Deep Learning and Neural Networks

Classifications / Machine Learning / Deep Learning and Neural Networks

TRACK

Neural network training, deep architectures, representation learning, and deployment patterns.

32 lessons

LESSONS

1. 001
   Perceptron Foundations

   30 min

   [TODO]
2. 002
   Activation Functions and Nonlinearity

   30 min

   [TODO]
3. 003
   Feedforward Propagation and Layered Representation

   30 min

   [TODO]
4. 004
   Loss Functions and Error Signals

   30 min

   [TODO]
5. 005
   Chain Rule & Computational Graphs

   30 min

   [TODO]
6. 006
   Backpropagation Algorithm - Step by Step

   30 min

   [TODO]
7. 007
   Implementing Backpropagation from Scratch

   30 min

   [TODO]
8. 008
   Gradient Checking & Debugging

   30 min

   [TODO]
9. 009
   SGD Variants - Momentum, RMSprop, Adam

   30 min

   [TODO]
10. 010
    Learning Rate Schedules

    30 min

    [TODO]
11. 011
    Weight Initialization

    30 min

    [TODO]
12. 012
    Batch Normalization

    30 min

    [TODO]
13. 013
    Dropout & Regularization Techniques

    30 min

    [TODO]
14. 014
    Data Augmentation Strategies

    30 min

    [TODO]
15. 015
    Introduction to PyTorch

    30 min

    [TODO]
16. 016
    Building Production Neural Networks

    30 min

    [TODO]
17. 017
    Convolution Operation

    30 min

    [TODO]
18. 018
    Pooling and CNN Architecture

    30 min

    [TODO]
19. 019
    Classic CNN Architectures

    30 min

    [TODO]
20. 020
    Building CNNs in PyTorch

    30 min

    [TODO]
21. 021
    RNN Fundamentals

    30 min

    [TODO]
22. 022
    LSTM and GRU

    30 min

    [TODO]
23. 023
    Sequence-to-Sequence Models

    30 min

    [TODO]
24. 024
    Text Generation with RNNs

    30 min

    [TODO]
25. 025
    Transfer Learning Fundamentals

    30 min

    [TODO]
26. 026
    Fine-Tuning Pre-trained Models

    30 min

    [TODO]
27. 027
    Embeddings and Feature Extraction

    30 min

    [TODO]
28. 028
    Domain Adaptation and Few-Shot Learning

    30 min

    [TODO]
29. 029
    Model Optimization for Production

    30 min

    [TODO]
30. 030
    Serving Models with APIs

    30 min

    [TODO]
31. 031
    Containerization with Docker

    30 min

    [TODO]
32. 032
    Cloud Deployment

    30 min

    [TODO]

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