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Free Download The Deep Reinforcement Learning Guide to Connect Four Published: 3/2025 Created by: Alex Palioudakis MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Level: Intermediate | Genre: eLearning | Language: English | Duration: 57 Lectures ( 13h 58m ) | Size: 4.4 GB Master Deep Reinforcement Learning by building an AI agent from scratch to excel at Connect Four. What you'll learn Implement Tic-tac-toe and Connect Four in Python from scratch Understand the foundations of Reinforcement Learning (RL) Understand the basics of Deep Learning (DL) Handle the challenges combining RL and Dl into Deep Reinforcement Learning (DRL) Implement an artifical intelligent (AI) agent that plays Connect Four using DRL Develop AI agents for simple and complex games Build and optimize AI models in Python Understand heuristic approaches in implementing bots for games Requirements Basic programming knowledge: Familiarity with Python will help you follow along with the coding examples and exercises. However, we have a quick Python refresher with all the concepts that we will use later. Basic understanding of machine learning concepts: Some exposure to general machine learning principles is helpful but not required (like linear regression, training models, understanding data, etc). Familiarity with development tools: Having basic experience using an IDE (e.g., PyCharm) or Jupyter notebooks can streamline the coding process, but we will guide you on how to set these up and how to use them. For beginners, don't worry! We'll cover the essential concepts in reinforcement learning and neural networks, and you'll be able to follow along even with limited prior experience. No specialized equipment is required beyond a computer capable of running Python which we'll help you set up. Description Are you ready to elevate your AI skills by mastering Deep Reinforcement Learning (DRL) through an exciting project? Embark on a comprehensive journey into the world of DRL with our meticulously designed course, "The Deep Reinforcement Learning Guide to Connect Four." This course is tailored to guide you from foundational concepts to advanced applications, culminating in the creation of a proficient DRL player for the game of Connect Four.Course Highlights:Foundations of Reinforcement Learning: Begin with an in-depth exploration of tabular reinforcement learning using the classic game of Tic-Tac-Toe. Understand the core principles and methodologies that form the bedrock of Reinforcement Learning (RL).Transition to Complex Environments: Progress to the more intricate game of Connect Four, where you'll learn to implement heuristics to navigate the limitations of tabular methods.Introduction to Neural Networks: Dive into the realm of neural networks, focusing on their role as value approximation functions. You'll gain hands-on experience by constructing a neural network library from scratch using only NumPy, demystifying the mechanics behind these powerful models.Building a DRL Player: In the culminating chapter, integrate all acquired knowledge to develop a Deep Reinforcement Learning player for Connect Four. Despite utilizing a straightforward architecture with dense layers, your DRL agent will exhibit impressive gameplay capabilities.Why Enroll?Comprehensive Curriculum: Our course offers a structured learning path, ensuring a solid grasp of both theoretical concepts and practical implementations.Hands-On Projects: Engage in a project that uses deep reinforce learning and provide tangible outcomes, enhancing your portfolio.Expert Guidance: Benefit from clear, concise explanations and step-by-step instructions, making complex topics accessible.Who Should Enroll?This course is ideal for:Aspiring AI and machine learning enthusiasts seeking to delve into reinforcement learning.Developers aiming to enhance their skill set with advanced DRL techniques.Anyone with a passion for understanding the intricacies of AI through practical applications.Join us in this educational adventure and equip yourself with the skills to design and implement sophisticated DRL agents from the ground up. Enroll now to start your journey in building advanced AI agents! Who this course is for This course is designed for anyone interested in learning how to build AI agents using deep reinforcement learning. Aspiring AI developers: If you want to learn how to create AI models that can play games, make decisions, and optimize strategies, this course will guide you through step by