Deep reinforcement learning has gathered much attention recently. Impressive results were achieved in activities as diverse as autonomous driving, game playing, molecular recombination, and robotics. In all these fields, computer programs have taught themselves to solve difficult problems. They have learned to fly model helicopters and perform aerobatic manoeuvers such as loops and rolls. In some applications they have even become better than the best humans, such as in Atari, Go, poker and StarCraft. The way in which deep reinforcement learning explores complex environments reminds us of how children learn, by playfully trying out things, getting feedback, and trying again. The computer seems to truly possess aspects of human learning; this goes to the heart of the dream of artificial intelligence. The successes in research have not gone unnoticed by educators, and universities have started to offer courses on the subject. The aim of this book is to provide a comprehensive overview of the field of deep reinforcement learning. The book is written for graduate students of artificial intelligence, and for researchers and practitioners who wish to better understand deep reinforcement learning methods and their challenges. We assume an undergraduate-level of understanding of computer science and artificial intelligence; the programming language of this book is Python. We describe the foundations, the algorithms and the applications of deep reinforcement learning. We cover the established model-free and model-based methods that form the basis of the field. Developments go quickly, and we also cover advanced topics: deep multi-agent reinforcement learning, deep hierarchical reinforcement learning, and deep meta learning.

Domain classification is the fundamental task in natural language understanding (NLU), which often requires fast accommodation to new emerging domains. This constraint makes it impossible to retrain all previous domains, even if they are accessible to the new model. Most existing continual learning approaches suffer from low accuracy and performance fluctuation, especially when the distributions of old and new data are significantly different. In fact, the key real-world problem is not the absence of old data, but the inefficiency to retrain the model with the whole old dataset. Is it potential to utilize some old data to yield high accuracy and maintain stable performance, while at the same time, without introducing extra hyperparameters? In this paper, we proposed a hyperparameter-free continual learning model for text data that can stably produce high performance under various environments. Specifically, we utilize Fisher information to select exemplars that can "record" key information of the original model. Also, a novel scheme called dynamical weight consolidation is proposed to enable hyperparameter-free learning during the retrain process. Extensive experiments demonstrate that baselines suffer from fluctuated performance and therefore useless in practice. On the contrary, our proposed model CCFI significantly and consistently outperforms the best state-of-the-art method by up to 20% in average accuracy, and each component of CCFI contributes effectively to overall performance.

The ability to recover from an unexpected external perturbation is a fundamental motor skill in bipedal locomotion. An effective response includes the ability to not just recover balance and maintain stability but also to fall in a safe manner when balance recovery is physically infeasible. For robots associated with bipedal locomotion, such as humanoid robots and assistive robotic devices that aid humans in walking, designing controllers which can provide this stability and safety can prevent damage to robots or prevent injury related medical costs. This is a challenging task because it involves generating highly dynamic motion for a high-dimensional, non-linear and under-actuated system with contacts. Despite prior advancements in using model-based and optimization methods, challenges such as requirement of extensive domain knowledge, relatively large computational time and limited robustness to changes in dynamics still make this an open problem. In this thesis, to address these issues we develop learning-based algorithms capable of synthesizing push recovery control policies for two different kinds of robots : Humanoid robots and assistive robotic devices that assist in bipedal locomotion. Our work can be branched into two closely related directions : 1) Learning safe falling and fall prevention strategies for humanoid robots and 2) Learning fall prevention strategies for humans using a robotic assistive devices. To achieve this, we introduce a set of Deep Reinforcement Learning (DRL) algorithms to learn control policies that improve safety while using these robots.

Capturing and simulating intelligent adaptive behaviours within spatially explicit individual-based models remains an ongoing challenge for researchers. While an ever-increasing abundance of real-world behavioural data are collected, few approaches exist that can quantify and formalise key individual behaviours and how they change over space and time. Consequently, commonly used agent decision-making frameworks, such as event-condition-action rules, are often required to focus only on a narrow range of behaviours. We argue that these behavioural frameworks often do not reflect real-world scenarios and fail to capture how behaviours can develop in response to stimuli. There has been an increased interest in Machine Learning methods and their potential to simulate intelligent adaptive behaviours in recent years. One method that is beginning to gain traction in this area is Reinforcement Learning (RL). This paper explores how RL can be applied to create emergent agent behaviours using a simple predator-prey Agent-Based Model (ABM). Running a series of simulations, we demonstrate that agents trained using the novel Proximal Policy Optimisation (PPO) algorithm behave in ways that exhibit properties of real-world intelligent adaptive behaviours, such as hiding, evading and foraging.

