Aurélien Géron
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how.
By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and Tensor Flow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started.
- Explore the machine learning landscape, particularly neural nets
- Use Scikit-Learn to track an example machine-learning project end-to-end
- Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods
- Use the Tensor Flow library to build and train neural nets
- Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning
- Learn techniques for training and scaling deep neural nets.
Scott Hartshorn
If you are looking for a book to help you understand how the machine learning algorithms “Random Forest” and “Decision Trees” work behind the scenes, then this is a good book for you.
Those two algorithms are commonly used in a variety of applications including big data analysis for industry and data analysis competitions like you would find on Kaggle.
This book explains how Decision Trees work and how they can be combined into a Random Forest to reduce many of the common problems with decision trees, such as overfitting the training data.
Kevin M. Lynch & Frank C. Park
This introduction to robotics offers a distinct and unified perspective of the mechanics, planning and control of robots. Ideal for self-learning, or for courses, as it assumes only freshman-level physics, ordinary differential equations, linear algebra and a little bit of computing background.
Modern Robotics presents the state-of-the-art, screw-theoretic techniques capturing the most salient physical features of a robot in an intuitive geometrical way.
With numerous exercises at the end of each chapter, accompanying software written to reinforce the concepts in the book and video lectures aimed at changing the classroom experience, this is the go-to textbook for learning about this fascinating subject.
Roger Arrick
Discover what robots can do and how they work
Find out how to build your own robot and program it to perform tasks
Ready to enter the robot world? This book is your passport! It walks you through building your very own little metal assistant from a kit, dressing it up, giving it a brain, programming it to do things, even making it talk. Along the way, you’ll gather some tidbits about robot history, enthusiasts’ groups, and more.
Sebastian Thrun, Wolfram Burgard & Dieter Fox
Probabilistic robotics is a new and growing area in robotics, concerned with perception and control in the face of uncertainty.
Building on the field of mathematical statistics, probabilistic robotics endows robots with a new level of robustness in real-world situations.
This book introduces the reader to a wealth of techniques and algorithms in the field. All algorithms are based on a single overarching mathematical foundation.
Each chapter provides example implementations in pseudo code, detailed mathematical derivations, discussions from a practitioner’s perspective, and extensive lists of exercises and class projects.
The book’s Web site, www.probabilistic-robotics.org, has additional material.
The book is relevant for anyone involved in robotic software development and scientific research. It will also be of interest to applied statisticians and engineers dealing with real-world sensor data.
Michael Margolis, Brian Jepson and Nicholas Robert Weldin
Want to create devices that interact with the physical world? This cookbook is perfect for anyone who wants to experiment with the popular Arduino microcontroller and programming environment.
You’ll find more than 200 tips and techniques for building a variety of objects and prototypes such as IoT solutions, environmental monitors, location and position-aware systems, and products that can respond to touch, sound, heat, and light.
Updated for the Arduino 1.8 release, the recipes in this third edition include practical examples and guidance to help you begin, expand, and enhance your projects right away—whether you’re an engineer, designer, artist, student, or hobbyist.
Get up to speed on the Arduino board and essential software concepts quickly
Learn basic techniques for reading digital and analog signals
Use Arduino with a variety of popular input devices and sensors
Drive visual displays, generate sound, and control several types of motors
Connect Arduino to wired and wireless networks
Learn techniques for handling time delays and time measurement
Apply advanced coding and memory-handling techniques
Steven L. Brunton & J. Nathan Kutz
Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems.
This textbook brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science.
It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy.
Aimed at advanced undergraduate and beginning graduate students in the engineering and physical sciences, the text presents a range of topics and methods from introductory to state of the art.
Scientific WorldWide Comunity 