step. Software developers and engineers: If you're looking to expand your skill set into AI, neural networks, and reinforcement learning, this course will help you build a solid foundation. Data scientists and machine learning enthusiasts: Those with an interest in reinforcement learning and its applications to game-playing AI, such as Tic-Tac-Toe and Connect Four, will find this course practical and engaging. Students and academics: If you're studying computer science, machine learning, or AI, this course offers practical examples and hands-on coding experience to reinforce theoretical knowledge. Beginners to AI and neural networks: Even if you have limited experience with AI or Python programming, this course is designed to ease you into these topics and help you understand how AI agents learn through reinforcement learning. Whether you are looking to strengthen your knowledge of AI algorithms or explore how deep reinforcement learning can be applied to real-world scenarios, this course provides valuable insights and practical experience. 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Free Download Ingress Controller Deep Dive In Aks | Ingress | Ssl | Tls Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 3.27 GB | Duration: 4h 9m How do we access my URL of the Application in Kubernetes Production Env and Make Sure Running over HTTPS What you'll learn Which Services Mostly Uses in Kubernetes Production Env How Traffic Flow from Inside Pod to outside Network Where we have Define URL of Application so that I Can access my Application Using URL How do We Make Sure Your Application Running over HTTPS How do We Generate SSL or TLS Certificate How do we Import TLS Certificate in Application Requirements No Programming Language Required Description In this Course i have covered Generally Which services uses in Kubernetes Production Environement to Access the application.I have deployed one e-commerce website and access through Nginx Ingress Controller. I have setup ingress from Basic and then go to advanced level.What i did in my Real time project same process i have covered and also added few common troubleshooting steps i have added so that you can also get real time exposure on Kubernetes and will be more confident during interviws.Below are the Common Interview Qs by Interviewer1 - Tell me what type of services and Which services Generally used in Kubernetes production env2 - How do we secure your Application URL3 - How Network Traffic Flow from Inside Pod to Outside Cluster, 4 - Explain Architecture of Network Traffic Flow in KubernetesWhich Services Mostly Uses in Kubernetes Production EnvHow Traffic Flow from Inside Pod to outside NetworkWhere we have Define URL of Application so that I Can access my Application Using URL ( www webapprouting.kubernetes azureHow do We Make Sure Your Application Running over HTTPSHow do We Generate SSL or TLS CertificateHow do we Import TLS Certificate in ApplicationWhat is Helm and Helm ChartWhat is Nginx Ingress ControllerHow do we Deploy Nginx Ingress Controller Using Helm ChartWhat's Is Ingress Object, where We have Define URL of Application in Ingress What is Rollback in helm and how do we rollback to Previous VersionHow Do we Access Application by using URL of Application Overview Section 1: Introduction Lecture 1 What is Helm _ How to deploy Application Using Helm _ Part -01 Lecture 2 How to Install Particular Version of Application and How to Upgrade A Lecture 3 How to Upgrade Application and RollBack to Previous Version _ Part -03 Lecture 4 Deploy Jenkins Using HELM Chart and customization of Value Yaml file _ Part -04 Lecture 5 How do We RollBack to Application Using Helm Chart and SSL Certificate Lecture 6 How Do We Generate SSL certificate and TLS Certificate to use in Application Section 2: Troubleshooting Related to Application URL down | Common Use Cases Lecture 7 My Application URL not working How to troubleshoot this issue Begineer Homepage https://www.udemy.com/course/ingress-controller-deep-dive-in-aks-ingress-ssl-tls/ Rapidgator https://rg.to/file/46c4fc8055d32a14990c6a128dae274f/ivzkv.Ingress.Controller.Deep.Dive.In.Aks..Ingress..Ssl..Tls.part3.rar.html https://rg.to/file/7afc0222c38de6a22639d550fdef06b7/ivzkv.Ingress.Controller.Deep.Dive.In.Aks..Ingress..Ssl..Tls.part2.rar.html https://rg.to/file/8b89570be40a40466354d7c87a98fdf9/ivzkv.Ingress.Controller.Deep.Dive.In.Aks..Ingress..Ssl..Tls.part4.rar.html https://rg.to/file/c7c40c1bff6c2202755d266c85ba7825/ivzkv.Ingress.Controller.Deep.Dive.In.Aks..Ingress..Ssl..Tls.part1.rar.html Fikper Free Download https://fikper.com/0tap8Hv3o5/ivzkv.Ingress.Controller.Deep.Dive.In.Aks..Ingress..Ssl..Tls.part4.rar.html https://fikper.com/LhONCsu9MW/ivzkv.Ingress.Controller.Deep.Dive.In.Aks..Ingress..Ssl..Tls.part1.rar.html https://fikper.com/gW2QaeV2GS/ivzkv.Ingress.Controller.Deep.Dive.In.Aks..Ingress..Ssl..Tls.part3.rar.html https://fikper.com/s071X7XOGx/ivzkv.Ingress.Controller.Deep.Dive.In.Aks..Ingress..Ssl..Tls.part2.rar.html No Password - Links are Interchangeable

