Learning Objectives - In this module you will learn about the Recommendation platforms and implement a Recommender using MapReduce. Topics - User based recommendation, User Neighbourhood, Item based Recommendation, Implementing a Recommender using MapReduce, Platforms: Similarity Measures, Manhattan Distance, Euclidean Distance, Cosine Similarity, Pearson's Correlation Similarity, Loglikihood Similarity, Tanimoto, Evaluating Recommendation Engines (Online and Offline), Recommendors in Production.

This course lays the foundation from which you can begin using Python to solve any problem - whether in Data Analysis, Machine Learning or Web Development. It gives you a fundamental understanding of Python loops, data structures, functions, classes and more to help you solve basic programming tasks so that you can confidently apply those skills to solve real problems. The course assumes zero prior experience with Python, though some fundamental knowledge of programming is recommended.

Scala has emerged as an important tool for performing various data analysis tasks efficiently. This video will help you leverage popular Scala libraries and tools and perform core data analysis tasks with ease. This course will introduce you to Deeplearning4j; you will start with tasks such as integrating with Spark and Linear Regression with Deep Learning. Then you will make use of popular Scala libraries such as Breeze to plot your data. There is also a special focus on using Bokeh to plot your data.

As if the point needs belaboring, but sure: The future of technology, no matter how far down the line you trace it, will inevitably run into A.I. at some point. So it's fitting -- if not overdue -- that an established, esteemed American university would offer up an undergraduate degree in artificial intelligence. And that school is Carnegie Mellon University, of course. Per the MIT Tech Review, the program will be run out of the college's School of Computer Science. It'll involve the social and ethical impacts of A.I. as much as it will computational learning, along with the technical knowhow to have a decent grasp on what the future of A.I. is going to be, and maybe practical work on some of it, too (as a precursor to joining CMU's top-flight status as the graduate school for A.I.).

Somewhere within the Vatican exists the Vatican Secret Archives, whose 53 miles of shelving contains more than 600 collections of account books, official acts, papal correspondence, and other historical documents. Though its holdings date back to the eighth century, it has in the past few weeks come to worldwide attention. This has brought about all manner of jokes about the plot of Dan Brown's next novel, but also important news about the technology of manuscript digitization. It seems a project to get the contents of the Vatican Secret Archives digitized and online has made great progress cracking a problem that once seemed impossibly difficult: turning handwriting into computer-searchable text. In Codice Ratio is "developing a full-fledged system to automatically transcribe the contents of the manuscripts" that uses not the standard method of optical character recognition (OCR), which looks for the spaces between words, but a new way that can handle connected cursive and calligraphic letters.

Keras is an open source neural network library written in Python. It is capable of running on top of MXNet, Deep learning Tensorflow, CNTK, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being minimal, modular, and extensible. This course provides a comprehensive expert level details in deep learning(Keras). We start by a brief recap of the most common concepts found in machine learning.

Lookout is a new app that uses image recognition and artificial intelligence to describe a scene through a phone's camera. Google (GOOG) announced the app at its annual I/O developer conference this week. Google is testing the app now and said it will be released later this year, starting on Pixel devices. If you walk into building lobby with an Android camera held in your hand or worn around the neck on a lanyard, pointed outward, Lookout will detect people and objects like elevator doors, and read nearby text. The app doesn't require an internet connection.

You are a data scientist. Every day, you stare at reams of data trying to apply the latest and brightest of models to uncover new insights, but there seems to be an endless supply of obstacles. Your colleagues depend on you to monetize your firm's data - and the clock is ticking. Troubleshooting Python Machine Learning is the answer. We have systematically researched common ML problems documented online around data wrangling, debugging models such as Random Forests and SVMs, and visualizing tricky results.

Knowledge distillation (KD) consists of transferring knowledge from one machine learning model (the teacher}) to another (the student). Commonly, the teacher is a high-capacity model with formidable performance, while the student is more compact. By transferring knowledge, one hopes to benefit from the student's compactness. %we desire a compact model with performance close to the teacher's. We study KD from a new perspective: rather than compressing models, we train students parameterized identically to their teachers. Surprisingly, these {Born-Again Networks (BANs), outperform their teachers significantly, both on computer vision and language modeling tasks. Our experiments with BANs based on DenseNets demonstrate state-of-the-art performance on the CIFAR-10 (3.5%) and CIFAR-100 (15.5%) datasets, by validation error. Additional experiments explore two distillation objectives: (i) Confidence-Weighted by Teacher Max (CWTM) and (ii) Dark Knowledge with Permuted Predictions (DKPP). Both methods elucidate the essential components of KD, demonstrating a role of the teacher outputs on both predicted and non-predicted classes. We present experiments with students of various capacities, focusing on the under-explored case where students overpower teachers. Our experiments show significant advantages from transferring knowledge between DenseNets and ResNets in either direction.

Judea Pearl, chancellor's professor of computer science and statistics at UCLA, has written his first book intended for a general audience, "The Book of Why: The New Science of Cause and Effect." The book, which was written with co-author Dana Mackenzie, explores causality -- the study of cause and effect -- from its origins to its applications at the leading edges of science. Pearl, a UCLA faculty member since 1970, received the 2011 A.M. Turing Award, considered the "Nobel Prize" in computing, for his landmark work in processing information under uncertainty. His new book will be published on May 15. That same day, Pearl will deliver a talk at the Charles E. Young Research Library as part of the UCLA Library Writer Series.