Abhishek Thakur

This book explores the critical role of LiDAR technology in autonomous navigation and advanced driver assistance systems (ADAS). It explores the fundamental principles of LiDAR, comparing it with other sensor technologies like radar and cameras while examining the various types of LiDAR systems, including time-of-flight, flash, and frequency-modulated continuous wave systems. It emphasises real-world use cases, including setting up LiDAR data acquisition systems and addressing challenges like sensor calibration, alignment, and integration into autonomous systems.• Discusses in detail LiDAR’s working principles, laser pulse wavelengths, point cloud data, motion compensation, and datasets commonly used in LiDAR research• Examines the effects of ambient light, adverse weather conditions (rain, fog, snow), and practical strategies for mitigating these challenges• Describes advanced methods for object detection, segmentation, and multi-object tracking using LiDAR point clouds, including solutions like AnchorPoint and Smart3DMOT• Presents techniques for creating high-definition 3D maps and implementing SLAM (Simultaneous Localization and Mapping) that are essential for autonomous navigation• Offers practical insights into autonomous navigation, including LiDAR-based localization, path planning, obstacle avoidance, and real-world case studies like autonomous shuttles• Explores multi-LiDAR calibration, emphasizing alignment, fusion, and synchronization to enhance coverage and reduce blind spots in autonomous systems.· Offers a detailed guide on open-source LiDAR processing tools like PCL, Open3D, and ROS for data handling and visualization.By combining theoretical principles with practical applications and case studies, this book serves as a reference book for academics and researchers in computer science, electronics, communication engineering, and autonomous technologies.

Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios About This Book • Build efficient deep learning pipelines using the popular Tensorflow framework • Train neural networks such as ConvNets, generative models, and LSTMs • Includes projects related to Computer Vision, stock prediction, chatbots and more Who This Book Is For This book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book. What You Will Learn • Set up the TensorFlow environment for deep learning • Construct your own ConvNets for effective image processing • Use LSTMs for image caption generation • Forecast stock prediction accurately with an LSTM architecture • Learn what semantic matching is by detecting duplicate Quora questions • Set up an AWS instance with TensorFlow to train GANs • Train and set up a chatbot to understand and interpret human input • Build an AI capable of playing a video game by itself –and win it! In Detail TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing so, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation, and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently. Style and approach This book contains 10 unique, end-to-end projects covering all aspects of deep learning and their implementations with TensorFlow. Each project will equip you with a unique skillset in training efficient deep learning models, and empower you to implement your own projects more confidently