GitHub is much more than a software versioning tool, which it was originally meant to be. Now people from different backgrounds and not just software engineers are using it to share their tools / libraries they developed on their own, or even share resources that might be helpful for the community. Following the best repos on GitHub can be an immense learning experience. You not only see what are the best open contributions, but also see how their code was written and implemented. Being an avid data science enthusiast, I have curated a list of repositories that have been particularly famous in the year 2017.

Today's blog post is the most fun I've EVER had writing a PyImageSearch tutorial. In keeping with the Christmas and Holiday season, I'll be demonstrating how to take a deep learning model (trained with Keras) and then deploy it to the Raspberry Pi. This image classifier has been specifically trained to detect if Santa Claus is in our video stream. I won't spoil the surprise (but it does involve a 3D Christmas tree and a jolly tune). And most of all, have fun! Today's blog post is a complete guide to running a deep neural network on the Raspberry Pi using Keras.

Adding further understanding, a companion study confirms empirically that ES (with a large enough perturbation size parameter) acts differently than SGD would, because it optimizes for the expected reward of a population of policies described by a probability distribution (a cloud in the search space), whereas SGD optimizes reward for a single policy (a point in the search space). In practice, SGD in RL is accompanied by injecting parameter noise, which turns points in the search space into clouds (in expectation).

Unlike task-specific algorithms, Deep Learning is a part of Machine Learning family based on learning data representations. With massive amounts of computational power, machines can now recognize objects and translate speech in real time, enabling a smart Artificial intelligence in systems. The concept of a software simulating the neocortex's large array of neurons in an artificial neural network is decades old, and it has led to as many disappointments as breakthroughs. But because of improvements in mathematical formulas and increasingly powerful computers, today researchers & data scientists can model many more layers of virtual neurons than ever before. Languishing through the 1970's, early neural networks could simulate only a very limited number of neurons at once, so they could not recognize patterns of great complexity.

After all, it's been a popular focus in movies such as The Terminator, The Matrix, and Ex Machina (a personal favorite of mine). But you may have recently been hearing about other terms like "Machine Learning" and "Deep Learning," sometimes used interchangeably with artificial intelligence. As a result, the difference between artificial intelligence, machine learning, and deep learning can be very unclear. I'll begin by giving a quick explanation of what Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) actually mean and how they're different. Then, I'll share how AI and the Internet of Things are inextricably intertwined, with several technological advances all converging at once to set the foundation for an AI and IoT explosion.

You don't have to agree with Elon Musk's apocalyptic fears of artificial intelligence to be concerned that, in the rush to apply the technology in the real world, some algorithms could inadvertently cause harm. This type of self-learning software powers Uber's self-driving cars, helps Facebook identify people in social-media posts, and let's Amazon's Alexa understand your questions. Now DeepMind, the London-based AI company owned by Alphabet Inc., has developed a simple test to check if these new algorithms are safe. Researchers put AI software into a series of simple, two-dimensional video games composed of blocks of pixels, like a chess board, called a gridworld. It assesses nine safety features, including whether AI systems can modify themselves and learn to cheat.

According to CEO Kaisa Helminen, Fimmic Ltd is a software company that operates in the medical sector. The company provides software solutions that utilize artificial intelligence (AI) and machine vision in tissue section analytics. Fimmic's staff consists of professionals from various fields who have comprehensive knowledge of medicine and software development, as well as solid experience in deep learning AI technologies and life science business. Fimmic's software solutions are originally based on inventions created by the company's Chief Scientific Officer Johan Lundin and his brother, Director of Concept Design, Mikael Lundin. The brothers, who are both medical doctors, originally developed the cloud-based software platform for research use, to speed up the analysis and sharing of extremely large microscope images taken from tissues sections.

Unlike other machine learning algorithms, neural networks are also designed to be able to learn from their mistakes. This is another reason many deep-learning scientists believe we are finally beginning to develop machines that come close to what most people would consider artificial intelligence. Just like humans, machines can now make a prediction, act on that prediction and learn from that action, whether right or wrong. Like humans, this allows them to become more accurate over time as more decisions and feedback is collected. For the first time ever, neural networks are enabling human-like insight and learning in computers.

Over the past few years AI has exploded, and especially since 2015. Much of that has to do with the wide availability of GPUs that make parallel processing ever faster, cheaper, and more powerful. It also has to do with the simultaneous one-two punch of practically infinite storage and a flood of data of every stripe (that whole Big Data movement) – images, text, transactions, mapping data, you name it.

Does the idea of working on Transformative AI Spark your interest? We are working with a new Cambridge start-up that are looking to make ground breaking changes to the way in which we interact with services and devices. We have a fantastic opportunity to work alongside some of the leading experts in the Machine Learning field in a start-up that has been founded and funded by one of the original investors in Deepmind. The possibilities for this technology are attracting some of the brightest lights within the Machine Learning and Artificial Intelligence field. Whether you are a Machine Learning research based Scientist or a C /Python programming guru within the field we would love to hear from you.