mp4 |
26.38MB |
| 15. Comparison Operators.srt |
5.22KB |
| 15. Creating a ModelCheckpoint to save our model's weights during training.mp4 |
68.99MB |
| 15. Creating a ModelCheckpoint to save our model's weights during training.srt |
10.72KB |
| 15. Evaluating a TensorFlow regression model part 7 (mean absolute error).mp4 |
56.09MB |
| 15. Evaluating a TensorFlow regression model part 7 (mean absolute error).srt |
8.10KB |
| 15. Evaluating every individual class in our dataset.mp4 |
131.77MB |
| 15. Evaluating every individual class in our dataset.srt |
19.30KB |
| 15. Getting information from your tensors (tensor attributes).mp4 |
87.39MB |
| 15. Getting information from your tensors (tensor attributes).srt |
16.96KB |
| 15. Milestone Project 1 Food Vision Big™, exercises and extra-curriculum.html |
2.32KB |
| 15. Non-linearity part 5 Replicating non-linear activation functions from scratch.mp4 |
146.61MB |
| 15. Non-linearity part 5 Replicating non-linear activation functions from scratch.srt |
18.28KB |
| 15. Tools We Will Use.mp4 |
27.34MB |
| 15. Tools We Will Use.srt |
6.08KB |
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| 16 |
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| 16. Breaking our CNN model down part 6 Compiling and fitting our baseline CNN.mp4 |
77.08MB |
| 16. Breaking our CNN model down part 6 Compiling and fitting our baseline CNN.srt |
9.86KB |
| 16. Evaluating a TensorFlow regression model part 7 (mean square error).mp4 |
32.31MB |
| 16. Evaluating a TensorFlow regression model part 7 (mean square error).srt |
3.88KB |
| 16. Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).mp4 |
68.15MB |
| 16. Fitting and evaluating Model 2 (and saving its weights using ModelCheckpoint).srt |
9.85KB |
| 16. Getting great results in less time by tweaking the learning rate.mp4 |
136.78MB |
| 16. Getting great results in less time by tweaking the learning rate.srt |
19.38KB |
| 16. Indexing and expanding tensors.mp4 |
86.57MB |
| 16. Indexing and expanding tensors.srt |
16.96KB |
| 16. Optional Elements of AI.html |
975B |
| 16. Plotting our model's F1-scores for each separate class.mp4 |
77.94MB |
| 16. Plotting our model's F1-scores for each separate class.srt |
10.69KB |
| 16. Sorting Arrays.mp4 |
32.82MB |
| 16. Sorting Arrays.srt |
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498.79KB |
| 17.1 numpy-images.zip |
7.27MB |
| 17.2 NumPy Video code.html |
190B |
| 17.3 Section Notes.html |
184B |
| 17. Breaking our CNN model down part 7 Evaluating our CNN's training curves.mp4 |
106.20MB |
| 17. Breaking our CNN model down part 7 Evaluating our CNN's training curves.srt |
17.08KB |
| 17. Creating a function to load and prepare images for making predictions.mp4 |
109.54MB |
| 17. Creating a function to load and prepare images for making predictions.srt |
15.79KB |
| 17. Loading and comparing saved weights to our existing trained Model 2.mp4 |
62.67MB |
| 17. Loading and comparing saved weights to our existing trained Model 2.srt |
9.65KB |
| 17. Manipulating tensors with basic operations.mp4 |
45.22MB |
| 17. Manipulating tensors with basic operations.srt |
6.95KB |
| 17. Setting up TensorFlow modelling experiments part 1 (start with a simple model).mp4 |
127.26MB |
| 17. Setting up TensorFlow modelling experiments part 1 (start with a simple model).srt |
17.44KB |
| 17. Turn Images Into NumPy Arrays.mp4 |
85.98MB |
| 17. Turn Images Into NumPy Arrays.srt |
10.60KB |
| 17. Using the TensorFlow History object to plot a model's loss curves.mp4 |
62.12MB |
| 17. Using the TensorFlow History object to plot a model's loss curves.srt |
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| 18 |
1.20MB |
| 18. Assignment NumPy Practice.html |
2.17KB |
| 18. Breaking our CNN model down part 8 Reducing overfitting with Max Pooling.mp4 |
130.44MB |
| 18. Breaking our CNN model down part 8 Reducing overfitting with Max Pooling.srt |
19.25KB |
| 18. Making predictions on our test images and evaluating them.mp4 |
171.68MB |
| 18. Making predictions on our test images and evaluating them.srt |
23.48KB |
| 18. Matrix multiplication with tensors part 1.mp4 |
