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.

Latent tree models are graphical models defined on trees, in which only a subset of variables is observed. They were first discussed by Judea Pearl as tree-decomposable distributions to generalise star-decomposable distributions such as the latent class model. Latent tree models, or their submodels, are widely used in: phylogenetic analysis, network tomography, computer vision, causal modeling, and data clustering. They also contain other well-known classes of models like hidden Markov models, Brownian motion tree model, the Ising model on a tree, and many popular models used in phylogenetics. We offer here a concise introduction to the theory of latent tree models. We emphasise the role of tree metrics in the structural description of this model class, in designing learning algorithms, and in understanding fundamental limits of what and when can be learned. We present Gaussian and general Markov models as subclasses of latent tree models that admits tractable and rigorous analysis. A leaf of T is a vertex of degree one, an internal vertex is a vertex which is not a leaf, and an inner edge is an edge whose both ends are internal vertices. Given a treeT define a rooted tree as a directed graph obtained from T by picking one of its verticesr and directing all edges away fromr . The vertexr is called the root. Trees will be always leaf-labeled with the labelling set{ 1,...,m}, where m is the number of leaves. An undirected tree is trivalent if each internal vertex has degree precisely three. A rooted tree is a binary rooted tree if each internal vertex has precisely two children. In many applications rooted trees are depicted without using arrows, where direction is made implicit by drawing the root on the top and the leaves on the bottom; see Figure 1(c). Two special types of undirected trees are: a star tree with one internal vertex and a trivalent tree on four leaves called a quartet tree; see Figure 1(a) and (b). A forest is a collection of trees. Forests here are also leaf-labeled with the labelling set is{ 1,...,m}, which means that each tree in this collection is leaf-labeled and the corresponding collection of labelling sets forms a set partition of { 1,...,m}. We define three graph operations on trees (forests). Removing an edge means removing that edge from the edge set. Contracting an edge u v means removingu,v from the vertex set, adding a new vertexw and edges such thatw is adjacent to all vertices which were adjacent tou or v. Suppressing a vertex of degree two means removing that vertex and replacing the two edges incident to that vertex by a single edge. 1 2 3 4 5 1 2 3 4 (a) (b) (c) Figure 1: (a) An undirected star tree with five leaves, (b) a quartet tree, (c) a binary rooted tree.

Lecture 1 of Tim Roughgarden's Algorithmic Game Theory class at Stanford (Autumn 2013) Class description: Topics at the interface of computer science and game theory such as: algorithmic mechanism design; combinatorial auctions; computation of Nash equilibria and relevant complexity theory; congestion and potential games; cost sharing; game theory and the Internet; matching markets; network formation; online learning algorithms; price of anarchy; prior-free auctions; selfish routing; sponsored search.

Special note: we're teaching a fully updated part 1, in person, for seven weeks from Oct 30, 2017, at the USF Data Institute. See the course page for details and application form. Part 1 of the course has now been viewed by tens of thousands of students, introducing them to nearly all of today's best practices in deep learning, and providing many hours of hands-on practical coding exercises. We have collected some stories from graduates of part 1 on our testimonials page. Today, we are launching Part 2: Cutting Edge Deep Learning for Coders.