This article comes from Algobeans Layman tutorials in analytics. Whenever you spot a trend plotted against time, you would be looking at a time series. The de facto choice for studying financial market performance and weather forecasts, time series are one of the most pervasive analysis techniques because of its inextricable relation to time--we are always interested to foretell the future. One intuitive way to make forecasts would be to refer to recent time points. Today's stock prices would likely be more similar to yesterday's prices than those from five years ago.

Earlier this year, artificial intelligence scientist Sebastian Thrun and colleagues at Stanford University demonstrated that a "deep learning" algorithm was capable of diagnosing potentially cancerous skin lesions as accurately as a board-certified dermatologist.

To enable exponential health improvements we need to understand what artificial intelligence is, what we can do with it and how to do that. Artificial Intelligence (AI) is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. This the force multiplier that sits on top of all the low cost sensors and patient data inputs. For the A.I possibilities we can then think of D.A.S.H when looking at applications for health transformation. Neural Networks – e.g systems modelled on the brain & nervous system these are often used in deep learning systems and are used in character recognition, time series prediction, expert systems and classification Evolutionary computing – e.g systems modelled on evolutionary programming, evolution strategies and genetic algorithms used to solve complex real world problems e.g in populations or swarms Computer Vision – e.g systems enabling object recognition, image understanding and augmented reality used to automate vision based problem solving.

Microsoft is currently topping the leaderboard in the race to develop Machine Reading – the ability for computers to read arbitrary text and extract meaning from it sufficient to answer questions regarding the content. "We're trying to develop what we call a literate machine: A machine that can read text, understand text and then learn how to communicate, whether it's written or orally," said Kaheer Suleman, the co-founder of Maluuba, a Quebec-based deep learning startup that Microsoft acquired earlier this year. Microsoft's teams are currently on top of the SQuAD leaderboard, which pits academic teams against each other to develop technology to read information from Wikipedia to test how well AI systems can answer questions about text passages. Microsoft researchers and other industry and academic experts also are competing for the best results using another dataset, called MS MARCO, that uses real, anonymized data from Bing search queries to test a system's ability to answer real questions from real users. "We're not just going to build a bunch of algorithms to solve theoretical problems. We're using them to solve real problems and testing them on real data," said Rangan Majumder, a partner group program manager within Microsoft's Bing division.

TensorFlow was originally a deep learning research project of the Google Brain Team that has since become--by way of collaboration with 50 teams at Google--a new, open source library deployed across the Google ecosystem, including Google Assistant, Google Photos, Gmail, search, and more. With TensorFlow in place, Google is able to apply deep learning across numerous areas using perceptual and language-understanding tasks. This smart person's guide is an easy way to get up to speed on TensorFlow. We'll update this guide periodically when news and updates about TensorFlow are released. When you have a photo of the Eiffel Tower, Google Photos can identify the image.

When delving into the world of machine learning (ML), choosing one framework from many alternatives can be an intimidating task. You might already be familiar with the names, but it's useful to evaluate the options during the decision-making process. There are different frameworks, libraries, applications, toolkits, and datasets in the machine learning world that can be very confusing, especially if you're a beginner. Being accustomed to the popular ML frameworks is necessary when it comes to choosing one to build your application. This is why we compiled a list of the top 10 machine learning frameworks.

By now, you might already know machine learning, a branch in computer science that studies the design of algorithms that can learn. Today, you're going to focus on deep learning, a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain. These algorithms are usually called Artificial Neural Networks (ANN). Deep learning is one of the hottest fields in data science with many case studies with marvelous results in robotics, image recognition and Artificial Intelligence (AI). One of the most powerful and easy-to-use Python libraries for developing and evaluating deep learning models is Keras; It wraps the efficient numerical computation libraries Theano and TensorFlow.

Programming languages are the primary tool of the software development industry. Since the 1940's hundreds of them have been created and a huge amount of new lines of code in diverse programming languages are written and pushed to active repositories every day. We believe that a source code classifier that can identify the programming language that a piece of code is written in would be a very useful tool for automatic syntax highlighting and label suggestion on platforms, such as StackOverflow and technical wikis. This inspired us to train a model for classifying code snippets based on their language, leveraging recent AI techniques for text classification. We collected hundreds of thousands of source code files from GitHub repositories using the GitHub API.

KDnuggets 18th annual software poll is asking What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? We tried to include the most popular tools and platforms, while keeping the total number of choices under 100. The tools are organized in 4 sections, with tools alphabetical in each section. Combining these different types of tools in one poll is messy, but it allows interesting analysis afterwards, such as What Big Data, Data Science, Deep Learning software goes together? This is the 18th such KDnuggets Poll, and several of the past polls suffered from excessive voting from some vendors.

There is a new wave of deep neural networks coming. They are the evolution of feed-forward models, that we previously analyzed in detail. The new kind of neural networks are an evolution of the initial feed-forward model of LeNet5 / AlexNet and derivatives, and include more sophisticated by-pass schemes than ResNet / Inception. These feedforward neural networks are also called encoders, as they compress and encode images into smaller representation vectors. To surpass these limitation, we need a new kind of network that can project back a learned representation into the input image space, and also that can be trained on temporally coherent sequences of images: we need to train on videos.