100.85MB |
| 18. Matrix multiplication with tensors part 1.srt |
15.22KB |
| 18. Preparing Model 3 (our first fine-tuned model).mp4 |
198.23MB |
| 18. Preparing Model 3 (our first fine-tuned model).srt |
25.90KB |
| 18. Setting up TensorFlow modelling experiments part 2 (increasing complexity).mp4 |
95.63MB |
| 18. Setting up TensorFlow modelling experiments part 2 (increasing complexity).srt |
15.86KB |
| 18. Using callbacks to find a model's ideal learning rate.mp4 |
155.88MB |
| 18. Using callbacks to find a model's ideal learning rate.srt |
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| 19. Breaking our CNN model down part 9 Reducing overfitting with data augmentation.mp4 |
66.08MB |
| 19. Breaking our CNN model down part 9 Reducing overfitting with data augmentation.srt |
9.39KB |
| 19. Comparing and tracking your TensorFlow modelling experiments.mp4 |
92.25MB |
| 19. Comparing and tracking your TensorFlow modelling experiments.srt |
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| 19. Discussing the benefits of finding your model's most wrong predictions.mp4 |
59.30MB |
| 19. Discussing the benefits of finding your model's most wrong predictions.srt |
9.41KB |
| 19. Fitting and evaluating Model 3 (our first fine-tuned model).mp4 |
69.16MB |
| 19. Fitting and evaluating Model 3 (our first fine-tuned model).srt |
10.61KB |
| 19. Matrix multiplication with tensors part 2.mp4 |
107.79MB |
| 19. Matrix multiplication with tensors part 2.srt |
17.35KB |
| 19. Optional Extra NumPy resources.html |
1.02KB |
| 19. Training and evaluating a model with an ideal learning rate.mp4 |
89.01MB |
| 19. Training and evaluating a model with an ideal learning rate.srt |
11.87KB |
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| 2. Downloading and preparing data for our first transfer learning model.mp4 |
132.67MB |
| 2. Downloading and preparing data for our first transfer learning model.srt |
18.11KB |
| 2. Example classification problems (and their inputs and outputs).mp4 |
50.71MB |
| 2. Example classification problems (and their inputs and outputs).srt |
9.89KB |
| 2. Getting helper functions ready and downloading data to model.mp4 |
131.54MB |
| 2. Getting helper functions ready and downloading data to model.srt |
17.73KB |
| 2. Importing a script full of helper functions (and saving lots of space).mp4 |
89.39MB |
| 2. Importing a script full of helper functions (and saving lots of space).srt |
9.77KB |
| 2. Inputs and outputs of a neural network regression model.mp4 |
57.57MB |
| 2. Inputs and outputs of a neural network regression model.srt |
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| 2. Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.mp4 |
76.65MB |
| 2. Introduction to Convolutional Neural Networks (CNNs) with TensorFlow.srt |
12.11KB |
| 2. Join Our Online Classroom!.html |
2.43KB |
| 2. LinkedIn Endorsements.html |
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| 2. Making sure we have access to the right GPU for mixed precision training.mp4 |
88.15MB |
| 2. Making sure we have access to the right GPU for mixed precision training.srt |
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| 2. Section Overview.mp4 |
13.36MB |
| 2. Section Overview.mp4 |
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| 2. Section Overview.mp4 |
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| 2. Section Overview.srt |
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| 2. Section Overview.srt |
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| 2. What is Machine Learning.mp4 |
28.31MB |
| 2. What is Machine Learning.srt |
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| 2. Why use deep learning.mp4 |
62.32MB |
| 2. Why use deep learning.srt |
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| 20. Breaking our CNN model down part 10 Visualizing our augmented data.mp4 |
157.62MB |
| 20. Breaking our CNN model down part 10 Visualizing our augmented data.srt |
21.55KB |
| 20. Comparing our model's results before and after fine-tuning.mp4 |
84.18MB |
| 20. Comparing our model's results before and after fine-tuning.srt |
