Here is a list of the best courses in Data Science and Machine Learning from Udemy. With the back-to-school sale, you can get these and other Udemy courses for $12 ($10 if it is your first purchase), 90-95% off original price. Offer expires on Aug 10, 2017. Udemy.com is an online marketplace for learning, their data science content is updated regularly by the instructors who created good courses (filled with actionable tools) and bite-size lessons that help you cover defined topics at your own pace. Ready to be thrown into the deep end and learn the real problems a data scientist faces on a daily basis?

A quick, hassle-free way to stay on top of robotics news, our robotics digest is released on the first Monday of every month. Sign up to get it in your inbox. From wacky talking Einsteins to clumsy security'bots, from speedy drones to the underwater operations at Fukushima, it's been another busy month. So let's kick off our July review with a look at robots in action! You'd be forgiven for missing this first one.

Machine Learning and Deep Learning has brought the future here. Predicting the future has always been the most sought after skill in this world. How much money could you make if you could predict the price of a stock or if you could predict which color will be in fashion six months later? You can predict almost anything that you wish. The future will be in your own palms.

UPDATE: Mar 20, 2016 - Added my new follow-up course on Deep Learning, which covers ways to speed up and improve vanilla backpropagation: momentum and Nesterov momentum, adaptive learning rate algorithms like AdaGrad and RMSProp, utilizing the GPU on AWS EC2, and stochastic batch gradient descent. We look at TensorFlow and Theano starting from the basics - variables, functions, expressions, and simple optimizations - from there, building a neural network seems simple! Deep learning is all the rage these days. What exactly is deep learning? Well, it all boils down to neural networks.

Convolutional Neural Network (CNNs) are typically associated with Computer Vision. CNNs are responsible for major breakthroughs in Image Classification and are the core of most Computer Vision systems today. More recently CNNs have been applied to problems in Natural Language Processing and gotten some interesting results. In this paper, we will try to explain the basics of CNNs, its different variations and how they have been applied to NLP. This is a more concise survey than the paper below, and does a good job at 1/5 the length.

Yesterday, IBM opened its first Machine Learning Hub in Bengaluru. The company hopes that the Hub will allow engineers, data professionals, business analysts and organizations to greatly benefit and learn through collaboration with like-minded organisations. IBM says that various enterprises will now have an opportunity to work closely with IBM data experts to understand the technology to analyze, visualize and interpret data. The Bengaluru ML hub is an addition to IBM's growing ML Hubs, similar to the ones in Toronto, San Jose, California, Beijing, Boblingen, and at IBM's Silicon Valley Lab. According to IBM, their experts will also assist visitors build and test rapid, scalable prototypes for fast deployment of their models.

Deep learning is a subfield of machine learning that is a set of algorithms that is inspired by the structure and function of the brain. TensorFlow is the second machine learning framework that Google created and used to design, build, and train deep learning models.You can use the TensorFlow library do to numerical computations, which in itself doesn't seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated between these edges. The name "TensorFlow" is derived from the operations which neural networks perform on multidimensional data arrays or tensors! For now, this is all you need to know about tensors, but you'll go deeper into this in the next sections! Today's TensorFlow tutorial for beginners will introduce you to performing deep learning in an interactive way: Also, you could be interested in a course on Deep Learning in Python, DataCamp's Keras tutorial or the keras with R tutorial. To understand tensors well, it's good to have some working knowledge of linear algebra and vector calculus. You already read in the introduction that tensors are implemented in TensorFlow as multidimensional data arrays, but some more introduction is maybe needed in order to completely grasp tensors and their use in machine learning.

Consider a probabilistic model with joint distribution p(x, z) where x are data and z are latent variables and/or random parameters. Suppose that exact inference in p(x, z) is intractable which means that the posterior distribution p(z x) p(x, z) p(x, z)dz, is difficult to compute due to the normalizing constant p(x) p(x, z)dz that represents the probability of the data and it is known as evidence or marginal likelihood. The marginal likelihood is essential for estimation of any extra parameters in p(x) or for model comparison. Approximate inference algorithms target to approximate p(z x) and/or p(x). Two general frameworks, that we briefly review next, are based on Markov chain Monte Carlo (MCMC) [33, 2] and variational inference (VI) [17, 40].

BigDL runs natively on Apache Spark, and because Qubole offers a greatly enhanced and optimized Spark as a service, it makes for a perfect deployment platform. In this Part 1 of a two-part series, you will learn how to get started with distributed Deep Learning library BigDL on Qubole. By the end, you will have BigDL installed on a Spark cluster with a distributed Deep Learning library readily available for you to use in your Deep Learning applications running on Qubole. In Part 2, you will learn how to write a Deep Learning application on Qubole that uses BigDL to identify handwritten digits (0 to 9) using a LeNet-5 (Convolutional Neural Networks) model that you will train and validate using MNIST database. Before we get started, here's some introduction and background on the technologies involved.

Computer Vision generates mathematical models from images; Computer Graphics draws in images from models and lastly image processing takes image as an input and gives an image at the output. Computer Vision is an overlapping field drawing on concepts from areas such as artificial intelligence, digital image processing, machine learning, deep learning, pattern recognition, probabilistic graphical models, scientific computing and a lot of mathematics. Watch these videos and alongside implementing the learned concepts and algorithms by following GaTech Prof. James Hays' projects of his Computer Vision class. Have a quick go through Building Machine Learning Systems with Python and Python Machine Learning.