Course Notes: Neural Networks and Deep Learning

Course notes aggregated from Roger Grosse and Jimmy Ba's Neural Networks and Deep Learning Class at the University of Toronto.

Table of Contents

1. Introduction

   • Introduction to the course content and foundational concepts.

2. Linear_Regression

   • Introduction to linear regression.
   • Mathematical foundations and applications.

3. Linear_Classifiers

   • Overview of linear classifiers.
   • Perceptron, support vector machines, and logistic regression.

4. Training_a_Classifier

   • Techniques for training classifiers.
   • Loss functions, gradient descent, and overfitting.

5. Multilayer_Perceptrons

   • Detailed explanation of multilayer perceptrons (MLPs).
   • Activation functions, architecture, and training.

6. Backpropagation

   • Understanding backpropagation.
   • Derivation and practical implementation in neural networks.

7. Distributed_Representations

   • Concept of distributed representations in neural networks.
   • Word embeddings and feature learning.

8. Automatic_Differentiation

   • Overview of automatic differentiation.
   • Techniques and applications in deep learning.

9. Optimization

   • Methods for optimization in machine learning.
   • Gradient descent variants, and other optimization algorithms.

10. Convolutional_Networks

    • Introduction to convolutional neural networks (CNNs).
    • Architectures, convolution operations, and applications.

11. Image_Classification

    • Techniques and models for image classification.
    • CNNs, data augmentation, and transfer learning.

12. Generalization

    • Concepts of generalization in machine learning models.
    • Bias-variance tradeoff and regularization techniques.

13. Recurrent_Neural_Nets

    • Recurrent neural networks (RNNs) and their applications.
    • Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU).

14. Exploding_and_Vanishing_Gradients

    • Challenges of training deep networks.
    • Techniques to address exploding and vanishing gradients.

15. Autoregressive_and_Reversible_Models

    • Overview of autoregressive and reversible models.
    • Applications in sequence modeling and generative models.