Hands-on-Machine-Learning-with-Scikit-第二版完整先行版

Python Crash Course is a fast-paced, thorough introduction to programming with Python that will have you writing programs, solving problems, and making things that work in no time. In the first half of the book, you'll learn about basic programming concepts, such as lists, dictionaries, classes, and loops, and practice writing clean and readable code with exercises for each topic. You'll also learn how to make your programs interactive and how to test your code safely before adding it to a project. In the second half of the book, you'll put your new knowledge into practice with three substantial projects: a Space Invaders-inspired arcade game, data visualizations with Python's super-handy libraries, and a simple web app you can deploy online. As you work through Python Crash Course, you'll learn how to: Use powerful Python libraries and tools, including matplotlib, NumPy, and Pygal Make 2D games that respond to keypresses and mouse clicks, and that grow more difficult as the game progresses Work with data to generate interactive visualizations Create and customize simple web apps and deploy them safely online Deal with mistakes and errors so you can solve your own programming problems If you've been thinking seriously about digging into programming, Python Crash Course will get you up to speed and have you writing real programs fast. Why wait any longer? Start your engines and code! Table of Contents Part I: Basics Chapter 1: Getting Started Chapter 2: Variables and Simple Data Types Chapter 3: Introducing Lists Chapter 4: Working with Lists Chapter 5: if Statements Chapter 6: Dictionaries Chapter 7: User Input and while Loops Chapter 8: Functions Chapter 9: Classes Chapter 10: Files and Exceptions Chapter 11: Testing Your Code Part II: Projects Project 1: Alien Invasion Project 2: Data Visualization Project 3: Web Applications Appendix A: Installing Python Appendix B: Text Editors Appendix C: Getting Help Appendix D: Using Git for Version Control

Hands-On Enterprise Automation with Python: Automate common administrative and security tasks with the most popular language Python Invent your own Python scripts to automate your infrastructure Hands-On Enterprise Automation with Python starts by covering the set up of a Python environment to perform automation tasks, as well as the modules, libraries, and tools you will be using. We’ll explore examples of network automation tasks using simple Python programs and Ansible. Next, we will walk you through automating administration tasks with Python Fabric, where you will learn to perform server configuration and administration, along with system administration tasks such as user management, database management, and process management. As you progress through this book, you’ll automate several testing services with Python scripts and perform automation tasks on virtual machines and cloud infrastructure with Python. In the concluding chapters, you will cover Python-based offensive security tools and learn how to automate your security tasks. By the end of this book, you will have mastered the skills of automating several system administration tasks with Python. What You Will Learn Understand common automation modules used in Python Develop Python scripts to manage network devices Automate common Linux administration tasks with Ansible and Fabric Managing Linux processes Administrate VMware, OpenStack, and AWS instances with Python Security automation and sharing code on GitHub

yolov5 7中手势识别，detect3.py可以做通信识别(用Qt项目Tcp Socket接收)，detect3.py可以做视频流的识别 项目已做脱皿处理，训练数据已清除，但\yolov5\runs\train\exp2\weights\last.pt为yolov5l6.pt做600次迭代 希望“友商”赶紧跟上！

Hands-On Machine Learning with Scikit-Learn and TensorFlow代码及数据集

原版PDF Hands-On Machine Learning with Scikit-Learn and TensorFlow 不是早期预览版（EAP），而是正式版

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow 2nd edition

Learning Core Audio A Hands-On Guide to Audio Programming for Mac and iOS 英文无水印pdf pdf所有页面使用FoxitReader和PDF-XChangeViewer测试都可以打开 本资源转载自网络，如有侵权，请联系上传者或csdn删除 本资源转载自网络，如有侵权，请联系上传者或csdn删除

Shaders need shader model 3.0+, hands also looks pretty at standard and mobile shaders. PBR Arms and Hands for female and male characters in standalone and mobile versions.. Assets use our custom and fast standard skin shader which supported features like: Translucency, Tint, Rim light, Ramp. With smart sliders you are able to control every aspect of the shader. Skin shader could be also used for other parts of character as basic skin shader. Models are animated and they contains pack of basic animations like: catching, throwing, fist fighting, idle, 3 spells, sword/knife fighting, pistol shooting. This pack contains: - Female and male arms for first person games in standalone and mobile versions. - Female and male hands for VR games in standalone and mobile versions. - All textures in 3 variants - Blood and Dirt support - Controllers for left and right hand/arms - 16 Animations for Arms - 21 Animations for Hands - Standard PBR skin shader - Test models: pistol and sword - Example and test system to control your hands and arms - Example and test VR and FP characters - Mecanim controllers for each hand - 35 Arms and hands textures (4096x4096) - 3 Ramp textures (256x16)

by Stefan Jansen Packt Publishing 2018-12-31 684 pages Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras Key Features Implement machine learning algorithms to build, train, and validate algorithmic models Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics Book Description The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This book enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This book shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You'll practice the ML work?ow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This book also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym. What you will learn Implement machine learning techniques to solve investment and trading problems Leverage market, fundamental