Fundamentals Of Machine Learning For Robotics And Automation(1st Edition)
Authors:
Jamie Flux
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Book details
ISBN: B0DFGF9CC9, 979-8337561295
Book publisher: Independently published
Book Summary: Book Description:Embark On A Comprehensive Journey Into The World Of Machine Learning Tailored Specifically For Robotics With This Essential Guide. Delve Into A Plethora Of Cutting-edge Techniques And Algorithms Designed To Tackle Various Challenges In Robotics And Automation. From Foundational Models Like Linear And Logistic Regression To Sophisticated Deep Learning Architectures, Each Chapter Is Meticulously Crafted To Enhance Understanding And Practical Application In Robotics. Coupled With Hands-on Python Code Implementations, This Book Is An Invaluable Resource For Both Beginners And Seasoned Professionals Aiming To Elevate Their Expertise In Robotic Systems.Key Features:- Comprehensive Coverage: Explores 66 Chapters Of Machine Learning Algorithms And Their Applications In Robotics, Each Backed By Python Code To Solidify Learning.- Multi-Faceted Approaches: Delves Into Diverse Topics Such As Sensor Fusion, Real-time Processing, And Cognitive Architectures, Ensuring A Well-rounded Understanding.- Hands-On Learning: Provides Practical Python Code Examples For Every Chapter To Facilitate Real-world Applications And Experimentations.- Emerging Technologies: Introduces Nascent Fields Like Quantum Machine Learning And Federated Learning, Keeping You Abreast Of The Latest Innovations.- Problem-Solving Focus: Addresses Real-world Issues In Robotics, Including Obstacle Detection, Path Planning, And Human-robot Interaction.What You Will Learn:- Implement Linear And Logistic Regression Models To Solve Motion Prediction And Decision-making Tasks.- Integrate K-Nearest Neighbors And Decision Trees For Enhanced Perception And Path Planning.- Master Deep Learning Techniques Such As CNNs For Vision Processing And RNNs For Sequence Prediction.- Harness Clustering, Bayesian Methods, And Reinforcement Learning For Anomaly Detection And Autonomous Control.- Adapt To State-of-the-art Frameworks Like Self-supervised Learning And SLAM For Improved Perception And Localization.Who This Book Is For:This Book Is Designed For Robotics Enthusiasts, Machine Learning Practitioners, Data Scientists, And Engineers Seeking To Deepen Their Understanding Of Applying Machine Learning In Robotics And Automation. It Is Also A Valuable Resource For Academic Professionals And Students Aiming To Bridge The Gap Between Theoretical Knowledge And Practical Implementation In Robotics Projects. With Its Extensive Use Of Python, Prior Programming Knowledge Will Be Beneficial To Maximize The Learning Experience.Elevate Your Robotics Projects With Advanced Machine Learning Insights And Stay Ahead In The Evolving Field Of Automation And Intelligent Systems!
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