Learn Autonomous Programming with Python

2024-01-30
Learn Autonomous Programming with Python

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  • Author: Varun P Divadkar
  • Publisher: BPB Publications
  • ISBN: 9355517637
  • Category : Computers
  • Languages : en
  • Pages : 492

Unleash the hidden potential of Python to emerge as a change maker of contemporary industry KEY FEATURES ● Explore Python commands for RPA, workflows and hyperautomation. ● Concise chapters with lucid examples and elaborate codes that make learning interesting. ● Practical industry use case at the end of every chapter to highlight its real world application. DESCRIPTION The current industry (also called Industry 4.0) has witnessed an unprecedented expansion of technology in a short span of time, owing to an exponential increase in computational power coupled with internet technology. Consequently, domains like artificial intelligence, machine learning, deep learning and robotic process automation have gained prominence and become the backbone of organizations, making it inevitable for professionals to upgrade their skills in these domains. Orchestrate your work with AI and ML. Learn RPA's power, conduct web symphonies, utilize spreadsheets, and automate emails. You can also extract data from PDFs and images, choreograph applications, and play with deep learning. Design workflows, create hyperautomation finales, and combine Python with UiPath. You can further build a solid stage for your projects with PyScript, and continue with test automation. This book equips you to revolutionize your work, one Python script at a time. This book can be used as ready to reference as well as a user manual for quick solutions to common organizational needs and even for brushing up on key technical domain concepts. WHAT YOU WILL LEARN ● You will have a clear understanding of Python and create concise, flexible and maintainable applications for current industry needs. ● You will explore web scraping techniques using powerful libraries to extract valuable data from the web. ● You will have a high level overview of fundamentals in ML, deep learning, RPA, and hyperautomation. ● You will learn to write compact and maintainable code in Python catering to typical applications in contemporary industries. ● You will also learn how to apply your learnings to real world industry scenarios using the practical Python use cases presented at the end of each chapter. WHO THIS BOOK IS FOR This book is specifically meant for students and professionals who have prior working knowledge of Python from a basic to intermediate level and would want to expand their horizon of Python programming. TABLE OF CONTENTS 1. Why Python for Automation? 2. RPA Foundations 3. Getting Started with AI/ML in Python 4. Automating Web Scraping 5. Automating Excel and Spreadsheets 6. Automating Emails and Messaging 7. Working with PDFs and Images 8. Mechanizing Applications, Folders and Actions 9. Intelligent Automation Part 1: Using Machine Learning 10. Intelligent Automation Part 2: Using Deep Learning 11. Automating Business Process Workflows 12. Hyperautomation 13. Python and UiPath 14. Architecting Automation Projects 15. The PyScript Framework 16. Test Automation in Python

Learn Robotics Programming

2021-02-12
Learn Robotics Programming

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  • Author: Danny Staple
  • Publisher: Packt Publishing Ltd
  • ISBN: 1839217863
  • Category : Computers
  • Languages : en
  • Pages : 602

Develop an extendable smart robot capable of performing a complex series of actions with Python and Raspberry Pi Key Features Get up to speed with the fundamentals of robotic programming and build intelligent robots Learn how to program a voice agent to control and interact with your robot's behavior Enable your robot to see its environment and avoid barriers using sensors Book Description We live in an age where the most complex or repetitive tasks are automated. Smart robots have the potential to revolutionize how we perform all kinds of tasks with high accuracy and efficiency. With this second edition of Learn Robotics Programming, you'll see how a combination of the Raspberry Pi and Python can be a great starting point for robot programming. The book starts by introducing you to the basic structure of a robot and shows you how to design, build, and program it. As you make your way through the book, you'll add different outputs and sensors, learn robot building skills, and write code to add autonomous behavior using sensors and a camera. You'll also be able to upgrade your robot with Wi-Fi connectivity to control it using a smartphone. Finally, you'll understand how you can apply the skills that you've learned to visualize, lay out, build, and code your future robot building projects. By the end of this book, you'll have built an interesting robot that can perform basic artificial intelligence operations and be well versed in programming robots and creating complex robotics projects using what you've learned. What you will learn Leverage the features of the Raspberry Pi OS Discover how to configure a Raspberry Pi to build an AI-enabled robot Interface motors and sensors with a Raspberry Pi Code your robot to develop engaging and intelligent robot behavior Explore AI behavior such as speech recognition and visual processing Find out how you can control AI robots with a mobile phone over Wi-Fi Understand how to choose the right parts and assemble your robot Who this book is for This second edition of Learn Robotics Programming is for programmers, developers, and robotics enthusiasts who want to develop a fully functional robot and leverage AI to build interactive robots. Basic knowledge of the Python programming language will help you understand the concepts covered in this robot programming book more effectively.