13.82KB |
| 20. How to save a TensorFlow model.mp4 |
92.29MB |
| 20. How to save a TensorFlow model.srt |
11.39KB |
| 20. Introducing more classification evaluation methods.mp4 |
42.21MB |
| 20. Introducing more classification evaluation methods.srt |
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| 20. Matrix multiplication with tensors part 3.mp4 |
80.62MB |
| 20. Matrix multiplication with tensors part 3.srt |
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| 20. Writing code to uncover our model's most wrong predictions.mp4 |
109.60MB |
| 20. Writing code to uncover our model's most wrong predictions.srt |
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| 21. Breaking our CNN model down part 11 Training a CNN model on augmented data.mp4 |
94.06MB |
| 21. Breaking our CNN model down part 11 Training a CNN model on augmented data.srt |
13.58KB |
| 21. Changing the datatype of tensors.mp4 |
71.39MB |
| 21. Changing the datatype of tensors.srt |
8.64KB |
| 21. Downloading and preparing data for our biggest experiment yet (Model 4).mp4 |
56.68MB |
| 21. Downloading and preparing data for our biggest experiment yet (Model 4).srt |
8.97KB |
| 21. Finding the accuracy of our classification model.mp4 |
34.07MB |
| 21. Finding the accuracy of our classification model.srt |
5.63KB |
| 21. How to load and use a saved TensorFlow model.mp4 |
104.37MB |
| 21. How to load and use a saved TensorFlow model.srt |
12.81KB |
| 21. Plotting and visualising the samples our model got most wrong.mp4 |
125.49MB |
| 21. Plotting and visualising the samples our model got most wrong.srt |
15.45KB |
| 210 |
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| 22 |
1.17MB |
| 22. (Optional) How to save and download files from Google Colab.mp4 |
67.70MB |
| 22. (Optional) How to save and download files from Google Colab.srt |
7.79KB |
| 22. Breaking our CNN model down part 12 Discovering the power of shuffling data.mp4 |
103.86MB |
| 22. Breaking our CNN model down part 12 Discovering the power of shuffling data.srt |
14.30KB |
| 22. Creating our first confusion matrix (to see where our model is getting confused).mp4 |
65.70MB |
| 22. Creating our first confusion matrix (to see where our model is getting confused).srt |
11.54KB |
| 22. Making predictions on and plotting our own custom images.mp4 |
108.30MB |
| 22. Making predictions on and plotting our own custom images.srt |
14.61KB |
| 22. Preparing our final modelling experiment (Model 4).mp4 |
96.42MB |
| 22. Preparing our final modelling experiment (Model 4).srt |
14.88KB |
| 22. Tensor aggregation (finding the min, max, mean & more).mp4 |
89.58MB |
| 22. Tensor aggregation (finding the min, max, mean & more).srt |
12.88KB |
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498.26KB |
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773.93KB |
| 23 |
1.06MB |
| 23. Breaking our CNN model down part 13 Exploring options to improve our model.mp4 |
50.34MB |
| 23. Breaking our CNN model down part 13 Exploring options to improve our model.srt |
7.53KB |
| 23. Fine-tuning Model 4 on 100% of the training data and evaluating its results.mp4 |
96.84MB |
| 23. Fine-tuning Model 4 on 100% of the training data and evaluating its results.srt |
14.85KB |
| 23. Making our confusion matrix prettier.mp4 |
114.12MB |
| 23. Making our confusion matrix prettier.srt |
18.28KB |
| 23. Putting together what we've learned part 1 (preparing a dataset).mp4 |
143.51MB |
| 23. Putting together what we've learned part 1 (preparing a dataset).srt |
18.70KB |
| 23. Tensor troubleshooting example (updating tensor datatypes).mp4 |
69.39MB |
| 23. Tensor troubleshooting example (updating tensor datatypes).srt |
6.63KB |
| 23. Transfer Learning in TensorFlow Part 3 challenge, exercises and extra-curriculum.html |
2.28KB |
| 230 |
1.19MB |
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674.87KB |
| 24 |
1.16MB |
| 24. Comparing our modelling experiment results in TensorBoard.mp4 |
95.75MB |
| 24. Comparing our modelling experiment results in TensorBoard.srt |
15.74KB |
| 24. Downloading a custom image to make predictions on.mp4 |
53.08MB |
