I hope you enjoy them, and it would be great if you would leave brief reviews of these books in the comments below – I'm sure all the authors would appreciate your comments and shares. About the Author Lee Baker is an award-winning software creator with a passion for turning data into a story. A proud Yorkshireman, he now lives by the sparkling shores of the East Coast of Scotland. Physicist, statistician and programmer, child of the flower-power psychedelic '60s, it's amazing he turned out so normal! Turning his back on a promising academic career to do something more satisfying, as the CEO and co-founder of Chi-Squared Innovations he now works double the hours for half the pay and 10 times the stress - but 100 times the fun! He also wanted to be rich, famous and good looking.

Tree methods are commonly used in data science to understand patterns within data and to build predictive models. The term Tree Methods covers a variety of techniques with different levels of complexity but my aim is to highlight three I find useful. To set the problem up let's assume we have a census dataset containing age, education, employment status and so on. Given all this information we want to see if we can predict whether a person earns more than $50k per year. How can tree methods help us?

As you may have noticed, we've been researching artificial intelligence (AI) and its economic and educational implications. From healthcare to transportation, we believe it is incredibly important for young people and adults to be learning about AI, and we are writing more about it to equip teachers and parents with information to help young people ask good questions about the implications of AI on their lives and livelihoods. To get the scoop, I sat Tom Vander Ark down for a podcast interview on AI. You'll also hear from Gerald Huff, a senior Silicon Valley software engineer, who shares his thoughts on AI and what it means for students and the transportation industry. Listen to the podcast, read excerpts from the interview below and be sure follow the campaign at #AskAboutAI.

Dumbo startup Paperspace announced on Thursday that it is launching a service to allow startups and software developers to run Linux for cheap in their virtual desktop. Erb's company is a bit like Airbnb, or many other of the sharing economy startups, but for processing power. It pays for an asset, in this case super heavy duty GPUs and processing power, and rents it out to individual users as they need it. "I've been impressed with the Paperspace team since Dillon and Dan showed me their first demo at YC…," Reddit cofounder Alex Ohanion wrote on the company's Product Hunt launch. "I think their take on machine learning will open up the tech to an even broader audience. I can see this really taking off the developer community."

Walter Pitts was used to being bullied. He'd been born into a tough family in Prohibition-era Detroit, where his father, a boiler-maker, had no trouble raising his fists to get his way. One afternoon in 1935, they chased him through the streets until he ducked into the local library to hide. The library was familiar ground, where he had taught himself Greek, Latin, logic, and mathematics--better than home, where his father insisted he drop out of school and go to work. Outside, the world was messy. Inside, it all made sense. Not wanting to risk another run-in that night, Pitts stayed hidden until the library closed for the evening. Alone, he wandered through the stacks of books until he came across Principia Mathematica, a three-volume tome written by Bertrand Russell and Alfred Whitehead between 1910 and 1913, which attempted to reduce all of mathematics to pure logic. Pitts sat down and began to read. For three days he remained in the library until he had read each volume cover to cover--nearly 2,000 pages in all--and had identified several mistakes. Deciding that Bertrand Russell himself needed to know about these, the boy drafted a letter to Russell detailing the errors.

I'm a software engineer by training and I've had little interaction with AI. I had always wanted to delve deeper into machine learning, but never really found my "in". That's why when Google open sourced TensorFlow in November 2015, I got super excited and knew it was time to jump in and start the learning journey. Not to sound dramatic, but to me, it actually felt kind of like Prometheus handing down fire to mankind from the Mount Olympus of machine learning. In the back of my head was the idea that the entire field of Big Data and technologies like Hadoop were vastly accelerated when Google researchers released their Map Reduce paper. This time it's not a paper – it's the actual software they use internally after years and years of evolution.

The deep learning process could be about to change dramatically thanks to work being carried out Cray, Microsoft and the Swiss National Supercomputing Centre. In existing architectures and conventional systems, deep learning requires a slow training process that can take months, something that can lead to significantly higher costs and delays in making scientific discoveries. Cray believes that its work with Microsoft and CSSC could have solved this problem by applying supercomputing architectures to accelerate the training process. The three worked together to scale the Microsoft Cognitive Toolkit on a Cray XC50 supercomputer at CSCS nicknamed "Piz Daint". According to the supercomputer manufacturer, deep learning problems share algorithmic similarities with applications that are traditionally run on a massively parallel supercomputer.

The latest artificial-intelligence techniques are being adopted by companies at a blistering pace. Before long, hackers might start taking a closer look, too, and they could cause all sorts of trouble by tricking these systems with illusory data. Speaking at a recent AI conference in Barcelona, Spain, Ian Goodfellow, a research scientist at OpenAI who has done pioneering work on deceiving machine-learning systems, said attacking the systems is easy. "Almost anything bad you can think of doing to a machine-learning model can be done right now," he said. "And defending is really, really hard."

The commonest questions about data science are to do with getting started. I have even found myself vacillating between these two states -- and to limit the sense of panic and doom that normally accompanies this statelessness, I maintain a list of resources that contain instructional material which achieves a healthy tradeoff between the two states. In other words, these are resources that help you learn machine learning and its underlying disciplines, as well as the nuances of the software tools available to implement them. On top of my list is Machine Learning for Hackers. One of the reasons I love this book is that it is actually a textbook on machine learning meant for hackers.

Has Deep Learning Made Traditional Machine Learning Irrelevant?