This is the code repository for Deep Learning with fastai Cookbook, published by Packt.

Leverage the easy-to-use fastai framework to unlock the power of deep learning

fastai is an easy-to-use deep learning framework built on top of PyTorch that lets you rapidly create complete deep learning solutions with as few as 10 lines of code. Both predominant low-level deep learning frameworks, TensorFlow and PyTorch, require a lot of code, even for straightforward applications. In contrast, fastai handles the messy details for you and lets you focus on applying deep learning to actually solve problems.

This book covers the following exciting features:

• Prepare real-world raw datasets to train fastai deep learning models
• Train fastai deep learning models using text and tabular data
• Create recommender systems with fastai
• Find out how to assess whether fastai is a good fit for a given problem
• Deploy fastai deep learning models in web applications

If you feel this book is for you, get your copy today!

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

for(var i = 0; i < relationship_list.length; i++) {
var opt = relationship_list[i];
select_relationship.innerHTML += "<option value=\""
+ opt + "\">" + opt + "</option>";

Following is what you need for this book: This book is for data scientists, machine learning developers, and deep learning enthusiasts looking to explore the fastai framework using a recipe-based approach. Working knowledge of the Python programming language and machine learning basics is strongly recommended to get the most out of this deep learning book.

With the following software and hardware list you can run all code files present in the book (Chapter 1-8).

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

• Mastering spaCy [Packt] [Amazon]

• Automated Machine Learning [Packt] [Amazon]

Mark Ryan is a machine learning practitioner and technology manager who is passionate about delivering end-to-end deep learning applications that solve real-world problems. Mark has worked on deep learning projects that incorporate a variety of related technologies, including Rasa chatbots, web applications, and messenger platforms. As a strong believer in democratizing technology, Mark advocates for Keras and fastai as accessible frameworks that open up deep learning to non-specialists. Mark has a degree in computer science from the University of Waterloo and a Master of Science degree in computer science from the University of Toronto.