| 24. Downloading a custom image to make predictions on.srt |
6.93KB |
| 24. Finding the positional minimum and maximum of a tensor (argmin and argmax).mp4 |
96.50MB |
| 24. Finding the positional minimum and maximum of a tensor (argmin and argmax).srt |
12.38KB |
| 24. Putting things together with multi-class classification part 1 Getting the data.mp4 |
87.22MB |
| 24. Putting things together with multi-class classification part 1 Getting the data.srt |
13.77KB |
| 24. Putting together what we've learned part 2 (building a regression model).mp4 |
121.38MB |
| 24. Putting together what we've learned part 2 (building a regression model).srt |
17.95KB |
| 240 |
623.51KB |
| 241 |
1.13MB |
| 242 |
748.12KB |
| 25 |
1.33MB |
| 25. How to view and delete previous TensorBoard experiments.mp4 |
21.91MB |
| 25. How to view and delete previous TensorBoard experiments.srt |
2.81KB |
| 25. Multi-class classification part 2 Becoming one with the data.mp4 |
48.65MB |
| 25. Multi-class classification part 2 Becoming one with the data.srt |
9.99KB |
| 25. Putting together what we've learned part 3 (improving our regression model).mp4 |
155.11MB |
| 25. Putting together what we've learned part 3 (improving our regression model).srt |
18.80KB |
| 25. Squeezing a tensor (removing all 1-dimension axes).mp4 |
30.20MB |
| 25. Squeezing a tensor (removing all 1-dimension axes).srt |
3.84KB |
| 25. Writing a helper function to load and preprocessing custom images.mp4 |
105.15MB |
| 25. Writing a helper function to load and preprocessing custom images.srt |
13.73KB |
| 26 |
1.76MB |
| 26. Making a prediction on a custom image with our trained CNN.mp4 |
99.90MB |
| 26. Making a prediction on a custom image with our trained CNN.srt |
15.46KB |
| 26. Multi-class classification part 3 Building a multi-class classification model.mp4 |
142.80MB |
| 26. Multi-class classification part 3 Building a multi-class classification model.srt |
21.13KB |
| 26. One-hot encoding tensors.mp4 |
59.73MB |
| 26. One-hot encoding tensors.srt |
7.98KB |
| 26. Preprocessing data with feature scaling part 1 (what is feature scaling).mp4 |
92.51MB |
| 26. Preprocessing data with feature scaling part 1 (what is feature scaling).srt |
13.88KB |
| 26. Transfer Learning in TensorFlow Part 2 challenge, exercises and extra-curriculum.html |
2.64KB |
| 27 |
1.81MB |
| 27. Multi-class classification part 4 Improving performance with normalisation.mp4 |
113.41MB |
| 27. Multi-class classification part 4 Improving performance with normalisation.srt |
16.21KB |
| 27. Multi-class CNN's part 1 Becoming one with the data.mp4 |
140.19MB |
| 27. Multi-class CNN's part 1 Becoming one with the data.srt |
22.69KB |
| 27. Preprocessing data with feature scaling part 2 (normalising our data).mp4 |
97.18MB |
| 27. Preprocessing data with feature scaling part 2 (normalising our data).srt |
13.93KB |
| 27. Trying out more tensor math operations.mp4 |
55.93MB |
| 27. Trying out more tensor math operations.srt |
6.23KB |
| 28 |
1.82MB |
| 28. Exploring TensorFlow and NumPy's compatibility.mp4 |
43.75MB |
| 28. Exploring TensorFlow and NumPy's compatibility.srt |
7.11KB |
| 28. Multi-class classification part 5 Comparing normalised and non-normalised data.mp4 |
26.77MB |
| 28. Multi-class classification part 5 Comparing normalised and non-normalised data.srt |
5.44KB |
| 28. Multi-class CNN's part 2 Preparing our data (turning it into tensors).mp4 |
72.72MB |
| 28. Multi-class CNN's part 2 Preparing our data (turning it into tensors).srt |
9.95KB |
| 28. Preprocessing data with feature scaling part 3 (fitting a model on scaled data).mp4 |
75.72MB |
| 28. Preprocessing data with feature scaling part 3 (fitting a model on scaled data).srt |
10.97KB |
| 29 |
232.10KB |
| 29. Making sure our tensor operations run really fast on GPUs.mp4 |
110.91MB |
| 29. Making sure our tensor operations run really fast on GPUs.srt |
14.45KB |
| 29. Multi-class classification part 6 Finding the ideal learning rate.mp4 |
73.34MB |
| 29. Multi-class classification part 6 Finding the ideal learning rate.srt |
14.91KB |
| 29. Multi-class CNN's part 3 Building a multi-class CNN model.mp4 |