Learn Robotics Programming

2018-11-29
Learn Robotics Programming

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  • Author: Danny Staple
  • Publisher: Packt Publishing Ltd
  • ISBN: 1789345936
  • Category : Computers
  • Languages : en
  • Pages : 462

Gain experience of building a next-generation collaboration robot Key FeaturesGet up and running with the fundamentals of robotic programmingProgram a robot using Python and the Raspberry Pi 3Learn to build a smart robot with interactive and AI-enabled behaviorsBook Description We live in an age where the most difficult human tasks are now automated. Smart and intelligent robots, which will perform different tasks precisely and efficiently, are the requirement of the hour. A combination of Raspberry Pi and Python works perfectly when making these kinds of robots. Learn Robotics Programming starts by introducing you to the basic structure of a robot, along with how to plan, build, and program it. As you make your way through the book, you will gradually progress to adding different outputs and sensors, learning new building skills, and writing code for interesting behaviors with sensors. You’ll also be able to update your robot, and set up web, phone, and Wi-Fi connectivity in order to control it. By the end of the book, you will have built a clever robot that can perform basic artificial intelligence (AI) operations. What you will learnConfigure a Raspberry Pi for use in a robotInterface motors and sensors with a Raspberry PiImplement code to make interesting and intelligent robot behaviorsUnderstand the first steps in AI behavior such as speech recognition visual processingControl AI robots using Wi-FiPlan the budget for requirements of robots while choosing partsWho this book is for Learn Robotics Programming is for programmers, developers, and enthusiasts interested in robotics and developing a fully functional robot. No major experience required just some programming knowledge would be sufficient.

Learning Robotics using Python

2018-06-27
Learning Robotics using Python

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  • Author: Lentin Joseph
  • Publisher: Packt Publishing Ltd
  • ISBN: 1788629973
  • Category : Computers
  • Languages : en
  • Pages : 273

Design, simulate, and program interactive robots Key Features Design, simulate, build, and program an interactive autonomous mobile robot Leverage the power of ROS, Gazebo, and Python to enhance your robotic skills A hands-on guide to creating an autonomous mobile robot with the help of ROS and Python Book Description Robot Operating System (ROS) is one of the most popular robotics software frameworks in research and industry. It has various features for implementing different capabilities in a robot without implementing them from scratch. This book starts by showing you the fundamentals of ROS so you understand the basics of differential robots. Then, you'll learn about robot modeling and how to design and simulate it using ROS. Moving on, we'll design robot hardware and interfacing actuators. Then, you'll learn to configure and program depth sensors and LIDARs using ROS. Finally, you'll create a GUI for your robot using the Qt framework. By the end of this tutorial, you'll have a clear idea of how to integrate and assemble everything into a robot and how to bundle the software package. What you will learn Design a differential robot from scratch Model a differential robot using ROS and URDF Simulate a differential robot using ROS and Gazebo Design robot hardware electronics Interface robot actuators with embedded boards Explore the interfacing of different 3D depth cameras in ROS Implement autonomous navigation in ChefBot Create a GUI for robot control Who this book is for This book is for those who are conducting research in mobile robotics and autonomous navigation. As well as the robotics research domain, this book is also for the robot hobbyist community. You’re expected to have a basic understanding of Linux commands and Python.

Learning Robotics Using Python

2018-06-27
Learning Robotics Using Python

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  • Author: Lentin Joseph
  • Publisher:
  • ISBN: 9781788623315
  • Category : Automation
  • Languages : en
  • Pages : 280

