Notebooks — Grokking Deep Learning by Andrew W. Trask
- Grokking Deep Learning was written to help give you a foundation in deep learning so that you can master a major deep learning framework.
- Requires no math background beyond basic arithmetic.
- Doesn't rely on a high-level library that might hide what's going on.
- Anyone can read this book and understand how deep learning really works.
- You won't just read the theory, you'll discover it yourself.
(You can Buy the Book from [Manning Publications](https: //www.manning.com/books/grokking-deep-learning) or Amazon).
Roadmap
"Grokking Deep Learning" has 16 Chapters:
- Introducing Deep Learning: Why you should Learn It?
- fundamental Concepts: How Do Machines Learn?
- Introduction to Neural Learning: Forward Propagation
- Introduction to Neural Learning: Gradient Descent
- Learning Multiple Weights at a Time: Generalizing Gradient Descent
- Building your first deep neural network: Introduction to Backpropagation
- How to Picture Neural Networks: In your Head & on Paper
- Learning Signal & Ignoring Noise: Introduction to Regularization & Batching
- Modeling Probabilities & Non-Linearities: Activation Functions
- Neural Learning about Edges & Corners: Introduction to Convolutional Neural Networks
- Neural Networks that Understand Language: King - Man + Woman == ?
- Neural Networks that write like Shakespeare: Recurrent Layers for Variable Length Data
- Introducing Automatic Optimization: Let's build a deep learning framework
- Learning to Write like Shakespeare: Long Short-term Memory
- Deep Learning on Unseen Data: Introducing Federated Learning
- Where to Go from Here: A brief Guide
Missing Notebooks are mostly based on original content from the Book.