Deconstructing Deep Learning

on YouTube ↗35m

" summary

Covers concrete implementations: a CNN built in Keras/TensorFlow runnable in Google Colab, with five hidden layers, 32 convolutional filters, max pooling and 25% dropout, trained in batches of 256 to about 90% accuracy on Fashion‑MNIST; an LSTM with 32‑dim word embeddings, trained three epochs on IMDB reviews, reporting ~87% accuracy. Explains backprop/gradient descent and shows feature maps, misclassified examples, and practical preprocessing steps that changed results.

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" chapters (35)

1. 0:00Start
2. 0:26Agenda
3. 1:18Deep Learning Biologically inspired machine learning with multi
4. 2:46Artificial Neural Network Structur
5. 3:51Deep Artificial Neural Network
6. 4:10Artificial Neural Network Node
7. 5:12Activation Functions
8. 7:00Training an Artificial Neural Netwo
9. 9:05Updating Weights via Gradient De
10. 10:22Deep Learning 101 Summary
11. 11:05How computers «see images
12. 13:25Another Approach: Convolutional Fi
13. 14:18Applying Convolutional Filters
14. 15:34Applying ReLU Activation Functio
15. 16:02Dimensionality Reduction via Pooli
16. 16:58Feature Identification and Dimensionality
17. 17:37Use Case: Fashion MNIST
18. 18:03CNN Implementation
19. 18:44CNN Architecture Overview
20. 22:14Training and Testing the CNN
21. 22:39CNN Test Results
22. 23:09Some examples of incorrectly classified
23. 23:33CNN Summary
24. 24:46Recurrent Neural Networks to the Re
25. 25:15Unrolling a Recurrent Neural Netw
26. 25:54Long Short-Term Memory (LSTM) Net
27. 26:11A LSTM Node
28. 26:48Use Case : IMDB Reviews
29. 27:21Example Positive Review
30. 27:35Pre-Processing Dataset
31. 28:12Word Embedding for NLP
32. 29:00LSTM Implementation
33. 30:45Training and Testing the LSTM
34. 31:22LSTM Test Results
35. 32:55Example of an incorrectly classified re

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