Free Download Distributed Deep Learning With Jax Large-Scale Training Published 10/2024 MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz Language: English | Size: 232.64 MB | Duration: 0h 30m Implement Multi-Device Parallel Training Strategies for Efficient Large Model Training - FSDP/TP/PP/DP What you'll learn Distributed Training of Large Scale Machine Learning Models How to parallelize models over workers (tensor, pipeline, data, sequence) Collective communication in training large scale models Using Jax and XLA to distribute models over thousands of workers Requirements Intermediate Python, Entry level Machine Learning knowledge, Some Jax knowledge preferred Description Master the art and science of distributed training for GPT-style language models in this comprehensive course using JAX and XLA. Learn how to scale up autoregressive transformer training from scratch, implementing the same techniques used by leading AI labs to train large language models efficiently across multiple devices.What You'll Learn:Distributed Training FundamentalsUnderstanding distributed computing for language modelsJAX's transformation and parallelization primitivesXLA (Accelerated Linear Algebra) compilation optimizationMastering collective communication primitives: • All-reduce for gradient aggregation • All-gather for distributed data collection • Broadcast for parameter synchronization • Scatter/Gather for efficient token distributionAdvanced Parallelization Strategies for GPTTensor Parallelism: Splitting attention heads and MLP layersPipeline Parallelism: Optimal transformer layer distributionSequence Parallelism: Handling long sequences efficientlyData Parallelism: Scaling batch processingHybrid parallelism approaches specific to GPT modelsGSPMD and Automated ShardingImplementing GSPMD for GPT model componentsAutomatic sharding strategies for attention layersOptimizing communication patterns for autoregressive modelsCustom sharding annotations for transformer blocksPractical ImplementationBuilding distributed GPT architectures from scratchEfficient parameter sharding and synchronizationHandling distributed attention computationManaging causal masks in distributed settingsImplementing efficient parameter serversPerformance Optimization and DebuggingProfiling distributed GPT trainingOptimizing attention computationImplementation of gradient checkpointingMemory optimization techniquesHandling numerical stability in distributed settingsHands-on Projects:Implement a mini-GPT from scratch with distributed trainingBuild hybrid parallelism strategies for GPT trainingCreate custom sharding strategies using GSPMDOptimize collective communication patterns for transformer blocksDeploy and manage distributed GPT training across multiple nodesWho This Course is For:Machine Learning Engineers working on language modelsDeep Learning Researchers scaling up GPT-style modelsSoftware Engineers transitioning to large-scale MLAnyone interested in training large language modelsPrerequisites:Strong Python programming skillsBasic understanding of transformer architecturesFamiliarity with language modeling conceptsBasic linear algebra and calculusExperience with any deep learning frameworkBy the end of this course, you'll understand how to implement and scale GPT model training across distributed systems. You'll master the intricacies of parallel training strategies, communication primitives, and optimization techniques specific to large language models. Overview Section 1: Introduction Lecture 1 Introduction Lecture 2 Introduction to Mesh Lecture 3 Collective Communication Lecture 4 Data Parallelism Lecture 5 FSDP (Fully Sharded Data Parallel) Lecture 6 FSDP+TP (Fully Sharded Data Parallel plus Tensor Parallel Lecture 7 Pipeline Parallel Anyone who wants to learn how to parallelize and scale machine learning models over thousands of workers using Jax and XLA. Screenshot Homepage https://www.udemy.com/course/distributed-deep-learning-with-jax-large-scale-training/ Rapidgator https://rg.to/file/4bc44421b08ac8b1f92285285b93c872/jpwdm.Distributed.Deep.Learning.With.Jax.LargeScale.Training.rar.html Fikper Free Download https://fikper.com/DeMh4x1gNJ/jpwdm.Distributed.Deep.Learning.With.Jax.LargeScale.Training.rar.html No Password - Links are Interchangeable

