Developed by Google Brain, TensorFlow is one of the most popular open-source libraries for numerical computation. This library helps in building and training deep neural network applications and offers APIs for beginners and experts to develop for desktop, mobile, web, and cloud. In this article, we list down 9 free tutorials to become a pro in the open-source machine learning framework, TensorFlow. In this official documentation, you will learn how to use machine learning techniques, utilise machine learning at production scale, creating and deploying TensorFlow models on the web and mobile, understanding TensorFlow's High-Level APIs and much more. In this tutorial, you will learn the basics and advance machine learning topics like Linear Regression, Classifiers, create, train and evaluate a neural network like CNN, RNN, autoencoders, etc.

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This document is an attempt to provide a summary of the mathematical background needed for an introductory class in machine learning, which at UC Berkeley is known as CS 189/289A. Our assumption is that the reader is already familiar with the basic concepts of multivariable calculus and linear algebra (at the level of UCB Math 53/54). We emphasize that this document is not a replacement for the prerequisite classes. Most subjects presented here are covered rather minimally; we intend to give an overview and point the interested reader to more comprehensive treatments for further details. Note that this document concerns math background for machine learning, not machine learning itself.

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Earlier this year, we announced TensorFlow 2.0 in alpha at the TensorFlow Dev Summit. Today, we're delighted to announce that the final release of TensorFlow 2.0 is now available! Learn how to install it here. TensorFlow 2.0 is driven by the community telling us they want an easy-to-use platform that is both flexible and powerful, and which supports deployment to any platform. TensorFlow 2.0 provides a comprehensive ecosystem of tools for developers, enterprises, and researchers who want to push the state-of-the-art in machine learning and build scalable ML-powered applications.

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T HE D IFFERENTIABLEC ROSS-E NTROPYM ETHOD Brandon Amos 1 Denis Y arats 12 1 Facebook AI Research 2 New Y ork University A BSTRACT We study the Cross-Entropy Method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant (DCEM) that enables us to differentiate the output of CEM with respect to the objective function's parameters. In the machine learning setting this brings CEM inside of the end-to-end learning pipeline where this has otherwise been impossible. We show applications in a synthetic energy-based structured prediction task and in non-convex continuous control. In this paper we focus on the setting of optimizing an unconstrained, non-convex, and continuous objective function f θ(x): R n Θ R as ˆ x arg min x f θ(x), where f is parameterized by θ Θ and has inputs x R n . If it exists, some (sub-)derivative θˆ x is useful in the machine learning setting to make the output of the optimization procedure end-to-end learnable. For example, θ could parameterize a predictive model that is generating potential outcomes conditional on x happening that you want to optimize over. End-to-end learning in these settings can be done by defining a loss function L on top of ˆ x and taking gradient steps θL . If f θ were convex this gradient is easy to analyze and compute when it exists and is unique (Gould et al., 2016; Johnson et al., 2016; Amos et al., 2017; Amos & Kolter, 2017). Unfortunately analyzing and computing a "derivative" through the non-convex arg min here is not as easy and is challenging in theory and practice. No such derivative may exist in theory, it might not be unique, and even if it uniquely exists, the numerical solver being used to compute the solution may not find a global or even local optimum of f . One promising direction to sidestep these issues is to approximate the arg min operation with an explicit optimization procedure that is interpreted as just another compute graph and unrolled through.

-- In a multi-agent setting, the optimal policy of a single agent is largely dependent on the behavior of other agents. We investigate the problem of multi-agent reinforcement learning, focusing on decentralized learning in non-stationary domains for mobile robot navigation. We identify a cause for the difficulty in training non-stationary policies: mutual adaptation to sub-optimal behaviors, and we use this to motivate a curriculum-based strategy for learning interactive policies. The curriculum has two stages. First, the agent leverages policy gradient algorithms to learn a policy that is capable of achieving multiple goals. Second, the agent learns a modifier policy to learn how to interact with other agents in a multi-agent setting. We evaluated our approach on both an autonomous driving lane-change domain and a robot navigation domain. Single agent reinforcement learning (RL) algorithms have made significant progress in game playing [20] and robotics [13], however, single agent learning algorithms in multi-agent settings are prone to learn stereotyped behaviors that over-fit to the training environment [22], [15]. There are several reasons why multi-agent environments are more difficult: 1) interacting with an unknown agent requires having either multiple responses to a given situation or a more nuanced ability to perceive differences. The former breaks the Markov assumption, the latter rules out simpler solutions which are likely to be found first.