89.24MB |
| 29. Multi-class CNN's part 3 Building a multi-class CNN model.srt |
10.65KB |
| 29. TensorFlow Regression challenge, exercises & extra-curriculum.html |
1.98KB |
| 3 |
198.20KB |
| 3.1 httpsnumpy.orgdoc.html |
83B |
| 3.2 NumPy Video code.html |
190B |
| 3.3 NumPy Notes.html |
184B |
| 3. AIMachine LearningData Science.mp4 |
19.67MB |
| 3. AIMachine LearningData Science.srt |
6.45KB |
| 3. Anatomy and architecture of a neural network regression model.mp4 |
59.00MB |
| 3. Anatomy and architecture of a neural network regression model.srt |
12.25KB |
| 3. Downloading and turning our images into a TensorFlow BatchDataset.mp4 |
173.60MB |
| 3. Downloading and turning our images into a TensorFlow BatchDataset.srt |
22.01KB |
| 3. Downloading an image dataset for our first Food Vision model.mp4 |
72.94MB |
| 3. Downloading an image dataset for our first Food Vision model.srt |
10.31KB |
| 3. Downloading Workbooks and Assignments.html |
967B |
| 3. Exercise Meet The Community.html |
2.83KB |
| 3. Getting helper functions ready.mp4 |
31.09MB |
| 3. Getting helper functions ready.srt |
3.94KB |
| 3. Input and output tensors of classification problems.mp4 |
51.01MB |
| 3. Input and output tensors of classification problems.srt |
8.85KB |
| 3. Introducing Callbacks in TensorFlow and making a callback to track our models.mp4 |
94.89MB |
| 3. Introducing Callbacks in TensorFlow and making a callback to track our models.srt |
14.26KB |
| 3. Introducing Our Framework.mp4 |
11.39MB |
| 3. Introducing Our Framework.srt |
3.70KB |
| 3. NumPy Introduction.mp4 |
26.86MB |
| 3. NumPy Introduction.srt |
7.60KB |
| 3. Outlining the model we're going to build and building a ModelCheckpoint callback.mp4 |
40.61MB |
| 3. Outlining the model we're going to build and building a ModelCheckpoint callback.srt |
7.41KB |
| 3. TensorFlow Certificate.html |
385B |
| 3. What are neural networks.mp4 |
63.43MB |
| 3. What are neural networks.srt |
14.70KB |
| 30 |
467.04KB |
| 30. Multi-class classification part 7 Evaluating our model.mp4 |
119.14MB |
| 30. Multi-class classification part 7 Evaluating our model.srt |
16.96KB |
| 30. Multi-class CNN's part 4 Fitting a multi-class CNN model to the data.mp4 |
59.66MB |
| 30. Multi-class CNN's part 4 Fitting a multi-class CNN model to the data.srt |
8.96KB |
| 30. TensorFlow Fundamentals challenge, exercises & extra-curriculum.html |
1.95KB |
| 31 |
1.56MB |
| 31. Multi-class classification part 8 Creating a confusion matrix.mp4 |
40.52MB |
| 31. Multi-class classification part 8 Creating a confusion matrix.srt |
6.67KB |
| 31. Multi-class CNN's part 5 Evaluating our multi-class CNN model.mp4 |
41.05MB |
| 31. Multi-class CNN's part 5 Evaluating our multi-class CNN model.srt |
6.79KB |
| 31. Python + Machine Learning Monthly.html |
796B |
| 32 |
178.64KB |
| 32. LinkedIn Endorsements.html |
2.05KB |
| 32. Multi-class classification part 9 Visualising random model predictions.mp4 |
65.68MB |
| 32. Multi-class classification part 9 Visualising random model predictions.srt |
13.52KB |
| 32. Multi-class CNN's part 6 Trying to fix overfitting by removing layers.mp4 |
129.83MB |
| 32. Multi-class CNN's part 6 Trying to fix overfitting by removing layers.srt |
16.43KB |
| 33 |
42.12KB |
| 33. Multi-class CNN's part 7 Trying to fix overfitting with data augmentation.mp4 |
121.02MB |
| 33. Multi-class CNN's part 7 Trying to fix overfitting with data augmentation.srt |
16.32KB |
| 33. What patterns is our model learning.mp4 |
127.96MB |
| 33. What patterns is our model learning.srt |
20.83KB |
| 34 |
756.96KB |
| 34. Multi-class CNN's part 8 Things you could do to improve your CNN model.mp4 |
43.29MB |
| 34. Multi-class CNN's part 8 Things you could do to improve your CNN model.srt |
6.18KB |
| 34. TensorFlow classification challenge, exercises & extra-curriculum.html |
2.48KB |
| 35 |
523.70KB |
| 35. Multi-class CNN's part 9 Making predictions with our model on custom images.mp4 |
118.98MB |
| 35. Multi-class CNN's part 9 Making predictions with our model on custom images.srt |
11.90KB |
| 36 |
722.23KB |
| 36. Saving and loading our trained CNN model.mp4 |
