Connectionists: Artificial Intelligence with Python Cookbook

[Benjamin Auffarth]

Dear Colleagues
I am writing to call your attention to my book on Artificial Intelligence,
which could either serve as course material in graduate classes or as a
practical compendium of problem-solving through AI and machine learning in
the Python programming language.

The book deals with a set of topics that's much broader than most other
similarly themed books, everything is very hands-on and comes with code,
and I've attempted to get as close as possible to the state-of-the-art in
every application. I was able to incorporate a lot of my experience
since switching to the industry from neuroscience.

Here's the link:
https://www.amazon.com/Artificial-Intelligence-Python-Cookbook-algorithms/dp/1789133963

The feedback has been very positive so far (please also see the reviews). I
am including some information from the dust jacket.

*Work through practical recipes to learn how to solve complex machine
learning and deep learning problems using Python*
Key Features

   - Get up and running with artificial intelligence in no time using
   hands-on problem-solving recipes
   - Explore popular Python libraries and tools to build AI solutions for
   images, text, sounds, and images
   - Implement NLP, reinforcement learning, deep learning, GANs,
   Monte-Carlo tree search, and much more

Book Description

Artificial intelligence (AI) plays an integral role in automating
problem-solving. This involves predicting and classifying data and training
agents to execute tasks successfully. This book will teach you how to solve
complex problems with the help of independent and insightful recipes
ranging from the essentials to advanced methods that have just come out of
research.

Artificial Intelligence with Python Cookbook starts by showing you how to
set up your Python environment and taking you through the fundamentals of
data exploration. Moving ahead, you'll be able to implement heuristic
search techniques and genetic algorithms. In addition to this, you'll apply
probabilistic models, constraint optimization, and reinforcement learning.
As you advance through the book, you'll build deep learning models for
text, images, video, and audio, and then delve into algorithmic bias, style
transfer, music generation, and AI use cases in the healthcare and
insurance industries. Throughout the book, you'll learn about a variety of
tools for problem-solving and gain the knowledge needed to effectively
approach complex problems.

By the end of this book on AI, you will have the skills you need to write
AI and machine learning algorithms, test them, and deploy them for
production.
What you will learn

   - Implement data preprocessing steps and optimize model hyperparameters
   - Delve into representational learning with adversarial autoencoders
   - Use active learning, recommenders, knowledge embedding, and SAT solvers
   - Get to grips with probabilistic modeling with TensorFlow probability
   - Run object detection, text-to-speech conversion, and text and music
   generation
   - Apply swarm algorithms, multi-agent systems, and graph networks
   - Go from proof of concept to production by deploying models as
   microservices
   - Understand how to use modern AI in practice

Who this book is for

This AI machine learning book is for Python developers, data scientists,
machine learning engineers, and deep learning practitioners who want to
learn how to build artificial intelligence solutions with easy-to-follow
recipes. You'll also find this book useful if you're looking for
state-of-the-art solutions to perform different machine learning tasks in
various use cases. Basic working knowledge of the Python programming
language and machine learning concepts will help you to work with code
effectively in this book.
Table of Contents

   1. Getting Started with Artificial Intelligence in Python
   2. Advanced Topics in Supervised Machine Learning
   3. Patterns, Outliers, and Recommendations
   4. Probabilistic Modeling
   5. Heuristic Search Techniques and Logical Inference
   6. Deep Reinforcement Learning
   7. Advanced Image Applications
   8. Working with Moving Images
   9. Deep Learning in Audio and Speech
   10. Natural Language Processing
   11. Artificial Intelligence in Production



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