We demonstrate that a neural network pre-trained on text and fine-tuned on code solves Mathematics problems by program synthesis. We turn questions into programming tasks, automatically generate programs, and then execute them, perfectly solving university-level problems from MIT's large Mathematics courses (Single Variable Calculus 18.01, Multivariable Calculus 18.02, Differential Equations 18.03, Introduction to Probability and Statistics 18.05, Linear Algebra 18.06, and Mathematics for Computer Science 6.042), Columbia University's COMS3251 Computational Linear Algebra course, as well as questions from a MATH dataset (on Prealgebra, Algebra, Counting and Probability, Number Theory, and Precalculus), the latest benchmark of advanced mathematics problems specifically designed to assess mathematical reasoning. We explore prompt generation methods that enable Transformers to generate question solving programs for these subjects, including solutions with plots. We generate correct answers for a random sample of questions in each topic. We quantify the gap between the original and transformed questions and perform a survey to evaluate the quality and difficulty of generated questions. This is the first work to automatically solve, grade, and generate university-level Mathematics course questions at scale. This represents a milestone for higher education.

The NVIDIA Deep Learning Institute will collaborate with UF on developing new curriculum and coursework for both students and the community, including programing tuned to address the needs of young adults and teens to encourage their interest in STEM and AI, better preparing them for future educational and employment opportunities. UF will become the site of the latest NVIDIA AI Technology Center, where UF Graduate Fellows and NVIDIA employees will work together to advance AI. Establishing UF's Equitable AI program, led by Dr. Juan Gilbert, Department of Computer & Information Science & Engineering. The effort is convening faculty members across the university to create standards and certifications in developing tools and solutions that are cognizant of bias, unethical practice and legal and moral issues. The NVIDIA Deep Learning Institute will collaborate with UF on developing new curriculum and coursework for both students and the community, including programing tuned to address the needs of young adults and teens to encourage their interest in STEM and AI, better preparing them for future educational and employment opportunities.

Description 2021 Python for Machine Learning & Data Science Masterclass is the name of a training course in which data science and machine learning using Python are discussed. This course includes Numpy, Pandas, Matplotlib and Scikit-Learn training. This is one of the most complete courses in data science and machine learning on the Internet. After teaching more than 2 million learners, the instructor of this course has collected items over a year that he believes is the best way to teach zero. This comprehensive course is designed to be at the bootcamps level, which usually costs thousands of dollars.

Data scientists are in demand, there are no two ways about it. The jobs pay well, there are plenty of openings available, and the industry only appears to be growing in this post-pandemic digital world. It should come as no surprise then that data science students are also a growing sector of the world labor force. But learning data science is not easy. I remember my own experience trying to go from a data-savvy academic researcher to an industry data science professional.

As part of its annual market performance review, global software giant, Wondershare Technology is releasing a summary of major milestones achieved in 2021, including over 100 million software downloads, strong product adoption, successful social campaigns, and awards from globally renowned brands such as CES 2021, G2 Crowd, and Shorty Awards. Wondershare's recognitions for the year begin with a record-breaking 100 million software downloads and 1200 product upgrades, while the acquisition and integration of Ufoto into its fold deepened its penetration into the fast-growing photo and video editing segment. Underlining Wondershare's commitment to championing creativity and productivity were several social media events such as the Back-to-School campaign to engage students and educators, #WondershareBFF to incentivize creators to showcase their most precious friendship moments, and the explosively viral #WondershareChallenge that garnered 2.8 million impressions and 20,000 engagements. Topping off a very successful year were awards and special recognition from respected consumer brands, such as the "Future Tech Award" for "Best Creative Software Suite" from CES 2021, the Shorty Award for Wondershare Filmora X – Winner and Audience Honoree for Best in Photo and Video, and recognition as a "Leader" and "High Performer" at the G2 Crowd Fall 2021 Awards. Wondershare Filmora – A user-friendly video editing software that is hugely popular with the new generation of social media video creators and professionals alike.

By the end of the course you will have a comprehensive overview of the entire research, development and deployment lifecycle of a machine learning model, and understood the best coding practices, and things to consider to put a model in production. You will also have a better understanding of the tools available to you to deploy your models, and will be well placed to take the deployment of the models in any direction that serves the needs of your organization. What else should you know? This course will help you take the first steps towards putting your models in production. You will learn how to go from a Jupyter notebook to a fully deployed machine learning model, considering CI/CD, and deploying to cloud platforms and infrastructure. But, there is a lot more to model deployment, like model monitoring, advanced deployment orchestration with Kubernetes, and scheduled workflows with Airflow, as well as various testing paradigms such as shadow deployments that are not covered in this course.