Machine Learning is so pervasive today that you probably use it dozens of times a day without knowing it. In this class, you will learn about the most effective Machine Learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems.

This paper introduces the Behaviour Suite for Reinforcement Learning, or bsuite for short. bsuite is a collection of carefully-designed experiments that investigate core capabilities of reinforcement learning (RL) agents with two objectives. First, to collect clear, informative and scalable problems that capture key issues in the design of general and efficient learning algorithms. Second, to study agent behaviour through their performance on these shared benchmarks. To complement this effort, we open source github.com/deepmind/bsuite, which automates evaluation and analysis of any agent on bsuite. This library facilitates reproducible and accessible research on the core issues in RL, and ultimately the design of superior learning algorithms. Our code is Python, and easy to use within existing projects. We include examples with OpenAI Baselines, Dopamine as well as new reference implementations. Going forward, we hope to incorporate more excellent experiments from the research community, and commit to a periodic review of bsuite from a committee of prominent researchers.

This video is part of an online course, End-to-End Machine Learning with Tensorflow from Google Cloud. About this course: In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization. One of the best ways to review something is to work with the concepts and technologies that you have learned.

How can we protect, restore and promote sustainable operations and reduce our ecological footprint in both the private and public sectors? "AI is the easiest way to help the environment," claim experts in the first episode of the Analytics4Society podcast series. Listen in to learn more about the background to this claim. Experts also discuss how AI can help us enable 93% of the environmental goals outlined within the UN's 17 sustainable development goals. These goals provide a shared global blueprint to achieve a better and more sustainable future for all.

India ranks amongst the top three countries in the world in Artificial Intelligence (AI) skills, among top three in AI research, top nine when it comes to AI specialists, and top 20 on the AI Readiness Index. The demand for advanced AI-related technologies has the potential to unlock a trillion-dollar opportunity for India. Intel recognises that the complexity of bringing AI from model to real-world deployment takes a mix of established and novel hardware and software solutions and is committed to collaborating with the AI community to accelerate discoveries and make meaningful progress in how it uses AI to add value to our work and lives. Intel AI DevCamp (Intel AIDC) is one such effort to put AI tools into the hands of scientists, developers, analysts, and engineers. The event also serves as a showcase of innovation being driven in collaboration with industry leaders.

The concepts of risk-aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. Statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distributionally robust optimization (DRO), has recently received significant attention in both the operations research and statistical learning communities. This paper surveys main concepts and contributions to DRO, and its relationships with robust optimization, risk-aversion, chance-constrained optimization, and function regularization.

Here is a short and useful Review of Machine Learning Course A-Z: Hands-On Python & R in Data Science. This course potentiality brings you to build your successful career in data science. This is one of the Best Selling courses on Udemy where over 278,991 students enrolled and have a 4.4-star rating with 49,079 reviews. With this Best Machine Learning tutorial, you will learn to create Machine Learning Algorithms in both Python and R from Data Science experts. Kirill Eremenko is a data science coach and lifestyle entrepreneur and an aspiring Data Scientist & Forex Systems Expert with 4.5 average rating and 97,916 reviews.

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images.

From market research to direct mail metrics to web analytics to Big Data, the job of "marketing" has changed dramatically over time. We have arrived at a fundamental shift in marketing that is as impactful as the advent of the Internet: Artificial Intelligence and Machine Learning. This workshop introduces marketing professionals of all ranks to the theory, the language and the practical application of these disruptive technologies. This workshop will not teach you how to be a data scientist. It will teach you enough about the language and implications to speak cogently with your colleagues, and determine where to apply this innovative technology first.

Variational inference is becoming more and more popular for approximating intractable posterior distributions in Bayesian statistics and machine learning. Meanwhile, a few recent works have provided theoretical justification and new insights on deep neural networks for estimating smooth functions in usual settings such as nonparametric regression. In this paper, we show that variational inference for sparse deep learning retains the same generalization properties than exact Bayesian inference. In particular, we highlight the connection between estimation and approximation theories via the classical bias-variance trade-off and show that it leads to near-minimax rates of convergence for H\"older smooth functions. Additionally, we show that the model selection framework over the neural network architecture via ELBO maximization does not overfit and adaptively achieves the optimal rate of convergence.