xiaotao_hit的博客

Deep Learning for Computer Vision with Python（全三册），Adrian Rosebrock，Starter Bundle，Practitioner Bundle，ImageNet Bundle.

李航，统计学习方法.pdf，统计学习方法-勘误.pdf，仅供学习研究，24小时请删除，支持购买正版

cnn_utils.py、test_signs.h5、train_signs.h5，亲测！

Pedro Domingos，机器学习入门 Chapter 1 e Machine-Learning Revolution Chapter 2 e Master Algorithm Chapter 3 Hume’s Problem of Induction Chapter 4 How Does Your Brain Learn? Chapter 5 Evolution: Nature’s Learning Algorithm Chapter 6 In the Church of the Reverend Bayes Chapter 7 You Are What You Resemble Chapter 8 Learning Without a Teacher Chapter 9 e Pieces of the Puzzle Fall into Place Chapter 10 is Is the World on Machine Learning

Michael Bowles，Python机器学习：预测分析核心算法，英文版.

Patrick Felicia，A step-by-step guide to creating your first game.

Andreas C. Mueller and Sarah Guido，Python机器学习入门英文版

JOSEPH HOCKING，Unity 5实战使用 - C#和Unity开发多平台游戏.

Valera Cogut，A fast-paced guide to building impressive games and applications for Android devices with Unity 5.

Daniel Lélis Baggio，Create multiplatform computer vision desktop and web applications using the combination of OpenCV and Java.

Dusty Phillips，Unleash the power of Python 3 objects.

Joel Grus ■■ Get a crash course in Python ■■ Learn the basics of linear algebra, statistics, and probability— and understand how and when they're used in data science ■■ Collect, explore, clean, munge, and manipulate data ■■ Dive into the fundamentals of machine learning ■■ Implement models such as k-nearest neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering ■■ Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

Introduction Chapter 1: What is a neural network? Chapter 2: Biological analogies Chapter 3: Getting output from a neural network Chapter 4: Training a neural network with backpropagation Chapter 5: Theano Chapter 6: TensorFlow Chapter 7: Unsupervised learning, autoencoders, restricted Boltzmann machines, convolutional neural networks, and LSTMs Conclusion

HENRIK BRINK，et al，《实用机器学习》的英文版，推荐！

Alexander T. Combs，An approachable guide to applying advanced machine learning methods to everyday problems.

Introduction 1. Architecture, Performance, and Games II. Design Patterns Revisited 2. Command 3. Flyweight 4. Observer 5. Prototype 6. Singleton 7. State III. Sequencing Patterns 8. Double Buffer 9. Game Loop 10. Update Method IV. Behavioral Patterns 11. Bytecode 12. Subclass Sandbox 13. Type Object V. Decoupling Patterns 14. Component 15. Event Queue 16. Service Locator VI. Optimization Patterns 17. Data Locality 18. Dirty Flag 19. Object Pool 20. Spatial Partition

By: The LazyProgrammer Introduction Chapter 1: What is Machine Learning? Chapter 2: Classification and Regression Chapter 3: Linear Regression Chapter 4: Linear Classification Chapter 5: Logistic Regression Chapter 6: Maximum Likelihood Estimation Chapter 7: Gradient Descent Chapter 8: The XOR and Donut Problems Conclusion

Vahé Karamian，Unleash the full potential of Unity to build a fully playable, high-quality multiplayer RPG.

台大李宏毅老师深度学习课程PPT完整版，301页，经典！

Gabriele Lanaro，Python.High.Performance.2nd.Edition.2017.5

PETER HARRINGTON，经典书籍《机器学习实践》英文版。

David Julian，Design efficient machine learning systems that give you more accurate results

tf_utils.py，datasets（train_signs.h5、test_signs.h5），亲测！

What You Will Learn Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Apply your new found skills to solve real problems, through clearly-explained code for every technique and test Automate large sets of complex data and overcome time-consuming practical challenges Improve the accuracy of models and your existing input data using powerful feature engineering techniques Use multiple learning techniques together to improve the consistency of results Understand the hidden structure of datasets using a range of unsupervised techniques Gain insight into how the experts solve challenging data problems with an effective, iterative, and validation-focused approach Improve the effectiveness of your deep learning models further by using powerful ensembling techniques to strap multiple models together

Chapter 1: Introduction to Deep Learning Chapter 2: Machine Learning Fundamentals Chapter 3: Feed Forward Neural Networks Chapter 4: Introduction to Theano Chapter 5: Convolutional Neural Networks Chapter 6: Recurrent Neural Networks Chapter 7: Introduction to Keras Chapter 8: Stochastic Gradient Descent Chapter 9: Automatic Differentiation Chapter 10: Introduction to GPUs

Using machine learning to gain deeper insights from data is a key skill required by modern application developers and analysts alike. Python is a wonderful language to develop machine learning applications. As a dynamic language, it allows for fast exploration and experimentation. With its excellent collection of open source machine learning libraries you can focus on the task at hand while being able to quickly try out many ideas. This book shows you exactly how to find patterns in your raw data. You will start by brushing up on your Python machine learning knowledge and introducing libraries. You'll quickly get to grips with serious, real-world projects on datasets, using modeling, creating recommendation systems. Later on, the book covers advanced topics such as topic modeling, basket analysis, and cloud computing. These will extend your abilities and enable you to create large complex systems. With this book, you gain the tools and understanding required to build your own systems, tailored to solve your real-world data analysis problems.

Table of Contents Getting Started Variables and Basic Types Container Types Linear Algebra – Arrays Advanced Array Concepts Plotting Functions Classes Iterating Error Handling Namespaces, Scopes, and Modules Input and Output Testing Comprehensive Examples Symbolic Computations - SymPy References

包含：opt_utils.py，testCases.py，......亲测可用！

dnn_utils_v2.py，testCases_v3.py，dnn_app_utils_v2.py，亲测！

第一课第三周浅层神经网络编程作业，两个文件，亲测可用！

吴恩达老师深度学习课程作业用到的资源文件，亲测可用！