The "Learn SQL the Hard Way" and "SQL Problems & Solutions" are definitely worth looking in to. If you're looking for something slightly more fun and interactive, try GalaXQL. GalaXQL is a visual platform, offering lessons on SQL in a database of fictional galaxies. The galaxy rendering reflects the changes you make in the database.

Machine learning is one of the hottest new technologies to emerge into popular consciousness in the last decade, transforming fields from consumer electronics and healthcare to retail. This has led to intense curiosity about this field among many students and working professionals about the field. If you're a tech professional such as a software developer, business analyst or even a product manager, you might be curious about how machine learning can change they way you work and take your career to the next level. However, as a busy professional, you're also looking for a way to get a solid understanding of machine learning that's not only rigorous and practical, but also concise and fast. This machine learning tutorial will help you achieve your goals.

Google's TensorFlow has been a hot topic in deep learning recently. The open source software, designed to allow efficient computation of data flow graphs, is especially suited to deep learning tasks. It is designed to be executed on single or multiple CPUs and GPUs, making it a good option for complex deep learning tasks. In it's most recent incarnation – version 1.0 – it can even be run on certain mobile operating systems. This introductory tutorial to TensorFlow will give an overview of some of the basic concepts of TensorFlow in Python. These will be a good stepping stone to building more complex deep learning networks, such as Convolution Neural Networks and Recurrent Neural Networks, in the package.

A strange iPhone bug forces iPhones to stop working entirely. The bizarre issue doesn't appear to pose any immediate threat, and can only be done by someone who has access to the phone – unlike recent, more malicious issues like the ones that allow people to crash phones with just a text message. But it is certainly irritating and seems to work on all versions of iOS, and hasn't yet been fixed by Apple. The issue forces the iPhone to temporarily become useless. But once time has passed it will fix itself again, allowing the phone to come back to life.

In 2013 protests broke out in Oakland, California, directed against the private buses that shuttle tech workers from pricey homes in the city's gentrifying areas to jobs in Silicon Valley. "You live your comfortable lives," read a flyer that protesters handed out to passengers, "surrounded by poverty, homelessness, and death, seemingly oblivious to everything around you, lost in the big bucks and success." That moment of backlash was an outgrowth of what I call the New Urban Crisis: the decline of middle-class neighborhoods, the gentrification of the downtowns of certain cities, and the reshaping of America's metropolitan regions into islands of advantage surrounded by larger swaths of disadvantage. Technology is one of the country's biggest growth industries, but it comes at a price--just ask any teacher looking for housing in San Francisco. Meanwhile, other areas aspire to build similar tech-based economies, hoping to become a Silicon Alley, Prairie, or Gulch, though potentially triggering crises of their own.

Let's take a breath: robots and artificial intelligence systems are nowhere near displacing the human workforce. Nevertheless, no less a voice than Bill Gates has asserted just the opposite and called for a counterintuitive, preemptive strike on these innovations. His proposed weapon of choice? Taxes on technology to compensate for losses that haven't happened. Taxing this promising field of innovation is not only reactionary and antithetical to progress, it would discourage the development of technologies and systems that can improve everyday life. Imagine where we would be today if policy makers, fearing the unknown, had feverishly taxed personal computer software to protect the typewriter industry, or slapped imposts on digital cameras to preserve jobs for darkroom technicians.

In the past years, a collection of hardware, software and online service have managed to bring changes and reforms to classrooms and teaching methods. But the true disruption of education is yet to arrive. Artificial Intelligence has proven its role as a game changing factor in an increasing number of fields, causing transformations unimaginable in the past. It's now showing glimmers of how it might forever change the learning process, one of the oldest skills that mankind has mastered. We've teamed up with Product Hunt to offer you the chance to win an all expense paid trip to TNW Conference 2017!

So, I'm struggling a bit with the idea of applying machine learning and artificial intelligence technologies to everything around me. Google recently gave me a Google Home device; I'm not quite sure why. Maybe they wanted to give it more real world voice recognition training opportunities. Maybe they wanted me to write about it, as good social marketing. Maybe they wanted to hear how I'm advising their competitors, naughty, naughty. It's something that I feel a natural affinity for anyway, having spent some time at a "smart home" startup a decade ago, which we'd now label "IoT" technology.

In recent years there is a growing interest in using deep representations for reinforcement learning. In this paper, we present a methodology and tools to analyze Deep Q-networks (DQNs) in a non-blind matter. Moreover, we propose a new model, the Semi Aggregated Markov Decision Process (SAMDP), and an algorithm that learns it automatically. The SAMDP model allows us to identify spatio-temporal abstractions directly from features and may be used as a sub-goal detector in future work. Using our tools we reveal that the features learned by DQNs aggregate the state space in a hierarchical fashion, explaining its success. Moreover, we are able to understand and describe the policies learned by DQNs for three different Atari2600 games and suggest ways to interpret, debug and optimize deep neural networks in reinforcement learning.

Notably, this includes the mean and standard deviations of the RMSE scores from each population of results. The mean gives an idea of the average expected performance of a configuration, whereas the standard deviation gives an idea of the variance. The min and max RMSE scores also give an idea of the range of possible best and worst case examples that might be expected. Looking at just the mean RMSE scores, the results suggest that an epoch configured to 1000 may be better. The results also suggest further investigations may be warranted of epoch values between 1000 and 2000.