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23.1 TensorFlow Hub (resource for pre-trained deep learning models and more).html 79
25.1 TensorFlow Hub (resource for pre-trained deep learning models and more).html 79
25.5 PyTorch Hub (PyTorch version of TensorFlow Hub).html 85
25.4 Papers with Code (a great resource for some of the best machine learning papers with code examples).html 88
4.1 Google Colab (our workspace for the upcoming project).html 95
5.2 Google Colab (our workspace for the upcoming project).html 95
25.3 Andrei Karpathy's talk on AI at Tesla.html 95
18.2 TensorFlow guidelines for loading all kinds of data (turning your data into Tensors).html 98
27.2 The Softmax Function (activation function we use in our model).html 107
17.1 Blog post by Rachel Thomas (of fast.ai) on how and why you should create a validation set.html 108
26.1 Keras in TensorFlow Overview Documentation.html 108
5.1 Google Colab FAQ (things you should know about Google Colab).html 110
4.2 Google Colab IO example (how to get data in and out of your Colab notebook).html 113
6.2 Google Colab IO example (how to get data in and out of your Colab notebook).html 113
11.1 Google Colab example GPU usage.html 114
12.2 Google Colab Example of GPU speed up versus CPU.html 114
18.1 Documentation for loading images in TensorFlow.html 114
6.1 Kaggle Dog Breed Identification Competition Data.html 115
4.5 Introduction to Google Colab example notebook.html 116
12.1 Introduction to Google Colab example notebook.html 116
21.1 Yann LeCun's (OG of deep learning) Tweet on Batch Sizes.html 118
4.3 Kaggle Dog Breed Identification Competition (the basis of our upcoming project).html 119
35.1 TensorFlow documentation for the unbatch() function.html 127
10.1 Loading TensorFlow 2.0 into a Colab Notebook (if it isn't the default).html 129
14.1 Documentation on how many images Google recommends for image problems.html 129
25.2 MobileNetV2 (the model we're using) on TensorFlow Hub.html 132
30.1 TensorBoard Callback Documentation.html 134
31.1 Early Stopping Callback (a way to stop your model from training when it stops improving) Documentation.html 136
27.3 MobileNetV2 (the model we're using) architecture explanation by Sik-Ho Tsang.html 163
28.1 [Article] How to choose loss & activation functions when building a deep learning model.html 169
41.1 Dog Vision Prediction Probabilities Array.html 170
27.1 Step by step breakdown of a convolutional neural network (what MobileNetV2 is made of).html 172
42.1 Dog Vision Predictions with MobileNetV2 Ready for Kaggle Submission.html 180
4.4 End-to-end Dog Vision Notebook (the project we'll be working through).html 182
43.1 End-to-end Dog Vision Notebook (with annotations).html 185
43.2 End-to-end Dog Vision Notebook (from the videos).html 191
3. Setting Up With Google.html 568
8. Setting Up Our Data 2.srt 2.2K
24. Optional How machines learn and what's going on behind the scenes.html 2.7K
1. Section Overview.srt 2.8K
44. Finishing Dog Vision Where to next.html 3.9K
31. Preventing Overfitting.srt 5.5K
29. Summarizing Our Model.srt 6.0K
12. Optional GPU and Google Colab.srt 6.0K
5. Google Colab Workspace.srt 6.3K
7. Setting Up Our Data.srt 6.4K
13. Optional Reloading Colab Notebook.srt 7.8K
23. Preparing Our Inputs and Outputs.srt 7.8K
6. Uploading Project Data.srt 8.6K
33. Evaluating Performance With TensorBoard.srt 9.6K
30. Evaluating Our Model.srt 10K
27. Building A Deep Learning Model 3.srt 11K
17. Creating Our Own Validation Set.srt 11K
20. Turning Data Into Batches.srt 12K
28. Building A Deep Learning Model 4.srt 12K
11. Using A GPU.srt 12K
26. Building A Deep Learning Model 2.srt 13K
19. Preprocess Images 2.srt 13K
18. Preprocess Images.srt 13K
16. Turning Data Labels Into Numbers.srt 14K
38. Visualizing And Evaluate Model Predictions 3.srt 14K
15. Preparing The Images.srt 15K
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22. Visualizing Our Data.srt 16K
25. Building A Deep Learning Model.srt 16K
14. Loading Our Data Labels.srt 16K
42. Submitting Model to Kaggle.srt 17K
9. Importing TensorFlow 2.srt 17K
39. Saving And Loading A Trained Model.srt 17K
36. Visualizing Model Predictions.srt 17K
35. Transform Predictions To Text.srt 18K
37. Visualizing And Evaluate Model Predictions 2.srt 18K
43. Making Predictions On Our Images.srt 19K
40. Training Model On Full Dataset.srt 19K
34. Make And Transform Predictions.srt 19K
21. Turning Data Into Batches 2.srt 20K
2. Deep Learning and Unstructured Data.srt 20K
41. Making Predictions On Test Images.srt 20K
32. Training Your Deep Neural Network.srt 23K
![[VID]](/icons/movie.gif){kind=icon}
1. Section Overview.mp4 12M
8. Setting Up Our Data 2.mp4 21M
10. Optional TensorFlow 2.0 Default Issue.mp4 28M
10. Optional TensorFlow 2.0 Default Issue.srt 28M
31. Preventing Overfitting.mp4 37M
5. Google Colab Workspace.mp4 40M
7. Setting Up Our Data.mp4 42M
29. Summarizing Our Model.mp4 45M
12. Optional GPU and Google Colab.mp4 46M
23. Preparing Our Inputs and Outputs.mp4 50M
6. Uploading Project Data.mp4 52M
17. Creating Our Own Validation Set.mp4 66M
33. Evaluating Performance With TensorBoard.mp4 74M
4. Setting Up Google Colab.mp4 74M
4. Setting Up Google Colab.srt 74M
30. Evaluating Our Model.mp4 79M
11. Using A GPU.mp4 81M
28. Building A Deep Learning Model 4.mp4 86M
20. Turning Data Into Batches.mp4 88M
13. Optional Reloading Colab Notebook.mp4 89M
18. Preprocess Images.mp4 90M
2. Deep Learning and Unstructured Data.mp4 102M
19. Preprocess Images 2.mp4 105M
26. Building A Deep Learning Model 2.mp4 106M
27. Building A Deep Learning Model 3.mp4 106M
16. Turning Data Labels Into Numbers.mp4 107M
38. Visualizing And Evaluate Model Predictions 3.mp4 113M
14. Loading Our Data Labels.mp4 115M
9. Importing TensorFlow 2.mp4 117M
43. Making Predictions On Our Images.mp4 119M
36. Visualizing Model Predictions.mp4 119M
42. Submitting Model to Kaggle.mp4 121M
25. Building A Deep Learning Model.mp4 122M
22. Visualizing Our Data.mp4 122M
39. Saving And Loading A Trained Model.mp4 127M
35. Transform Predictions To Text.mp4 130M
15. Preparing The Images.mp4 134M
40. Training Model On Full Dataset.mp4 140M
41. Making Predictions On Test Images.mp4 141M
37. Visualizing And Evaluate Model Predictions 2.mp4 144M
21. Turning Data Into Batches 2.mp4 149M
34. Make And Transform Predictions.mp4 155M
32. Training Your Deep Neural Network.mp4 167M