Free Download Deep Reinforcement Learning made-easy Published 10/2024 Created by Dr. Muhammad Farhan MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch Genre: eLearning | Language: English | Duration: 113 Lectures ( 14h 40m ) | Size: 8.62 GB Reinforcement Learning for beginners to advanced learners What you'll learn To understand deep learning and reinforcement learning paradigms To understand Architectures and optimization methods for deep neural network training To implement deep learning methods within Tensor Flow and apply them to data To understand the theoretical foundations and algorithms of reinforcement learning To apply reinforcement learning algorithms to environments with complex dynamics Requirements Basic python programming but not necessary Description This course is the integration of deep learning and reinforcement learning. The course will introduce student with deep neural networks (DNN) starting from simple neural networks (NN) to recurrent neural network and long-term short-term memory networks. NN and DNN are the part of reinforcement learning (RL) agent so the students will be explained how to design custom RL environments and use them with RL agents. After the completion of the course the students will be able:To understand deep learning and reinforcement learning paradigmsTo understand Architectures and optimization methods for deep neural network trainingTo implement deep learning methods within Tensor Flow and apply them to data.To understand the theoretical foundations and algorithms of reinforcement learning.To apply reinforcement learning algorithms to environments with complex dynamics.Course Contents:Introduction to Deep Reinforcement LearningArtificial Neural Network (ANN)ANN to Deep Neural Network (DNN)Deep Learning Hyperparameters: RegularizationDeep Learning Hyperparameters: Activation Functions and OptimizationsConvolutional Neural Network (CNN)CNN ArchitectureRecurrent Neural Network (RNN)RNN for Long SequencesLSTM NetworkOverview of Markov Decision ProcessesBellman Equations and Value FunctionsDeep Reinforcement Learning with Q-LearningModel-Free PredictionDeep Reinforcement Learning with Policy GradientsExploration and Exploitation in Reinforcement Learning Who this course is for Data Scientists Machine Learning Engineers Robotics Programmer Homepage https://www.udemy.com/course/deep-reinforcement-learning-made-easy/ Screenshot Rapidgator https://rg.to/file/17bb3b4f36fd603d42ad91cea9877673/xzazt.Deep.Reinforcement.Learning.madeeasy.part05.rar.html https://rg.to/file/3153e970625d06a1d73ccf1ccf4eddcc/xzazt.Deep.Reinforcement.Learning.madeeasy.part08.rar.html https://rg.to/file/8760cc9f61907bca3d7976c9f88f56f2/xzazt.Deep.Reinforcement.Learning.madeeasy.part01.rar.html https://rg.to/file/c7bdc7d0f916c987c0c9b1f03dc1cc6a/xzazt.Deep.Reinforcement.Learning.madeeasy.part03.rar.html https://rg.to/file/ca282df3788d65fbe55bd8cb692d631b/xzazt.Deep.Reinforcement.Learning.madeeasy.part06.rar.html https://rg.to/file/e48f3f9d96ece792a258741add3ac19c/xzazt.Deep.Reinforcement.Learning.madeeasy.part07.rar.html https://rg.to/file/e7c3b0b92021e20ea78bc49086dec074/xzazt.Deep.Reinforcement.Learning.madeeasy.part04.rar.html https://rg.to/file/ec2b74b1487776fd6bea64bb35c1b1f7/xzazt.Deep.Reinforcement.Learning.madeeasy.part09.rar.html https://rg.to/file/f217b71395a9afd21a31ca9f19b3f42e/xzazt.Deep.Reinforcement.Learning.madeeasy.part02.rar.html Fikper Free Download https://fikper.com/26RETUlsXv/xzazt.Deep.Reinforcement.Learning.madeeasy.part09.rar.html https://fikper.com/8b58FiCYxz/xzazt.Deep.Reinforcement.Learning.madeeasy.part05.rar.html https://fikper.com/AeM3mDy4Ly/xzazt.Deep.Reinforcement.Learning.madeeasy.part02.rar.html https://fikper.com/SIV6PYLu4c/xzazt.Deep.Reinforcement.Learning.madeeasy.part07.rar.html https://fikper.com/ZB7vMPSutF/xzazt.Deep.Reinforcement.Learning.madeeasy.part01.rar.html https://fikper.com/j7Wemn8L4T/xzazt.Deep.Reinforcement.Learning.madeeasy.part08.rar.html https://fikper.com/oDvKYwyAEw/xzazt.Deep.Reinforcement.Learning.madeeasy.part06.rar.html https://fikper.com/uv55KY7aFi/xzazt.Deep.Reinforcement.Learning.madeeasy.part04.rar.html https://fikper.com/yf8zdk0QYX/xzazt.Deep.Reinforcement.Learning.madeeasy.part03.rar.html No Password - Links are Interchangeable