A BSTRACT Reinforcement learning algorithms use correlations between policies and rewards to improve agent performance. But in dynamic or sparsely rewarding environments these correlations are often too small, or rewarding events are too infrequent to make learning feasible. Human education instead relies on curricula-the breakdown of tasks into simpler, static challenges with dense rewards-to build up to complex behaviors. While curricula are also useful for artificial agents, handcrafting them is time consuming. This has lead researchers to explore automatic curriculum generation. Here we explore automatic curriculum generation in rich, dynamic environments. Using a setter-solver paradigm we show the importance of considering goal validity, goal feasibility, and goal coverage to construct useful curricula. We demonstrate the success of our approach in rich but sparsely rewarding 2D and 3D environments, where an agent is tasked to achieve a single goal selected from a set of possible goals that varies between episodes, and identify challenges for future work. Finally, we demonstrate the value of a novel technique that guides agents towards a desired goal distribution. Altogether, these results represent a substantial step towards applying automatic task curricula to learn complex, otherwise unlearnable goals, and to our knowledge are the first to demonstrate automated curriculum generation for goal-conditioned agents in environments where the possible goals vary between episodes. 1 I NTRODUCTION Reinforcement learning (RL) algorithms use correlations between policies and environmental rewards to reinforce and improve agent performance. But such correlation-based learning may struggle in dynamic environments with constantly changing settings or goals, because policies that correlate with rewards in one episode may fail to correlate with rewards in a subsequent episode. Correlation-based learning may also struggle in sparsely rewarding environments since by definition there are fewer rewards, and hence fewer instances when policy-reward correlations can be measured and learned from. In the most problematic tasks, agents may fail to begin learning at all. While RL has been used to achieve expert-level performance in some sparsely rewarding games (Silver et al., 2016; OpenAI, 2018; Vinyals et al., 2019), success has often required carefully engineered curricula to bootstrap learning, such as learning from millions of expert games or handcrafted shaping rewards. In some cases self-play between agents as they improve can serve as a powerful automatic curriculum for achieving expert or superhuman performance (Silver et al., 2018; Vinyals et al., 2019).

Cooperative learning in heterogeneous teams refers to learning methods in which teams are organised both to accomplish academic tasks and for individuals to gain knowledge. Competencies, personality and the gender of team members are key factors that influence team performance. Here, we introduce a team composition problem, the so-called synergistic team composition problem (STCP), which incorporates such key factors when arranging teams. Thus, the goal of the STCP is to partition a set of individuals into a set of synergistic teams: teams that are diverse in personality and gender and whose members cover all required competencies to complete a task. Furthermore, the STCP requires that all teams are balanced in that they are expected to exhibit similar performances when completing the task. We propose two efficient algorithms to solve the STCP . Our first algorithm is based on a linear programming formulation and is appropriate to solve small instances of the problem. Our second algorithm is an anytime heuristic that is effective for large instances of the STCP . Finally, we thoroughly study the computational properties of both algorithms in an educational context when grouping students in a classroom into teams using actual-world data. Keywords: team composition, exact algorithms, heuristic algorithms, optimisation, coalition formation 1. Introduction Active learning refers to a broad range of teaching techniques that engage students to participate in all learning activities in the classes. Typically, active learning strategies involve a substantial amount of students working together within teams. They do not only acquire and retain the information better but also are more content with their classes [2]. Nevertheless, not all teams facilitate learning. For team-based learning to be effective, every team composed in the classroom needs to be heterogeneous, i.e. diverse in individuals' characteristics. Furthermore, having some significantly weaker teams and some significantly stronger teams is undesirable. Hence, the distribution of teams in a classroom must be balanced in the sense that all teams are more or less equally strong. Even though much research in the industrial, organisational, and educational psychology fields investigated what are the predictors of team success, to the best of our knowledge, there are no computational models to build teams for a given task that are broadly used in the classrooms. Frequently studied individual characteristics that influence team performance are competencies, personality traits, and gender [3, 4, 5, 6]. Some of those characteristics were also acknowledged by multiagent systems (MAS) research. The most studied characteristic in MAS research are competencies [7, 8, 9, 10, 11].