This is the code repository for TensorFlow Reinforcement Learning Quick Start Guide, published by Packt. Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. If you feel this book is for you, get your copy today! All of the code is organized into folders.

Whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments, our latest ebook bundles from Packt is perfect for you! Get titles like Python Machine Learning, Reinforcement Learning Algorithms with Python, and Machine Learning Projects for Mobile Applications. Plus, your purchase will support Innocent Lives Foundation! Normally, the total cost for the ebooks in this bundle is as much as $1,051. Here at Humble Bundle, you choose the price and increase your contribution to upgrade your bundle! This bundle has a minimum $1 purchase.

We've been told countless times over the past few years what an impact Artificial Intelligence (AI) is going to have on all our lives. But while it's true that Artificial Intelligence will certainly provide some huge opportunities, a recent survey we ran at Packt (See direct PDF) has found there are plenty of challenges. All too often, among the hype and excitement, it's easy to see artificial intelligence as a silver bullet towards a future of automation and innovation. However, it's also true that like any other trend that receives significant attention, the reality of implementing AI is tricky – it's far from a silver bullet solution. In August, we asked 2,800 software engineers – some of them working in data science and analysis, but many more in areas like web development and cybersecurity – for their perspective and attitudes to artificial intelligence.

Take the next step in implementing various common and not-so-common neural networks with Tensorflow 1.x In this book, you will learn how to efficiently use TensorFlow, Google's open source framework for deep learning. You will implement different deep learning networks such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Q-learning Networks (DQNs), and Generative Adversarial Networks (GANs) with easy to follow independent recipes. You will learn how to make Keras as backend with TensorFlow. With a problem-solution approach, you will understand how to implement different deep neural architectures to carry out complex tasks at work.

Get everything you need to know to enter the world of deep learning when it comes to R with this book. Get started from the packages you need to have for your side, building models related to neural networks, prediction, and deep prediction, to fine tuning and optimizing everything you have. With data analysis and numerical computing tutorials at your disposal, discover how to make the most of IPython. Discover why Python is so loved in the data world and revolutionize your work today! Hadoop is one of the most important technologies in a world that is built on data.

What's data science going to look like in 2018? How are job roles in the field going to change? Will AI find new ways to capture the public imagination? There are certainly plenty of questions worth asking when it comes to data science and machine learning, especially after a year of change and big stories bringing artificial intelligence and deep learning into the public consciousness in a way it never has before. Everything at Packt is now $5 until 16th January!

In this post, taken from the book R Data Mining by Andrea Cirillo, we'll be looking at how to scrape PDF files using R. It's a relatively straightforward way to look at text mining – but it can be challenging if you don't know exactly what you're doing. Until January 15th, every single eBook and video by Packt is just $5! Start exploring some of Packt's huge range of R titles here. You may not be aware of this, but some organizations create something called a'customer card' for every single customer they deal with. This is quite an informal document that contains some relevant information related to the customer, such as the industry and the date of foundation. Probably the most precious information contained within these cards is the comments they write down about the customers.

Packt Publishing's $5 sale has become a recurring and welcome end to the year - a great chance for anyone working in tech to stock up on the resources and content they need for the new year. If you want to simply begin searching for the content you want, visit the Packt website now. For KDnuggets readers, there's a wealth of titles that readers will find interesting. Packt have put together a data analysis bundle, featuring some of their most popular data analysis titles of 2017. Whether you're experienced in R, a statistician that wants to take the next step with their coding skills or interested in exploring how Java can be used for data analysis, Packt has products that will prepare you for the new year.

If things continue at the current pace, half of India's IT professionals will have published a "data science" book with Packt by 2030. I am getting tired of reviewing new entries in this stream of low-quality copycats, written by people whose only qualification is the ability to read a couple of relevant books - if the reader is lucky, not those from Packt - and get some relevant R code, from those books or from R packages' vignettes. My recommendation is "Introduction to statistical learning" by James, Witten, Hastie and Tibshirani. I am remembering the story with "Learning Data Mining with R" by Makhabel. There were no reviews for months, then I posted mine, two stars.

It's safe to say that Python is a pretty popular tool across a whole range of industries and professions, thanks, no doubt, to the programming language's accessibility, wealth of libraries and frameworks, and of course, its huge community of die-hard devs that claim Python should be the tool of choice for any self-respecting developer. Packt's 2017 Skill Up survey, backed up these claims when it revealed that Python is the most-used tool for tech professionals across a range of vastly different job roles, slithering its way up from the number 2 spot in 2016. We asked Sebastian Raschka, applied machine learning and deep learning researcher and the author of Packt's best-selling book Python Machine Learning, why he always turns to Python and what's next for what is perhaps undeniably the most popular language of the last two decades. Here's what he had to say.