product iamge

Grokking Deep Learning

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Download
Search on Amazon

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

Artificial Intelligence is the most exciting technology of the century, and Deep Learning is, quite literally, the "brain" behind the world's smartest Artificial Intelligence systems out there. Grokking Deep Learning is the perfect place to begin the deep learning journey. Rather than just learning the "black box" API of some library or framework, readers will actually understand how to build these algorithms completely from scratch. Want to dig even deeper into Deep Learning? Be sure to check out our video course Grokking Deep Learning in Motion, available exclusively at Manning.com (https://www.manning.com/livevideo/​grokking-deep-learning-in-motion)!

Author(s): Andrew W. Trask
Edition: 1
Publisher: Manning Publications
Year: 2019

Language: English
Commentary: True PDF
Pages: 325
City: Shelter Island, NY
Tags: Machine Learning; Neural Networks; Deep Learning; Natural Language Processing; Unsupervised Learning; Supervised Learning; Python; Convolutional Neural Networks; Recurrent Neural Networks; Batch Processing; Backpropagation; Gradient Descent; Regularization; Natural Language Understanding; Long Short-Term Memory; PyTorch; Overfitting; Parametric Learning; Nonparametric Learning; Activation Functions; Federated Learning

1. Introducing Deep Learning: Why You Should Learn It
2. Fundamental Concepts: How Do Machines Learn
3. Introduction To Neural Prediction: Forward Propagation
4. Introduction To Neural Learning: Gradient Descent
5. Learning Multiple Weights At A Time: Generalizing Gradient Descent
6. Building Your First Deep Neural Network: Introduction To Backpropagation
7. How To Picture Neural Networks: In Your Head And On Paper
8. Learning Signal And Ignoring Noise: Introduction To Regularization And Batching
9. Modeling Probabilities And Nonlinearities: Activation Functions
10. Neural Learning About Edges And Corners: Intro To Convolutional Neural Networks
11. Neural Networks That Understand Language: King
12. Neural Networks That Write Like Shakespeare: Recurrent Layers For Variable-Length Data
13. Introducing Automatic Optimization: Let
14. Learning To Write Like Shakespeare: Long Short-Term Memory
15. Deep Learning On Unseen Data: Introducing Federated Learning
16. Where To Go From Here: A Brief Guide