Design, simulate, and program interactive robots Key Features Design, simulate, build, and program an interactive autonomous mobile robot Leverage the power of ROS, Gazebo, and Python to enhance your robotic skills A hands-on guide to creating an autonomous mobile robot with the help of ROS and Python Book Description Robot Operating System (ROS) is one of the most popular robotics software frameworks in research and industry. It has various features for implementing different capabilities in a robot without implementing them from scratch. This book starts by showing you the fundamentals of ROS so you understand the basics of differential robots. Then, you'll learn about robot modeling and how to design and simulate it using ROS. Moving on, we'll design robot hardware and interfacing actuators. Then, you'll learn to configure and program depth sensors and LIDARs using ROS. Finally, you'll create a GUI for your robot using the Qt framework. By the end of this tutorial, you'll have a clear idea of how to integrate and assemble everything into a robot and how to bundle the software package. What you will learn Design a differential robot from scratch Model a differential robot using ROS and URDF Simulate a differential robot using ROS and Gazebo Design robot hardware electronics Interface robot actuators with embedded boards Explore the interfacing of different 3D depth cameras in ROS Implement autonomous navigation in ChefBot Create a GUI for robot control Who this book is for This book is for those who are conducting research in mobile robotics and autonomous navigation. As well as the robotics research domain, this book is also for the robot hobbyist community. You're expected to have a basic understanding of Linux commands and Python.

Learning Robotics Using Python

2015
Learning Robotics Using Python

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  • Author: Joseph Lentin
  • Publisher:
  • ISBN: 9781680157499
  • Category : Automation
  • Languages : en
  • Pages :

Hands-On Vision and Behavior for Self-Driving Cars

2020-10-23
Hands-On Vision and Behavior for Self-Driving Cars

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  • Author: Luca Venturi
  • Publisher: Packt Publishing Ltd
  • ISBN: 1800201931
  • Category : Computers
  • Languages : en
  • Pages : 374

A practical guide to learning visual perception for self-driving cars for computer vision and autonomous system engineers Key FeaturesExplore the building blocks of the visual perception system in self-driving carsIdentify objects and lanes to define the boundary of driving surfaces using open-source tools like OpenCV and PythonImprove the object detection and classification capabilities of systems with the help of neural networksBook Description The visual perception capabilities of a self-driving car are powered by computer vision. The work relating to self-driving cars can be broadly classified into three components - robotics, computer vision, and machine learning. This book provides existing computer vision engineers and developers with the unique opportunity to be associated with this booming field. You will learn about computer vision, deep learning, and depth perception applied to driverless cars. The book provides a structured and thorough introduction, as making a real self-driving car is a huge cross-functional effort. As you progress, you will cover relevant cases with working code, before going on to understand how to use OpenCV, TensorFlow and Keras to analyze video streaming from car cameras. Later, you will learn how to interpret and make the most of lidars (light detection and ranging) to identify obstacles and localize your position. You’ll even be able to tackle core challenges in self-driving cars such as finding lanes, detecting pedestrian and crossing lights, performing semantic segmentation, and writing a PID controller. By the end of this book, you’ll be equipped with the skills you need to write code for a self-driving car running in a driverless car simulator, and be able to tackle various challenges faced by autonomous car engineers. What you will learnUnderstand how to perform camera calibrationBecome well-versed with how lane detection works in self-driving cars using OpenCVExplore behavioral cloning by self-driving in a video-game simulatorGet to grips with using lidarsDiscover how to configure the controls for autonomous vehiclesUse object detection and semantic segmentation to locate lanes, cars, and pedestriansWrite a PID controller to control a self-driving car running in a simulatorWho this book is for This book is for software engineers who are interested in learning about technologies that drive the autonomous car revolution. Although basic knowledge of computer vision and Python programming is required, prior knowledge of advanced deep learning and how to use sensors (lidar) is not needed.

Advanced Deep Learning with Python

2019-12-12
Advanced Deep Learning with Python

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  • Author: Ivan Vasilev
  • Publisher: Packt Publishing Ltd
  • ISBN: 1789952719
  • Category : Computers
  • Languages : en
  • Pages : 456

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key FeaturesGet to grips with building faster and more robust deep learning architecturesInvestigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorchApply deep neural networks (DNNs) to computer vision problems, NLP, and GANsBook Description In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles. By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learnCover advanced and state-of-the-art neural network architecturesUnderstand the theory and math behind neural networksTrain DNNs and apply them to modern deep learning problemsUse CNNs for object detection and image segmentationImplement generative adversarial networks (GANs) and variational autoencoders to generate new imagesSolve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence modelsUnderstand DL techniques, such as meta-learning and graph neural networksWho this book is for This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.

