November 29, 2020

531 words 3 mins read



Artificial Intelligence with Python Cookbook, published by Packt

repo name PacktPublishing/Artificial-Intelligence-with-Python-Cookbook
repo link
language Jupyter Notebook
size (curr.) 14529 kB
stars (curr.) 21
created 2020-01-06
license MIT License

Artificial Intelligence with Python Cookbook

This is the code repository for Artificial Intelligence with Python Cookbook, published by Packt.

Practical recipes for next-generation deep learning and neural networks using TensorFlow and PyTorch

What is this book about?

With artificial intelligence (AI) systems, we can develop goal-driven agents to automate problem-solving. This involves predicting and classifying the available data and training agents to execute tasks successfully. This book will help you to solve complex AI problems using practical recipes.

This book covers the following exciting features:

  • Implement data preprocessing steps and optimize model hyperparameters
  • Work with large amounts of data using distributed and parallel computing techniques
  • Get to grips with representational learning from images using InfoGAN
  • Delve into deep probabilistic modeling with a Bayesian network
  • Create your own artwork using adversarial neural networks

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

Instructions and Navigations

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

The code will look like the following:

from sklearn.datasets import fetch_openml
data = fetch_openml(data_id=42165, as_frame=True)

Following is what you need for this book: This AI book is for Python developers, data scientists, machine learning developers, and deep learning practitioners who want to learn how to build artificial intelligence solutions with easy-to-follow recipes. If you are looking for state-of-the-art solutions to perform different machine learning tasks in various use cases, this book is for you. Basic working knowledge of Python programming language and machine learning concepts will help you to work with the code.

With the following software and hardware list you can run all code files present in the book (Chapter 1-11).

Software and Hardware List

Chapter Software required OS required
1 Python 3.6 or later Windows, Mac OS X, and Linux (Any)
2 TensorFlow 2.0 or later Windows, Mac OS X, and Linux (Any)
3 PyTorch 1.6 or later Windows, Mac OS X, and Linux (Any)
4 Pandas 1.0 or later Windows, Mac OS X, and Linux (Any)
5 Scikit-learn 0.22.0 or later Windows, Mac OS X, and Linux (Any)

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

Get to Know the Author

Ben Auffarth is a full-stack data scientist with more than 15 years of work experience. With a background and Ph.D. in computational and cognitive neuroscience, he has designed and conducted wet lab experiments on cell cultures, analyzed experiments with terabytes of data, run brain models on IBM supercomputers with up to 64k cores, built production systems processing hundreds of thousands of transactions per day, and trained neural networks on millions of text documents. He resides in West London with his family, where you might find him in a playground with his young son. He co-founded and is the former president of Data Science Speakers, London.

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