Our popular event StateoftheArt() is back again this coming January. This time around we're proud to announce that we will open with an exciting discussion with Dr. Sebastian Raschka on generative AI and LLMS and an open Q&A for a chance to to learn more about the future of AI and deep learning. Certificates will be provided to those who attend this section. Afterwards, we deep-dive into exciting ways deep learning is applied across a wide variety of industries with leaders from Home Depot, Momentive, & Twilio. Following that is "The Economy of the Future" where you can tune in to an economist and learn their perspective on AI/ML.

Reinforcement learning (RL) has received a massive boost in attention recently. Thanks to impressive projects such as DeepMind's AlphaGo and AlphaGo Zero, which beat the world's best players in the strategy board game "Go", RL has garnered extensive news coverage. Just recently, RL was used to compete with the world's top e-sports players in the real-time strategy video game StarCraft II. Python Machine Learning, Third Edition covers the essential concepts of RL, starting from its foundations, and how RL can support decision making in complex environments. The book discusses agent-environment interactions and Markov decision processes (MDP), and considers three main approaches for solving RL problems: dynamic programming, MC learning, and TD learning. It discusses how the dynamic programming algorithm assumes that the full knowledge of environment dynamics is available, an assumption that is not typically true for most real-world problems.

I had been looking for a good book to recommend to my "Introduction to Data Science" classes at UCLA as a text to use once my class completes … sort of the next step after learning the basics. That's why I was looking forward to reviewing the new 3rd edition of the widely acclaimed title "Python Machine Learning" by Sebastian Raschka, Vahid Mirjalili. The book is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a useful resource you'll keep coming back to as you fill up your data science toolbox. I knew I was going to like it the minute I started thumbing through the pages and saw some mathematics.

The first GANs paper had just come out two years before we started working on the second edition, but we weren't sure of its relevance. However, GANs have evolved into one of the hottest and most widely used deep learning techniques. People use them for creating artwork, colorizing and improving the quality of photos, and to recreate old video game textures in higher resolutions. It goes without saying that an introduction to GANs was long overdue. Another important machine learning topic not included in previous editions is reinforcement learning, which has received a massive boost in attention recently. Thanks to impressive projects such as DeepMind's AlphaGo and AlphaGo Zero, reinforcement learning has received extensive news coverage.

I bought the first version of this book, and now also the second. The new version is very comprehensive. If you are using Python - it's almost a reference. I also like the emphasis on neural networks (and TensorFlow) - which (in my view) is where the Python community is heading. I am also planning to use this book in my teaching at Oxford University. The data pre-processing sections are also good. I found the sequence flow slightly unusual - but for an expert level audience, it's not a major issue.

I certainly can't speak about all books on the market. However, since the first edition was released, I engaged in countless discussions with my readers, to help them with particular questions and to get their opinion on the parts they found unclear or topics they wish I had covered. The connection between theory and praxis in particular was what readers found most helpful and somewhat lacking from other introductory texts (which, I heard, were either too theoretical or too practical). This constructive feedback has been invaluable for the second edition, helping me to focus on those parts that were still left unclear. In a nutshell, the second edition of Python Machine Learning provides a healthy mix of theory and practical examples that most people found so helpful in the first edition, and the second edition adds on top of it with many refinements and additional topics based on the large corpus of invaluable reader feedback.

First off, I would like to thank Sebastian Raschka, and Chris Wagner for providing the text and code that proved essential for writing this blog. For some time now, I have been wanting to replace simply pickling my sklearn pipelines. Pickle is incredibly convenient, but can be easy to corrupt, is not very transparent, and has compatibility issues. The latter has been quite a thorn in my side for several projects, and I stumbled upon it again while working on my own small text mining framework. Persistence is imperative when deploying a pipeline to a practical application like demo. Each piece of new data needs to be constructed in exactly the same vector size as it was offered in during development.

Machine learning is eating the software world, and now deep learning is extending machine learning. Thoroughly updated using the latest Python open source libraries, this book offers the practical knowledge and techniques you need to create and contribute to machine learning, deep learning, and modern data analysis. Fully extended and modernized, Python Machine Learning Second Edition now includes the popular TensorFlow deep learning library. The scikit-learn code has also been fully updated to include recent improvements and additions to this versatile machine learning library. Sebastian Raschka and Vahid Mirjalili's unique insight and expertise introduce you to machine learning and deep learning algorithms from scratch, and show you how to apply them to practical industry challenges using realistic and interesting examples.

Sebastian Raschka's new book, Python Machine Learning, has just been released. I got a chance to read a review copy and it's just as I expected - really great! It's well organized, super easy to follow, and it not only offers a good foundation for smart, non-experts, practitioners will get some ideas and learn new tricks here as well.