The Non-Programmers' Tutorial For Python 3 is a tutorial designed to be an introduction to the Python programming language. This guide is for someone with no programming experience. "The Coder's Apprentice" aims at teaching Python 3 to students and teenagers who are completely new to programming. Contrary to many of the other books that teach Python programming, this book assumes no previous knowledge of programming on the part of the students, and contains numerous exercises that allow students to train their programming skills. The book aims at striking the balance between a tutorial and reference book. Includes some fun exercises at the end! "A Byte of Python" is a free book on programming using the Python language. It serves as a tutorial or guide to the Python language for a beginner audience. If all you know about computers is how to save text files, then this is the book for you.

Bayesian optimisation presents a sample-efficient methodology for global optimisation. Within this framework, a crucial performance-determining subroutine is the maximisation of the acquisition function, a task complicated by the fact that acquisition functions tend to be non-convex and thus nontrivial to optimise. In this paper, we undertake a comprehensive empirical study of approaches to maximise the acquisition function. Additionally, by deriving novel, yet mathematically equivalent, compositional forms for popular acquisition functions, we recast the maximisation task as a compositional optimisation problem, allowing us to benefit from the extensive literature in this field. We highlight the empirical advantages of the compositional approach to acquisition function maximisation across 3958 individual experiments comprising synthetic optimisation tasks as well as tasks from Bayesmark. Given the generality of the acquisition function maximisation subroutine, we posit that the adoption of compositional optimisers has the potential to yield performance improvements across all domains in which Bayesian optimisation is currently being applied.

Convolutional Neural Networks This course is part of the Deep Learning Specialization Foundations of Convolutional Neural Networks. Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep ... About this Course 1,001,748 recent views This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. This is the fourth course of the Deep Learning Specialization.

Officially created in collaboration with the Google Flutter team. What you'll learn Build beautiful, fast, and native-quality apps with Flutter … What you'll be learning This tutorial uses an older version of Symfony… but since it's a JavaScript tutorial, the concepts … Master Linear Algebra What you'll learn Fundamentals … Online guide on how to learn GAMS like a Pro.

Great "keynote" from NeurIPS 2020 about the past, present and future of ML research -- especially as it pertains to algorithmic bias. It's 45 minutes but don't let that deter you. This is a well-paced presentation that's full of insightful conversations with researchers, engineers, and data scientists. The raw content of Jupyter Notebooks is a mix of dissimilar source code, Markdown, and HTML, making Jupyter notoriously challenging for code reviews. This tutorial shows how to use an open-source utility called nbautoexport to simplify the process.

On Wednesday, Dec. 16, at 4 pm, Hands-on Technology will host Code Your Own "A.I.," a virtual artificial intelligence-themed coding workshop. This class will cover the basics of "Machine Learning" as students learn to use artificial intelligence in their own code and game creations. One in a series of virtual "coding adventures" workshops, classes are perfect for beginner to intermediate level students. The workshop will also feature a "Careers & Innovations" component where the group will explore how artificial intelligence is involved in today's most interesting technology trends and careers. Registration for this Zoom workshop is available through the library's online events calendar at cplevents.org.

This course introduces principles, algorithms, and applications of machine learning from the point of view of modeling and prediction. It includes formulation of learning problems and concepts of representation, over-fitting, and generalization. These concepts are exercised in supervised learning and reinforcement learning, with applications to images and to temporal sequences. Learn how to perform supervised and reinforcement learning, with images and temporal sequences. This course includes lectures, lecture notes, exercises, labs, and homework problems.

MIT has posted online its introductory course on deep learning, which covers applications to computer vision, natural language processing, biology, and more. Students "will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow." Experience in Python is helpful but not necessary. The first lecture appears above. The rest of the course materials (videos & slides) can be found here.

Problems of cooperation--in which agents seek ways to jointly improve their welfare--are ubiquitous and important. They can be found at scales ranging from our daily routines--such as driving on highways, scheduling meetings, and working collaboratively--to our global challenges--such as peace, commerce, and pandemic preparedness. Arguably, the success of the human species is rooted in our ability to cooperate. Since machines powered by artificial intelligence are playing an ever greater role in our lives, it will be important to equip them with the capabilities necessary to cooperate and to foster cooperation. We see an opportunity for the field of artificial intelligence to explicitly focus effort on this class of problems, which we term Cooperative AI. The objective of this research would be to study the many aspects of the problems of cooperation and to innovate in AI to contribute to solving these problems. Central goals include building machine agents with the capabilities needed for cooperation, building tools to foster cooperation in populations of (machine and/or human) agents, and otherwise conducting AI research for insight relevant to problems of cooperation. This research integrates ongoing work on multi-agent systems, game theory and social choice, human-machine interaction and alignment, natural-language processing, and the construction of social tools and platforms. However, Cooperative AI is not the union of these existing areas, but rather an independent bet about the productivity of specific kinds of conversations that involve these and other areas. We see opportunity to more explicitly focus on the problem of cooperation, to construct unified theory and vocabulary, and to build bridges with adjacent communities working on cooperation, including in the natural, social, and behavioural sciences.

Spectral methods have emerged as a simple yet surprisingly effective approach for extracting information from massive, noisy and incomplete data. In a nutshell, spectral methods refer to a collection of algorithms built upon the eigenvalues (resp. singular values) and eigenvectors (resp. singular vectors) of some properly designed matrices constructed from data. A diverse array of applications have been found in machine learning, data science, and signal processing. Due to their simplicity and effectiveness, spectral methods are not only used as a stand-alone estimator, but also frequently employed to initialize other more sophisticated algorithms to improve performance. While the studies of spectral methods can be traced back to classical matrix perturbation theory and methods of moments, the past decade has witnessed tremendous theoretical advances in demystifying their efficacy through the lens of statistical modeling, with the aid of non-asymptotic random matrix theory. This monograph aims to present a systematic, comprehensive, yet accessible introduction to spectral methods from a modern statistical perspective, highlighting their algorithmic implications in diverse large-scale applications. In particular, our exposition gravitates around several central questions that span various applications: how to characterize the sample efficiency of spectral methods in reaching a target level of statistical accuracy, and how to assess their stability in the face of random noise, missing data, and adversarial corruptions? In addition to conventional $\ell_2$ perturbation analysis, we present a systematic $\ell_{\infty}$ and $\ell_{2,\infty}$ perturbation theory for eigenspace and singular subspaces, which has only recently become available owing to a powerful "leave-one-out" analysis framework.