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Reinforcement Learning (RL) is a subset of machine learning. In the RL training method, desired actions are rewarded, and undesired actions are punished. In general, an RL agent can understand and interpret its environment, take actions, and also learn through trial and error. Deep Reinforcement Learning (Deep RL) is also a subfield of machine learning. In Deep RL, intelligent machines and software are trained to learn from their actions in the same way that humans learn from experience. That is, Deep RL blends RL techniques with Deep Learning (DL) strategies. Deep RL has the capability to solve complex problems that were unmanageable by machines in the past. Therefore, the potential applications of Deep RL in various sectors such as robotics, medicine, finance, gaming, smart grids, and more are enormous. The phenomenal ability of Artificial Neural Networks (ANNs) to process unstructured information fast and learn like a human brain is starting to be exploited only now. We are only in the initial stages of seeing the full impact of the technology that combines the power of RL and ANNs. This latest technology has the potential to revolutionize every sphere of commerce and science. How Is This Course Different?In this detailed Learning by Doing course, each new theoretical explanation is followed by practical implementation. This course offers you the right balance between theory and practice. Six projects have been included in the course curriculum to simplify your learning. The focus is to teach RL and Deep RL to a beginner. Hence, we have tried our best to simplify things. The course ‘A Complete Guide to Reinforcement & Deep Reinforcement Learning' reflects the most in-demand workplace skills. The explanations of all the theoretical concepts are clear and concise. The instructors lay special emphasis on complex theoretical concepts, making it easier for you to understand them. The pace of the video presentation is neither fast nor slow. It's perfect for learning. You will understand all the essential RL and Deep RL concepts and methodologies. The course is:• Simple and easy to learn.• Self-explanatory.• Highly detailed.• Practical with live coding.• Up-to-date covering the latest knowledge of this field. As this course is an exhaustive compilation of all the fundamental concepts, you will be motivated to learn RL and Deep RL. Your learning progress will be quick. You are certain to experience much more than what you learn. At the end of each new concept, a revision task such as Homework/activity/quiz is assigned. The solutions for these tasks are also provided. This is to assess and promote your learning. The whole process is closely linked to the concepts and methods you have already learned. A majority of these activities are coding-based, as the goal is to prepare you for real-world implementations. In addition to high-quality video content, you will also get access to easy-to-understand course material, assessment questions, in-depth subtopic notes, and informative handouts in this course. You are welcome to contact our friendly team in case of any queries related to the course, and we assure you of a prompt response. The course tutorials are subdivided into 145+ short HD videos. In every video, you'll learn something new and fascinating. In addition, you'll learn the key concepts and methodologies of RL and Deep RL, along with several practical implementations. The total runtime of the course videos is 14+ hours. Why Should You Learn RL & Deep RL?RL and Deep RL are the hottest research topics in the Artificial Intelligence universe. Reinforcement learning (RL) is a subset of machine learning concerned with the actions that intelligent agents need to take in an environment in order to maximize the reward. RL is one of three essential machine learning paradigms, besides supervised learning and unsupervised learning. Let's look at the next hot research topic. Deep Reinforcement Learning (Deep RL) is a subset of machine learning that blends Reinforcement Learning (RL) and Deep Learning (DL). Deep RL integrates deep learning into the solution, permitting agents to make decisions from unstructured input data without human intervention. Deep RL algorithms can take in large inputs (e.g., every pixel rendered to the user's screen in a video game) and determine the best actions to perform to optimize an objective (e.g., attain the maximum game score). Deep RL has been used for an assortment of applications, including but not limited to video games, oil & gas, natural language processing, computer vision, retail, education, transportation, and healthcare. Course Content:The comprehensive course consists of the following topics:1. Introductiona. Motivationi. What is Reinforcement Learning?ii. How is it different from other Machine Learning Frameworks?iii. History of Reinforcement Learningiv. Why Reinforcement Learning?v. Real-world examplesvi. Scope of Reinforcement Learningvii. Limitations of Reinforcement Learningviii. Exercises and