The television show will be shot in both Canada and United States. The first Canadian episodes will be shot in Toronto on November 27, 2016. And the first US episodes will be shot on March 16 and 17 in 2017. These shows are going to be aired on (TBC) with an expected viewership of 3 million. Day one of the show will feature pitches by the artificial intelligence companies to the world of investors.

Data Science Tutorials for Python: This link contains a curated list of Python tutorials for Data Science, NLP and Machine Learning. This also serves as a reference guide for several common data analysis tasks. Data Science Tutorials for Python: This link contains a curated list of Python tutorials for Data Science, NLP and Machine Learning. This also serves as a reference guide for several common data analysis tasks. Data Science Tutorials for R: This link contains a curated list of R tutorials for Data Science, NLP and Machine Learning.

Publishing is no longer about writing just a book. Therefore publishing is not just dependent on itself but on other mediums as well. A best selling book makes it to a Netflix show or a movie and that in turn gives the book sales another fillip. So content in its traditional form - i.e as a book is not dying anytime. In fact, the need for good content is greater than ever as players like Netflix and HBO keep on searching for the next bestselling story to be made into a tv show or a movie.

We've already published the top big data presentations on slideshare, as well as great Github list of public data sets, or top machine learning projects, or top R packages. We've asked our readers to share a list of top Data Science videos on YouTube. Here, we share a list of top data science presentations from VideoLectures.net. These presentations received 5 to 20 times fewer page views than those on Slideshare, because they are far more technical, and attract a different, truly technical audience. You can check the entire list here.

The meeting format typically alternates between lecture/paper discussions and lab sessions where we review code. In our lecture sessions we discuss and gain a better understanding of course lectures. In our lab sessions, we walk methodically through code from course assignments. We intend to expand our projects beyond the course material, based on the interests of the group. We welcome all new members and participants, regardless of experience level, who are excited about rolling up their sleeves to dig into Deep Learning.

There are many Python machine learning resources freely available online. Go from zero to Python machine learning hero in 7 steps! The first step is often the hardest to take, and when given too much choice in terms of direction it can often be debilitating. This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best?

The Perceptron algorithm is the simplest type of artificial neural network. It is a model of a single neuron that can be used for two-class classification problems and provides the foundation for later developing much larger networks. In this tutorial, you will discover how to implement the Perceptron algorithm from scratch with Python. How to train the network weights for the Perceptron. How to implement the Perceptron algorithm for a real-world classification problem.

If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential. This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features. You will be acquainted with the use of logistic regression, regularization, and the various loss functions that are used by generalized linear models.

In this course, intended to expand upon your knowledge of neural networks and deep learning, you'll harness these concepts for computer vision using convolutional neural networks. Going in-depth on the concept of convolution, you'll discover its wide range of applications, from generating image effects to modeling artificial organs. Explore the StreetView House Number (SVHN) dataset using convolutional neural networks (CNNs) Build convolutional filters that can be applied to audio or imaging Extend deep neural networks w/ just a few functions Note: we strongly recommend taking The Deep Learning & Artificial Intelligence Introductory Bundle before this course. The Lazy Programmer is a data scientist, big data engineer, and full stack software engineer. For his master's thesis he worked on brain-computer interfaces using machine learning.

The world's best gamers may one day compete against the smartest computers Google cut power usage in its data centres by several percentage points earlier this year by trusting artificially intelligent software originally designed to beat 1980s-era Atari video games. And in the years to come, the Internet giant not only could save much more electricity but also solve far larger problems by taking on a much more complex video game. Research scientists at Google's DeepMind unit announced Friday they are developing a computer program that reads data about Blizzard Entertainment's "StarCraft II" games and learns how to play on its own. The software would have to figure out how to split its attention between micromanagement and long-term strategic decisions. It's that manoeuvring that could deliver big breakthroughs.