69.28MB |
| 36. Saving and loading our trained CNN model.srt |
9.07KB |
| 37 |
781.82KB |
| 37. TensorFlow computer vision and CNNs challenge, exercises & extra-curriculum.html |
2.51KB |
| 38 |
1011.34KB |
| 39 |
638.66KB |
| 4 |
410.84KB |
| 4.1 10 Minutes to pandas.html |
127B |
| 4.1 6 Step Guide.html |
147B |
| 4.1 httpsteachablemachine.withgoogle.com.html |
101B |
| 4.1 Zero to Mastery TensorFlow Deep Learning on GitHub.html |
114B |
| 4.2 Intro to pandas code.html |
191B |
| 4.3 Intro to pandas notes.html |
185B |
| 4. 6 Step Machine Learning Framework.mp4 |
23.45MB |
| 4. 6 Step Machine Learning Framework.srt |
6.86KB |
| 4. All Course Resources + Notebooks.html |
1.97KB |
| 4. Becoming One With Data.mp4 |
45.61MB |
| 4. Becoming One With Data.srt |
6.72KB |
| 4. Course Review.html |
176B |
| 4. Creating a data augmentation layer to use with our model.mp4 |
40.56MB |
| 4. Creating a data augmentation layer to use with our model.srt |
6.25KB |
| 4. Creating sample regression data (so we can model it).mp4 |
90.16MB |
| 4. Creating sample regression data (so we can model it).srt |
16.12KB |
| 4. Discussing the four (actually five) modelling experiments we're running.mp4 |
15.87MB |
| 4. Discussing the four (actually five) modelling experiments we're running.srt |
3.58KB |
| 4. Exercise Machine Learning Playground.mp4 |
42.56MB |
| 4. Exercise Machine Learning Playground.srt |
8.13KB |
| 4. Exploring the TensorFlow Hub website for pretrained models.mp4 |
102.96MB |
| 4. Exploring the TensorFlow Hub website for pretrained models.srt |
14.67KB |
| 4. Introduction to TensorFlow Datasets (TFDS).mp4 |
116.84MB |
| 4. Introduction to TensorFlow Datasets (TFDS).srt |
17.62KB |
| 4. Pandas Introduction.mp4 |
27.46MB |
| 4. Pandas Introduction.srt |
6.91KB |
| 4. Quick Note Correction In Next Video.html |
1.25KB |
| 4. Typical architecture of neural network classification models with TensorFlow.mp4 |
112.73MB |
| 4. Typical architecture of neural network classification models with TensorFlow.srt |
14.61KB |
| 4. What is deep learning already being used for.mp4 |
76.21MB |
| 4. What is deep learning already being used for.srt |
13.48KB |
| 40 |
1007.62KB |
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656.03KB |
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883.01KB |
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1.29MB |
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415.21KB |
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1.06MB |
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1.88MB |
| 5 |
329.22KB |
| 5.1 pandas-anatomy-of-a-dataframe.png |
333.24KB |
| 5. Becoming One With Data Part 2.mp4 |
104.59MB |
| 5. Becoming One With Data Part 2.srt |
16.06KB |
| 5. Building and compiling a TensorFlow Hub feature extraction model.mp4 |
135.63MB |
| 5. Building and compiling a TensorFlow Hub feature extraction model.srt |
18.91KB |
| 5. Comparing the TensorFlow Keras Sequential API versus the Functional API.mp4 |
26.45MB |
| 5. Comparing the TensorFlow Keras Sequential API versus the Functional API.srt |
4.03KB |
| 5. Creating a headless EfficientNetB0 model with data augmentation built in.mp4 |
80.41MB |
| 5. Creating a headless EfficientNetB0 model with data augmentation built in.srt |
13.45KB |
| 5. Creating and viewing classification data to model.mp4 |
106.08MB |
| 5. Creating and viewing classification data to model.srt |
14.39KB |
| 5. Exploring and becoming one with the data (Food101 from TensorFlow Datasets).mp4 |
116.71MB |
| 5. Exploring and becoming one with the data (Food101 from TensorFlow Datasets).srt |
22.34KB |
| 5. How Did We Get Here.mp4 |
30.49MB |
| 5. How Did We Get Here.srt |
7.34KB |
| 5. NumPy DataTypes and Attributes.mp4 |
78.97MB |
| 5. NumPy DataTypes and Attributes.srt |
20.04KB |
| 5. Series, Data Frames and CSVs.mp4 |
95.43MB |
| 5. Series, Data Frames and CSVs.srt |
18.45KB |
| 5. The Final Challenge.html |
176B |
| 5. The major steps in modelling with TensorFlow.mp4 |
181.81MB |
| 5. The major steps in modelling with TensorFlow.srt |
25.74KB |
| 5. Types of Machine Learning Problems.mp4 |
