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Dive into Deep Learning - Julia

Preliminaries

1. Data Manipulation
2. Data Preprocessing
3. Linear Algebra
4. Calculus
5. Automatic Differentiation
6. Probability and Statistics

Linear Neural Networks for Regression

7. Linear Regression
8. Concise Implementation of Linear Regression

Linear Neural Networks for Classification

9. The Image Classification Dataset
10. Concise Implementation of Softmax Regression

Multilayer Perceptrons

11. Multilayer Perceptrons
12. Implementation of Multilayer Perceptrons
13. Numerical Stability and Initialization
14. Dropout

Builders’ Guide

15. Parameter Management
16. File I/O
17. GPUs

Recurrent Neural Networks

18. Working with Sequences
19. Concise Implementation of Recurrent Neural Networks

Modern Recurrent Neural Networks

20. Long Short-Term Memory (LSTM)
21. Gated Recurrent Units (GRU)
22. Deep Recurrent Neural Networks

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By Nero Blackstone

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