TensorFlow Reinforcement Learning Quick Start Guide

2019-03-30
TensorFlow Reinforcement Learning Quick Start Guide

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  • Author: Kaushik Balakrishnan
  • Publisher: Packt Publishing Ltd
  • ISBN: 1789533449
  • Category : Computers
  • Languages : en
  • Pages : 175

Leverage the power of Tensorflow to Create powerful software agents that can self-learn to perform real-world tasks Key FeaturesExplore efficient Reinforcement Learning algorithms and code them using TensorFlow and PythonTrain Reinforcement Learning agents for problems, ranging from computer games to autonomous driving.Formulate and devise selective algorithms and techniques in your applications in no time.Book Description Advances in reinforcement learning algorithms have made it possible to use them for optimal control in several different industrial applications. With this book, you will apply Reinforcement Learning to a range of problems, from computer games to autonomous driving. The book starts by introducing you to essential Reinforcement Learning concepts such as agents, environments, rewards, and advantage functions. You will also master the distinctions between on-policy and off-policy algorithms, as well as model-free and model-based algorithms. You will also learn about several Reinforcement Learning algorithms, such as SARSA, Deep Q-Networks (DQN), Deep Deterministic Policy Gradients (DDPG), Asynchronous Advantage Actor-Critic (A3C), Trust Region Policy Optimization (TRPO), and Proximal Policy Optimization (PPO). The book will also show you how to code these algorithms in TensorFlow and Python and apply them to solve computer games from OpenAI Gym. Finally, you will also learn how to train a car to drive autonomously in the Torcs racing car simulator. By the end of the book, you will be able to design, build, train, and evaluate feed-forward neural networks and convolutional neural networks. You will also have mastered coding state-of-the-art algorithms and also training agents for various control problems. What you will learnUnderstand the theory and concepts behind modern Reinforcement Learning algorithmsCode state-of-the-art Reinforcement Learning algorithms with discrete or continuous actionsDevelop Reinforcement Learning algorithms and apply them to training agents to play computer gamesExplore DQN, DDQN, and Dueling architectures to play Atari's Breakout using TensorFlowUse A3C to play CartPole and LunarLanderTrain an agent to drive a car autonomously in a simulatorWho this book is for Data scientists and AI developers who wish to quickly get started with training effective reinforcement learning models in TensorFlow will find this book very useful. Prior knowledge of machine learning and deep learning concepts (as well as exposure to Python programming) will be useful.

Hands-On ROS for Robotics Programming

2020-02-26
Hands-On ROS for Robotics Programming

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  • Author: Bernardo Ronquillo Japón
  • Publisher: Packt Publishing Ltd
  • ISBN: 183855288X
  • Category : Computers
  • Languages : en
  • Pages : 414

Take your ROS skills to the next level by implementing complex robot structures in a ROS simulation Key FeaturesLearn fundamental ROS concepts and apply them to solve navigation tasksWork with single board computers to program smart behavior in mobile robotsUnderstand how specific characteristics of the physical environment influence your robot’s performanceBook Description Connecting a physical robot to a robot simulation using the Robot Operating System (ROS) infrastructure is one of the most common challenges faced by ROS engineers. With this book, you'll learn how to simulate a robot in a virtual environment and achieve desired behavior in equivalent real-world scenarios. This book starts with an introduction to GoPiGo3 and the sensors and actuators with which it is equipped. You'll then work with GoPiGo3's digital twin by creating a 3D model from scratch and running a simulation in ROS using Gazebo. Next, the book will show you how to use GoPiGo3 to build and run an autonomous mobile robot that is aware of its surroundings. Finally, you'll find out how a robot can learn tasks that have not been programmed in the code but are acquired by observing its environment. You'll even cover topics such as deep learning and reinforcement learning. By the end of this robot programming book, you'll be well-versed with the basics of building specific-purpose applications in robotics and developing highly intelligent autonomous robots from scratch. What you will learnGet to grips with developing environment-aware robotsGain insights into how your robots will react in physical environmentsBreak down a desired behavior into a chain of robot actionsRelate data from sensors with context to produce adaptive responsesApply reinforcement learning to allow your robot to learn by trial and errorImplement deep learning to enable your robot to recognize its surroundingsWho this book is for If you are an engineer looking to build AI-powered robots using the ROS framework, this book is for you. Robotics enthusiasts and hobbyists who want to develop their own ROS robotics projects will also find this book useful. Knowledge of Python and/or C++ programming and familiarity with single board computers such as Raspberry Pi is necessary to get the most out of this book.