60.46MB |
| 5. Types of Machine Learning Problems.srt |
14.46KB |
| 5. What is and why use TensorFlow.mp4 |
69.16MB |
| 5. What is and why use TensorFlow.srt |
11.74KB |
| 50 |
607.14KB |
| 51 |
1.27MB |
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1.09MB |
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1.43MB |
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410.75KB |
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468.42KB |
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529.67KB |
| 57 |
1.70MB |
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78.12KB |
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217.52KB |
| 6 |
1.45MB |
| 6.1 httpsml-playground.com#.html |
88B |
| 6. Becoming One With Data Part 3.mp4 |
39.89MB |
| 6. Becoming One With Data Part 3.srt |
6.54KB |
| 6. Blowing our previous models out of the water with transfer learning.mp4 |
99.46MB |
| 6. Blowing our previous models out of the water with transfer learning.srt |
13.66KB |
| 6. Checking the input and output shapes of our classification data.mp4 |
38.15MB |
| 6. Checking the input and output shapes of our classification data.srt |
6.57KB |
| 6. Creating a preprocessing function to prepare our data for modelling.mp4 |
132.19MB |
| 6. Creating a preprocessing function to prepare our data for modelling.srt |
18.84KB |
| 6. Creating NumPy Arrays.mp4 |
66.84MB |
| 6. Creating NumPy Arrays.srt |
12.45KB |
| 6. Creating our first model with the TensorFlow Keras Functional API.mp4 |
132.18MB |
| 6. Creating our first model with the TensorFlow Keras Functional API.srt |
15.84KB |
| 6. Data from URLs.html |
1.09KB |
| 6. Exercise YouTube Recommendation Engine.mp4 |
19.43MB |
| 6. Exercise YouTube Recommendation Engine.srt |
5.61KB |
| 6. Fitting and evaluating our biggest transfer learning model yet.mp4 |
70.15MB |
| 6. Fitting and evaluating our biggest transfer learning model yet.srt |
11.43KB |
| 6. Steps in improving a model with TensorFlow part 1.mp4 |
45.82MB |
| 6. Steps in improving a model with TensorFlow part 1.srt |
7.62KB |
| 6. Types of Data.mp4 |
29.31MB |
| 6. Types of Data.srt |
6.48KB |
| 6. What is a Tensor.mp4 |
27.58MB |
| 6. What is a Tensor.srt |
4.99KB |
| 60 |
299.35KB |
| 61 |
1.51MB |
| 62 |
1.53MB |
| 63 |
1.80MB |
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1.92MB |
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68.21KB |
| 66 |
871.47KB |
| 67 |
1.00MB |
| 68 |
1.41MB |
| 69 |
1.63MB |
| 7 |
231.26KB |
| 7. Batching and preparing our datasets (to make them run fast).mp4 |
132.24MB |
| 7. Batching and preparing our datasets (to make them run fast).srt |
19.22KB |
| 7. Building an end to end CNN Model.mp4 |
155.09MB |
| 7. Building an end to end CNN Model.srt |
26.00KB |
| 7. Building a not very good classification model with TensorFlow.mp4 |
125.29MB |
| 7. Building a not very good classification model with TensorFlow.srt |
16.03KB |
| 7. Compiling and fitting our first Functional API model.mp4 |
132.84MB |
| 7. Compiling and fitting our first Functional API model.srt |
15.76KB |
| 7. Describing Data with Pandas.mp4 |
75.65MB |
| 7. Describing Data with Pandas.srt |
14.22KB |
| 7. NumPy Random Seed.mp4 |
51.95MB |
| 7. NumPy Random Seed.srt |
10.44KB |
| 7. Plotting the loss curves of our ResNet feature extraction model.mp4 |
62.09MB |
| 7. Plotting the loss curves of our ResNet feature extraction model.srt |
10.81KB |
| 7. Steps in improving a model with TensorFlow part 2.mp4 |
90.23MB |
| 7. Steps in improving a model with TensorFlow part 2.srt |
13.12KB |
| 7. Types of Evaluation.mp4 |
17.74MB |
| 7. Types of Evaluation.srt |
4.56KB |
| 7. Types of Machine Learning.mp4 |
22.81MB |
| 7. Types of Machine Learning.srt |
5.51KB |
| 7. Unfreezing some layers in our base model to prepare for fine-tuning.mp4 |
100.07MB |
| 7. Unfreezing some layers in our base model to prepare for fine-tuning.srt |
16.60KB |
| 7. What we're going to cover throughout the course.mp4 |
29.38MB |
| 7. What we're going to cover throughout the course.srt |
7.23KB |
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141.06KB |
