"Neural Networks From Scratch" is a book intended to teach you how to build neural networks on your own, without any libraries, so you can better understand deep learning and how all of the elements work. This is so you can go out and do new/novel things with deep learning as well as to become more successful with even more basic models.
This book is to accompany the usual free tutorial videos and sample code from youtube.com/sentdex. This topic is one that warrants multiple mediums and sittings. Having something like a hard copy that you can make notes in, or access without your computer/offline is extremely helpful. All of this plus the ability for backers to highlight and post comments directly in the text should make learning the subject matter even easier.
Author(s): Harrison Kinsley, Daniel Kukieła
Edition: 1
Year: 2020
Language: English
Pages: 658
Tags: Programming, Neural Networks, AI, Artificial Intelligence, Python, Deep Learning
NNFS Chapter 1 - Introducing Neural Networks
NNFS Chapter 2 - Coding Our First Neurons
NNFS Chapter 3 - Adding Layers
NNFS Chapter 4 - Activation Functions
NNFS Chapter 5 - Loss
NNFS Chapter 6 - Introducing Optimization
NNFS Chapter 7 - Derivatives
NNFS Chapter 8 - Gradients, Partial Derivatives, and the Chain Rule
NNFS Chapter 9 - Backpropagation
NNFS Chapter 10 - Optimizers
NNFS Chapter 11 - Testing Data
NNFS Chapter 12 - Validation Data
NNFS Chapter 13 - Training Dataset
NNFS Chapter 14 - L1 and L2 Regularization
NNFS Chapter 15 - Dropout
NNFS Chapter 16 - Binary Logistic Regression
NNFS Chapter 17 - Regression
NNFS Chapter 18 - Model Object
NNFS Chapter 19 - A Real Dataset
NNFS Chapter 20 - Model Evaluation
NNFS Chapter 21 - Saving and Loading Model Information
NNFS Chapter 22 - Model Predicting_Inference
