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Book Review: Python Distilled

Python Distilled by David M. Beazley is a pragmatic book that presents some of the most important topics about the Python programming language in a concise form, designed to make it easier to find out the most relevant information bits in a context where resources abound and frequently are just too overwhelming.

Book Structure and Contents

The book is organized into 10 chapters. It starts off with the basics, such as variables, data types, operators, expressions, control flow, and looping. Compared to other books I have read recently, it uses a smaller font size, which is quite readable to me, but it may be less comfortable for some readers. On the other hand, it contributes to making it a lighter and less thick book, compared with other programming manuals. The book uses no syntax highlighting, but code samples are short enough that actually this does not become a problem at all.

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Book Review: Foundations of Deep Reinforcement Learning, by Laura Graesser and Wah Loon Keng

Deep Reinforcement Learning is a somewhat new field within Machine Learning or Artificial Intelligence (you may pick your favorite term between these two, even if they’re not strictly the same), which combines Deep Learning and Reinforcement Learning and is based on the general idea that an agent can learn by observing its actions and their consequences. No, it is not a return to John B. Watson and B. F. Skinner’s behavioral psychology. We are talking, instead, about a set of pretty advanced machine learning algorithms that, when properly mastered, allow computers to achieve remarkable results in some complex tasks. That’s what this book is about, so let’s dive in…

Book Structure and Contents

Foundations of Deep Reinforcement Learning - Theory and Practice in Python begins with a brief preliminary chapter, which serves to introduce a few concepts and terms that will be used throughout all the other chapters: agent, state, action, objective, reward, reinforcement, policy, value function, model, trajectory, transition.

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Comprehensive Review of Haiku R1/beta2

After about 20 months of hard work, the Haiku team has finally released, a few days ago, the second beta version of Haiku, the BeOS-inspired open-source operating system that aims to offer a fast, simple to use, and powerful alternative for personal computing. This time, I am particularly happy, even a bit proud of myself, because I have also been contributing with Portuguese translations for the user interface, and this is the first beta that includes those translations. So, let's celebrate!

I first wrote about Haiku back in 2018, right after the first Haiku beta was released. As an old-time BeOS user, I had been waiting for that moment. You can read my review of Haiku R1/beta1 in case you're curious (note: this is an external link to my blog since at the time I didn't publish at Dzone yet). So, today, I will write a few paragraphs about some things that have changed and share with you some of my impressions on what there's to love on this new operating system. And, just because it can be done and it's more fun, I will be writing, editing, and publishing this article just using Haiku R1/beta2. I will include a brief note explaining what software I used and if there were any difficulties.

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Book Review: Machine Learning With Python for Everyone by Mark E. Fenner

Machine learning, one of the hottest tech topics of today, is being used more and more. Sometimes it's the best tool for the job, other times a buzzword that is mainly used as a way to make a product look cooler. However, without knowing what ML is and how it works behind the scenes, it’s very easy to get lost. But this book does a great job of guiding you all the way from very simple math concepts to some sophisticated machine learning techniques. 

Today, in the Python ecosystem, we have a plethora of powerful data science and machine learning related packages available, like Numpy, Pandas, Scikit-learn, and many others, which help to simplify a lot of its inherent complexity. In case you are wondering, in terms of Python packages, the great hero in this book is Scikit-learn, often abbreviated as  sklearn. Of course, the data wrangling is much easier and much faster using Numpy and Pandas, so these two packages are always covering sklearn’s back. Seaborn and Matplotlib, two of the most standard data visualization packages for Python, are also used here. In chapter 10, patsy makes a brief appearance, and in chapter 15, pymc3 is used in the context of probabilistic graphic models. 

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Get Started With Vim

This is the tutorial I wish I had around some years ago when I first tried to learn how to use Vim. If you’re just beginning to know this amazing text editor, please keep reading; I am writing this tutorial right for you!

Whether you prefer to code in a simple steps editor or in a full-fledged IDE, the fact remains that a console-based text mode editor can be an invaluable tool for many purposes, from coding to remote system administration. VIM (Vi IMproved) is the most common version of a classic UNIX text editor named vi, which is currently available on almost any platform. It’s one of the most useful and complex console tools you can find, and also one you can make use right away, no matter what you do for a living (everyone needs to write something, right?).

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Book review – Supercharged Python, by Brian Overland and John Bennet

If you have been following beginner or even intermediate guides on Python and are starting to feel the need for more advanced learning; this book may be the one you have been looking for.

According to the authors, this book was written for those who already know the basics of Python but want to deepen their knowledge and skills. While being targeted to people who already know the fundamentals of Python, it still includes a quick review in the first chapter. It briefly discusses fundamentals like variables, operators, data types, basic I/O, if/else, while, for, function definitions and arguments, lists, tuples, dictionaries, sets, and the distinction between global and local variables. This initial chapter is presented as being an optional reading, as its contents are pretty basic, but the authors recommend that the reader takes a minute or so on the last to pages, which cover the global statement.