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592.13KB |
| 72 |
1.04MB |
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680.11KB |
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1.15MB |
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1.44MB |
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1.93MB |
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101.61KB |
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551.93KB |
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205.27KB |
| 8 |
390.43KB |
| 8.1 car-sales.csv |
369B |
| 8. Are You Getting It Yet.html |
160B |
| 8. Building and training a pre-trained EfficientNet model on our data.mp4 |
105.93MB |
| 8. Building and training a pre-trained EfficientNet model on our data.srt |
14.27KB |
| 8. Exploring what happens when we batch and prefetch our data.mp4 |
63.82MB |
| 8. Exploring what happens when we batch and prefetch our data.srt |
9.41KB |
| 8. Features In Data.mp4 |
36.78MB |
| 8. Features In Data.srt |
6.88KB |
| 8. Fine-tuning our feature extraction model and evaluating its performance.mp4 |
66.23MB |
| 8. Fine-tuning our feature extraction model and evaluating its performance.srt |
11.87KB |
| 8. Getting a feature vector from our trained model.mp4 |
147.62MB |
| 8. Getting a feature vector from our trained model.srt |
17.74KB |
| 8. How to approach this course.mp4 |
26.18MB |
| 8. How to approach this course.srt |
8.24KB |
| 8. Selecting and Viewing Data with Pandas.mp4 |
72.29MB |
| 8. Selecting and Viewing Data with Pandas.srt |
15.22KB |
| 8. Steps in improving a model with TensorFlow part 3.mp4 |
132.94MB |
| 8. Steps in improving a model with TensorFlow part 3.srt |
16.84KB |
| 8. Trying to improve our not very good classification model.mp4 |
84.29MB |
| 8. Trying to improve our not very good classification model.srt |
12.67KB |
| 8. Using a GPU to run our CNN model 5x faster.mp4 |
114.94MB |
| 8. Using a GPU to run our CNN model 5x faster.srt |
13.05KB |
| 8. Viewing Arrays and Matrices.mp4 |
70.66MB |
| 8. Viewing Arrays and Matrices.srt |
13.86KB |
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1.16MB |
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1.38MB |
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1.58MB |
| 85 |
256.63KB |
| 86 |
379.02KB |
| 87 |
393.55KB |
| 88 |
585.56KB |
| 89 |
1.11MB |
| 9 |
1.40MB |
| 9.1 httpswww.mathsisfun.comdatastandard-deviation.html.html |
116B |
| 9. Creating a function to view our model's not so good predictions.mp4 |
160.55MB |
| 9. Creating a function to view our model's not so good predictions.srt |
18.99KB |
| 9. Creating modelling callbacks for our feature extraction model.mp4 |
60.79MB |
| 9. Creating modelling callbacks for our feature extraction model.srt |
9.84KB |
| 9. Different Types of Transfer Learning.mp4 |
110.57MB |
| 9. Different Types of Transfer Learning.srt |
15.67KB |
| 9. Drilling into the concept of a feature vector (a learned representation).mp4 |
51.50MB |
| 9. Drilling into the concept of a feature vector (a learned representation).srt |
5.39KB |
| 9. Evaluating a TensorFlow model part 1 (visualise, visualise, visualise).mp4 |
66.94MB |
| 9. Evaluating a TensorFlow model part 1 (visualise, visualise, visualise).srt |
9.77KB |
| 9. Manipulating Arrays.mp4 |
80.67MB |
| 9. Manipulating Arrays.srt |
17.14KB |
| 9. Modelling - Splitting Data.mp4 |
27.55MB |
| 9. Modelling - Splitting Data.srt |
7.79KB |
| 9. Need A Refresher.html |
942B |
| 9. Saving and loading our trained model.mp4 |
57.41MB |
| 9. Saving and loading our trained model.srt |
8.98KB |
| 9. Selecting and Viewing Data with Pandas Part 2.mp4 |
106.49MB |
| 9. Selecting and Viewing Data with Pandas Part 2.srt |
18.95KB |
| 9. Trying a non-CNN model on our image data.mp4 |
100.56MB |
| 9. Trying a non-CNN model on our image data.srt |
11.63KB |
| 9. What Is Machine Learning Round 2.mp4 |
25.51MB |
| 9. What Is Machine Learning Round 2.srt |
6.25KB |
| 90 |
1.94MB |
| 91 |
1.49MB |
| 92 |
1.71MB |
| 93 |
1.75MB |
| 94 |
750.67KB |
| 95 |
951.41KB |
| 96 |
1.08MB |
| 97 |
1.77MB |
| 98 |
1.84MB |
| 99 |
141.64KB |
| TutsNode.com.txt |
61B |