Deep Learning models often perform significantly better than traditional machine learning algorithms in many tasks. This course consists of hands-on recipes to use deep learning in the context of supervised and unsupervised learning tasks. After covering the basics of working with TensorFlow, it shows you how to perform the traditional machine learning tasks in supervised learning: regression and classification. This course also covers how to perform unsupervised learning using cutting-edge techniques from Deep Learning. To address many different use cases, this product presents recipes for both the low-level API (TensorFlow core) as well as the high-level APIs (tf.contrib.lean

Digital Curator Mia Ridge writes: in this guest post, Dr Giles Bergel describes some experiments with the Library's digitised images... The University of Oxford's Visual Geometry Group has been working with a number of British Library curators to apply computer vision technology to their collections. On April 5 of this year I was invited by BL Digital Curator Dr. Mia Ridge to St. Pancras to showcase some of this work and to give curators the opportunity to try the tools out for themselves. Computer vision - the extraction of meaning from images - has made considerable strides in recent years, particularly through the application of so-called'deep learning' to large datasets. Cultural collections provide some of the most interesting test-cases for computer vision researchers, due to their complexity; the intensity of interest that researchers bring to them; and to their importance for human well-being.

NLP, or Natural Language Processing, is a computational approach to communication. This course will get you up-and-running with the popular NLP platform called Natural Language Toolkit (NLTK) in no time. You will start off by preparing text for Natural Language Processing by cleaning and simplifying it. Then you will implement more complex algorithms to break this text down and uncover contextual relationships that reveal the meaning and content of the text. You will learn how to tokenize various parts of sentences, and how to analyze them.

On the forefront of deep learning research is a technique called reinforcement learning, which bridges the gap between academic deep learning problems and ways in which learning occurs in nature in weakly supervised environments. This technique is heavily used when researching areas like learning how to walk, chase prey, navigate complex environments, and even play Go. This session will teach a neural network to play the video game Pong from just the pixels on the screen. No rules, no strategy coaching, and no PhD required. See all the sessions from Google I/O '18 here https://goo.gl/q1Tr8x

Like the course I just released on Hidden Markov Models, Recurrent Neural Networks are all about learning sequences - but whereas Markov Models are limited by the Markov assumption, Recurrent Neural Networks are not - and as a result, they are more expressive, and more powerful than anything we've seen on tasks that we haven't made progress on in decades. So what's going to be in this course and how will it build on the previous neural network courses and Hidden Markov Models? In the first section of the course we are going to add the concept of time to our neural networks. I'll introduce you to the Simple Recurrent Unit, also known as the Elman unit. We are going to revisit the XOR problem, but we're going to extend it so that it becomes the parity problem - you'll see that regular feedforward neural networks will have trouble solving this problem but recurrent networks will work because the key is to treat the input as a sequence.

This course is about the fundamental concepts of machine learning, facusing on neural networks. This topic is getting very hot nowadays because these learning algorithms can be used in several fields from software engineering to investment banking. Learning algorithms can recognize patterns which can help detect cancer for example. We may construct algorithms that can have a very very good guess about stock prices movement in the market. In the first chapter we are going to talk about the basics of the R programming language.

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

TekIQ is a seed stage startup developing AI based apps. We are currently looking for a Machine Learning / software engineer to develop proof of concept for a product idea. This will be an ideal position for someone looking to work on a technically interesting and challenging problem. The position also requires the ability to lead and direct the technology development. Interested applicant should have knowledge of and experience in working with speech recognition modules of commonly used ML libraries.

In SQL Server 2016, you have to the ability to integrate R for in-database, scalable machine learning. We will explore some of the challenges faced when pushing your algorithms and results to a consumable format. This is the first course in a series of courses to be released.

These articles are between 3 and 5 year old, but are still valuable today. The methodology used in these articles is modern, and still state-of-the-art today. Some discuss immense data sets still available to the public, and that resulted in designing new machine learning techniques to handle them. I am in the process of organizing these articles (written by myself) to eventually self-publish data science tutorials, in a few separate booklets, that are easy to understand for the layman with one year of data camp or college education in data science. The material will eventually be accessible to Data Science Central members, but not published in a traditional book. My writing style has evolved over time: I have moved away from writing academic papers long ago, to most recently share advanced knowledge in a way that is accessible to beginners, sometimes even ground-breaking material, such as this one.