Thanks to deep learning, we have very accurate speech recognition in our phones, self-driving cars, and filters that show us the news that is most interesting to us. Tensorflow, developed by Google, has become the most popular framework for deep learning, and now operates on a variety of devices such as multicore CPUs, general purpose GPUs, mobile devices, and custom ASICs. In this webinar, you will get a general introduction to working with Tensorflow and its surrounding ecosystem, general problem classes, where you can get big acceleration, and why run on a CPU. We will highlight some of the ideal use cases for TensorFlow on CPUs, including which models and types of operations benefit most from these optimizations, along with proposed benchmarks, projected accelerations, and how to tune performance for your systems. We will touch on advanced topics like using multiple nodes to train on large data sets.

If you want to break into AI, this Specialization will help you do so. Deep Learning is one of the most highly sought after skills in tech. We will help you become good at Deep Learning. In five courses, you will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to lead successful machine learning projects. You will learn about Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more.

Multiview representation learning is very popular for latent factor analysis. It naturally arises in many data analysis, machine learning, and information retrieval applications to model dependent structures among multiple data sources. For computational convenience, existing approaches usually formulate the multiview representation learning as convex optimization problems, where global optima can be obtained by certain algorithms in polynomial time. However, many evidences have corroborated that heuristic nonconvex approaches also have good empirical computational performance and convergence to the global optima, although there is a lack of theoretical justification. Such a gap between theory and practice motivates us to study a nonconvex formulation for multiview representation learning, which can be efficiently solved by a simple stochastic gradient descent (SGD) algorithm. We first illustrate the geometry of the nonconvex formulation; Then by characterizing the dynamics of the approximate limiting process, we establish global rates of convergence to the global optima. Numerical experiments are provided to support our theory.

I have been actively focusing on specialising Deep Learning for the last 2 years. My personal interest towards Deep learning started around 2015 when Google open sourced Tensorflow. Tried quickly couple of examples from the Tensorflow documentation and left with a feeling that Deep learning is difficult, partly because the framework was new and required better hardware and tons of patience. Fast forward to 2017 I have spent 100's of hours working on Deep learning projects and the technology has become more and more accessible due to several advancements in software (ease of usage -- Keras, PyTorch), hardware(GPU becoming commercially viable for someone like me sitting in India - Not still cheap), availability of data, good books and MOOCs. After completing the 3 most popular MOOCS in deep learning from Fast.ai, deeplearning.ai/Coursera

Sure, there are currently all sorts of options for learning machine learning. You've got your more traditional methods like textbooks. You've got your fancy newfangled approaches like MOOCs and video lectures on YouTube. Podcasts, blogs, Quora questions (and sometimes answers), and research papers abound! But Chris Albon has created and shared a way more cool way to reinforce your machine learning learning (not to be confused with learning reinforcement learning): the flashcard.

The field of natural language processing (NLP) makes it possible to understand patterns in large amounts of language data, from online reviews to audio recordings. But before a data scientist can really dig into an NLP problem, he or she must lay the groundwork that helps a model make sense of the different units of language it will encounter. Word embeddings are a set of feature engineering techniques widely used in predictive NLP modeling, particularly in deep learning applications. Word embeddings transform sparse vector representations of words into a dense, continuous vector space, enabling you to identify similarities between words and phrases -- on a large scale -- based on their context. In this piece, I'll explain the reasoning behind word embeddings and demostrate how to use these techniques to create clusters of similar words using data from 500,000 Amazon reviews of food. You can download the dataset to follow along.

Similarity Based ML models and algorithms (KNN etc.)

This is an implementation of Monotonic Calibrated Interpolated Look-Up Tables in TensorFlow. These are fast-to-evaluate and interpretable lattice models, also known as interpolated look-up tables. This library also provides a rich and intuitive set of regularizations and monotonicity constraints configurable per feature. This tutorial contains more detailed explanation about lattice models and usage in TensorFlow, and check out API docs for python APIs. TensorFlow Lattice is not an official Google product.

Seldon Core is a machine learning platform that helps your data science team deploy models into production. It provides an open-source data science stack that runs within a Kubernetes cluster. Weave Cloud works alongside machine learning platforms such as Seldon's. In this tutorial, you will deploy a predictive service that recognizes drawn numbers from 0 to 9. The predictive model was created using TensorFlow. This example describes how to deploy the pre-packaged Docker image that is available in the Seldon server.

We use text mining a lot in day-to-day data mining operations. In order to share our knowledge on this, to show that R is an extremely mature platform to do business-oriented text analytics and to give you practical experience with text mining, our course on Text Mining with R is scheduled for the 3rd consecutive year at LStat, the Leuven Statistics Research Center (Belgium) as well as at the Data Science Academy in Brussels. Courses are scheduled 2 times in November 2017 and also in March 2018. This course is a hands-on course covering the use of text mining tools for the purpose of data analysis. It covers basic text handling, natural language engineering and statistical modelling on top of textual data.