Tensorflow is Google's library for deep learning and artificial intelligence. It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version. Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning. In other words, if you want to do deep learning, you gotta know Tensorflow.

It is compact and to the point giving you practical "templates" on how to apply different classes of DL algorithms in PyTorch. PyTorch is a Python framework developed by Facebook to develop and deploy Deep Learning models. It is one of the most popular Deep Learning frameworks nowadays. You will learn everything that is needed for developing and applying deep learning models to your own data. All relevant and state of the art model architectures will be covered.

You want to be able to perform your own data analyses with R? You want to learn how to get business-critical insights out of your data? Or you want to get a job in this amazing field? In all of these cases, you found the right course! We will start with the very Basics of R, like data types and -structures, programming of loops and functions, data im- and export. Then we will dive deeper into data analysis: we will learn how to manipulate data by filtering, aggregating results, reshaping data, set operations, and joining datasets.

Looking for a faster way to understand data issues and patterns, before you dive into the fun part of training your ML model? Wanna learn how to train better ML models, by finding and fixing issues in your data? You've come to the right place. In this article, you will learn how to do data exploration at the speed of light, using the amazing open-source library Sweetviz. Let's go through a hands-on example and code you can find in this GitHub repository.

Whether you're looking for information that will help you certify Google Cloud in machine learning, how to build deep learning model-based products, or the best data cleaning strategies and practices, you've come to the right place. First, examine the literature on industrial processes and their aftermath. The list may be helpful. You will be successful in achieving this objective. Key Features: --------------- Learn how to convert a deep learning model running on notebook environments into a production-ready application supporting various deployment environments.

We have witnessed in recent years an ever-growing volume of information becoming available in a streaming manner in various application areas. As a result, there is an emerging need for online learning methods that train predictive models on-the-fly. A series of open challenges, however, hinder their deployment in practice. These are, learning as data arrive in real-time one-by-one, learning from data with limited ground truth information, learning from nonstationary data, and learning from severely imbalanced data, while occupying a limited amount of memory for data storage. We propose the ActiSiamese algorithm, which addresses these challenges by combining online active learning, siamese networks, and a multi-queue memory. It develops a new density-based active learning strategy which considers similarity in the latent (rather than the input) space. We conduct an extensive study that compares the role of different active learning budgets and strategies, the performance with/without memory, the performance with/without ensembling, in both synthetic and real-world datasets, under different data nonstationarity characteristics and class imbalance levels. ActiSiamese outperforms baseline and state-of-the-art algorithms, and is effective under severe imbalance, even only when a fraction of the arriving instances' labels is available. We publicly release our code to the community.

Communication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of the messages or to the goal that the message exchange aims to achieve. Next generation systems, however, can be potentially enriched by folding message semantics and goals of communication into their design. Further, these systems can be made cognizant of the context in which communication exchange takes place, providing avenues for novel design insights. This tutorial summarizes the efforts to date, starting from its early adaptations, semantic-aware and task-oriented communications, covering the foundations, algorithms and potential implementations. The focus is on approaches that utilize information theory to provide the foundations, as well as the significant role of learning in semantics and task-aware communications.

Robust reinforcement learning (RL) considers the problem of learning policies that perform well in the worst case among a set of possible environment parameter values. In real-world environments, choosing the set of possible values for robust RL can be a difficult task. When that set is specified too narrowly, the agent will be left vulnerable to reasonable parameter values unaccounted for. When specified too broadly, the agent will be too cautious. In this paper, we propose Feasible Adversarial Robust RL (FARR), a novel problem formulation and objective for automatically determining the set of environment parameter values over which to be robust. FARR implicitly defines the set of feasible parameter values as those on which an agent could achieve a benchmark reward given enough training resources. By formulating this problem as a two-player zero-sum game, optimizing the FARR objective jointly produces an adversarial distribution over parameter values with feasible support and a policy robust over this feasible parameter set. We demonstrate that approximate Nash equilibria for this objective can be found using a variation of the PSRO algorithm. Furthermore, we show that an optimal agent trained with FARR is more robust to feasible adversarial parameter selection than with existing minimax, domain-randomization, and regret objectives in a parameterized gridworld and three MuJoCo control environments.

Machine learning technology is now so common that you probably use it dozens of times a day without even realizing it. And since it has so many applications, the job prospects are great for anyone with a lot of machine learning experience. We just released a machine learning course on the freeCodeCamp.org YouTube channel that is the perfect place to start your learning journey. Kylie Ying developed this course.

Linear Algebra is usually a prerequisite of machine learning. However, one doesn't need to know all the concepts in linear algebra. In this course, I have compiled together all the important linear algebra concepts that are most frequently used in machine learning. This is the content I taught at Polytechnique Montreal as a refresher on linear algebra for machine learning. Understanding these concepts will help you navigate through an introductory course in machine learning.