Thoughtsb. Terminologies of RL with Case Studies and Real-World Examplesi. Agentii. Environmentiii. Actioniv. Statev. Transitionvi. Rewardvii. Quiz/Solutionviii. Policyix. Planningx. Exercises and Thoughts2. Hands-on to Basic Conceptsa. Naïve/Random Solutioni. Intro to gameii. Rules of the gameiii. Setupsiv. Implementation using Pythonb. RL-based Solutioni. Intro to Q Tableii. Dry Run of statesiii. How RL worksiv. Implementing RL-based solution using Pythonv. Comparison of solutionsvi. Conclusion3. Different types of RL Solutionsa. Hyper Parameters and ConceptsI. Intro to EpsilonII. How to update epsilonIII. Quiz/SolutionIV. Gamma, Discount FactorV. Quiz/SolutionVI. Alpha, Learning RateVII. Quiz/SolutionVIII. Do's and Don'ts of AlphaIX. Q Learning EquationX. Optimal Value for number of EpisodesXI. When to Stop Trainingb. Markov Decision Processi. Agent-environment interactionii. Goalsiii. Returnsiv. Episodesv. Value functionsvi. Optimization of policyvii. Optimization of the value functionviii. Approximationsix. Exercises and Thoughtsc. Q-Learningi. Intro to QLii. Equation Explanationiii. Implementation using Pythoniv. Off-Policy Learningd. SARSAi. Intro to SARSAii. State, Action, Reward, State, Actioniii. Equation Explanationiv. Implementation using Pythonv. On-Policy Learninge. Q-Learning vs. SARSAi. Difference in Equationii. Difference in Implementationiii. Pros and Consiv. When to use SARSAv. When to use Q Learningvi. Quiz/Solution4. Mini Project Using the Above Concepts (Frozen Lake)a. Intro to GYMb. Gym Environmentc. Intro to Frozen Lake Gamed. Rulese. Implementation using Pythonf. Agent Evaluationg. Conclusion5. Deep Learning/Neural Networksa. Deep Learning Frameworki. Intro to Pytorchii. Why Pytorch?iii. Installationiv. Tensorsv. Auto Differentiationvi. Pytorch Practiceb. Architecture of DNNi. Why DNN?ii. Intro to DNNiii. Perceptroniv. Architecturev. Feed Forwardvi. Quiz/Solutionvii. Activation Functionviii. Loss Functionix. Gradient Descentx. Weight Initializationxi. Quiz/Solutionxii. Learning Ratexiii. Batch Normalizationxiv. Optimizationsxv. Dropoutxvi. Early Stoppingc. Implementing DNN for CIFAR Using Python6. Deep RL / Deep Q Network (DQN)a. Getting to DQNi. Intro to Deep Q Networkii. Need of DQNiii. Basic Conceptsiv. How DQN is related to DNNv. Replay Memoryvi. Epsilon Greedy Strategyvii. Quiz/Solutionviii. Policy Networkix. Target Networkx. Weights Sharing/Target updatexi. Hyper-parametersb. Implementing DQNi. DQN Project - Cart and Pole using Pytorchii. Moving Averagesiii. Visualizing the agentiv. Performance Evaluation7. Car Racing Projecta. Intro to gameb. Implementation using DQN8. Trading Projecta. Stable Baselineb. Trading Bot using DQN9. Interview PreparationSuccessful completion of this course will enable you to:● Relate the concepts and practical applications of Reinforcement and Deep Reinforcement Learning with real-world problems● Apply for the jobs related to Reinforcement and Deep Reinforcement Learning ● Work as a freelancer for jobs related to Reinforcement and Deep Reinforcement Learning● Implement any project that requires Reinforcement and Deep Reinforcement Learning knowledge from scratch● Extend or improve the implementation of any other project for performance improvement● Know the theory and practical aspects of Reinforcement and Deep Reinforcement LearningWho Should Take the Course:Beginners who know absolutely nothing about Reinforcement and Deep Reinforcement LearningPeople who want to develop intelligent solutionsPeople who love to learn the theoretical concepts first before implementing them using PythonPeople who want to learn PySpark along with its implementation in realistic projectsMachine Learning or Deep Learning LoversAnyone interested in Artificial IntelligenceWhat You'll Learn:Fundamental concepts and methodologies of Reinforcement Learning (RL) and Deep Reinforcement Learning (Deep RL)Theoretical knowledge and practical implementation of RL and Deep RLSix projects to reinforce your learning and apply it to real-world scenariosThe latest knowledge and developments in the field of RL and Deep RLWhy This Course:Detailed Learning by Doing approach with practical implementation following each theoretical explanationBalance between theory and practiceClear and concise explanations of complex theoretical conceptsQuizzes, homework, and activities to assess and promote learningSubdivided into 145+ short HD videos with 14+ hours of runtimeComprehensive course materials, subtopic notes, and informative handoutsFriendly team support for any course-related questionsList of Keywords:Reinforcement LearningDeep Reinforcement LearningArtificial Neural NetworksMachine LearningPySparkIntelligent AgentsPractical ImplementationReal-World ApplicationsProjectsHands-On LearningTheoretical ConceptsPython ProgrammingArtificial IntelligenceEpsilon Greedy StrategyHyper-parametersDeep Q Network (DQN)Cart and PoleReady to Master Reinforcement and Deep Reinforcement Learning? Enroll Now and Dive into the Exciting World of AI!

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