Zefs Guide to Deep Learning (EARLY ACCESS)
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Zefs Guide to Deep Learning (EARLY ACCESS)

About the Book

Zefs Guide to Deep Learning is a short guide to the most important concepts in deep learning, the technique at the center of the current artificial intelligence revolution. It will give you a strong understanding of the core ideas and most important methods in deep learning. This book presents the foundational concepts behind machine learning, neural networks, and the recent major advancements in architectures and training techniques in an easy to understand way. It also covers the most important applications of deep neural networks, including computer vision, natural language processing, and beyond. Your time is valuable, Zefs Guide to Deep Learning will get you up to speed in around only 100 pages!

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Early Access

Right now I invite you to get early access to the in-progress version of the book at a much lower price than the final release. You will receive updates as I make progress and the final version when it's released. You will also have a chance to offer feedback to make this the best book possible. Purchasing the early access version of the book is a great way to support my project, so please check it out!

The Zefs Guides series

Zefs Guide to Deep Learning is the first book in the Zefs Guides series on deep learning and its applications. It forms the ground knowledge for the other books in the series:

  • Zefs Guide to Computer Vision
  • Zefs Guide to Natural Language Processing
  • Zefs Guide to Transformers

Zefs Guides are designed to help the beginner quickly get up to speed on topics in machine learning and data science and to help the experienced practitioner push their conceptual understanding even further. They are short and to the point, covering the most important topics and ideas. They're for you if you are a job seeker looking for a role in ML/AI/DS, a student studying for exams, an experienced data person dusting off your old knowledge, or an executive seeking a better understanding of some of today's most important technologies.

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  • Categories

    • Computers and Programming
    • Artificial Intelligence
    • Machine Learning
    • Data Science
    • Computer Science
    • Linguistics
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About the Author

Roy Keyes
Roy Keyes

Roy Keyes has worked in data science since 2012, building and leading teams at multiple tech startups as well as consulting for clients across a wide range of industries. Prior to data science, he received a PhD in computational physics, focusing on medical applications.

You can find his website and blog at roycoding.com.

Roy Keyes

Episode 207

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Table of Contents

  • 1 This is an Early Access Pre-release version of Zefs Guide to Deep Learning
  • 2 Introduction
    • Why deep learning?
    • Why this book?
    • What does this book cover and not cover?
    • How to use this book
  • 3 Machine Learning
    • What is machine learning?
    • Types of machine learning tasks and solutions
    • An example task
    • Formulating machine learning problems
    • Data sets and features
    • Measuring performance
    • Model selection
    • Model training
    • Productionization
    • Common issues
    • Common machine learning models
    • From “traditional” ML to deep learning
    • References
  • 4 Neural Networks
    • What is a neural network?
    • What are some tasks that neural networks can accomplish?
    • The building blocks of neural networks
    • What does a neural network do?
    • From basic neural networks to deep learning
    • Resources
  • 5 The rise of deep learning
    • Moving to deep neural networks
    • Where are we now with deep learning?
  • 6 Computer vision and convolutional neural networks
    • Computers and images
    • What’s hard about computer vision tasks?
    • Convolutional neural networks
    • Some important CNN model architectures for computer vision tasks
    • Common CNN techniques
    • Summary and resources
  • 7 Natural language processing and sequential data techniques
    • Text, natural language, and sequential data
    • Making a neural network remember
    • Creating context with embeddings
    • Architectures for sequential tasks
    • Combining vision and language
  • 8 Advanced techniques and practical considerations
    • Computational graphs
    • Self-supervised learning
    • Math topics related to deep learning
    • Machine learning operations
  • Notes

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