Hello! future game developers. You are reading this course as you are probably curious person trying to learn more about a great game engine - Unity and specifically, programming in C#. Each module either pushes your skills in Unity into new areas or pushes them to the very limits of what they can be used for. This course takes a practical, project-based approach to teach you the specifics game development with the Unity 3D game engine. We walk through a series of hands-on projects and step-by-step tutorials using Unity and other free or open-source software. By the end of the course, you will be equipped to develop rich, interactive experiences using Unity.

机器学习算法第二版 这是Packt发布的《 的代码库。 流行于数据科学和机器学习的算法 这本书是关于什么的？ 机器学习以其强大而快速的大型数据集预测而获得了极大的普及。 但是，强大功能背后的真正力量是涉及大量统计分析的复杂算法，该算法搅动大型数据集并产生实质性见解。 本书涵盖以下激动人心的功能： 研究特征选择和特征工程过程 评估性能和误差权衡以进行线性回归 建立数据模型并使用不同类型的算法了解其工作方式 学习调整支持向量机（SVM）的参数 探索自然语言处理（NLP）和推荐系统的概念 如果您觉得这本书适合您，请立即获取！ 说明和导航 所有代码都组织在文件夹中。 例如，Chapter02。 该代码将如下所示： from sklearn.svm import SVC from sklearn.model_selection import cross_val_score svc =

很经典的参考书。 1 Introduction 2 Probability Distributions 3 Linear Models for Regression 4 Linear Models for Classification 5 Neural Networks 6 Kernel Methods 7 Sparse Kernel Machines 8 Graphical Models 9 Mixture Models and EM 10 Approximate Inference 11 Sampling Methods ...

深度学习 [deep learning] AI圣经 Deep Learning 英文版 ( 花书 ) ，[美] Ian，Goodfellow，[加] Yoshua，Bengio，[加] Aaron ... 著

典型相关分析matlab实现代码 迁移学习 Transfer Learning Everything about Transfer Learning (Probably the most complete repository?). Your contribution is highly valued! If you find this repo helpful, please cite it as follows: 关于迁移学习的所有资料，包括：介绍、综述文章、最新文章、代表工作及其代码、常用数据集、硕博士论文、比赛等等。(可能是目前最全的迁移学习资料库？) 欢迎一起贡献！ 如果认为本仓库有用，请在你的论文和其他出版物中进行引用！ @Misc{transferlearning.xyz, howpublished = {\url{http://transferlearning.xyz}}, title = {Everything about Transfer Learning and Domain Adapation}, author = {Wang, Jindong and othe

Machine Learning Yearning_英文版+中文版 (中文版会持续更新，并有更新的链接地址) 注：转载别人的，无商业目的，资源共享。

火炬指标 PyTorch的模型评估指标 火炬指标作为自定义库，以提供Pytorch共同ML评价指标，类似于tf.keras.metrics 。 如，Pytorch没有用于模型评估指标的内置库torch.metrics 。 这类似于的指标库。 用法 pip install --upgrade torch-metrics from torch_metrics import Accuracy ## define metric ## metric = Accuracy ( from_logits = False ) y_pred = torch . tensor ([ 1 , 2 , 3 , 4 ]) y_true = torch . tensor ([ 0 , 2 , 3 , 4 ]) print ( metric ( y_pred , y_true )) ## define metri

Preface Deep learning is a fascinating field. Artificial neural networks have been around for a long time, but something special has happened in recent years. The mixture of new faster hardware, new techniques and highly optimized open source libraries allow very large networks to be created with frightening ease. This new wave of much larger and much deeper neural networks are also impressively skillful on a range of problems. I have watched over recent years as they tackle and handily become state-of-the-art across a range of difficult problem domains. Not least object recognition, speech recognition, sentiment classification, translation and more. When a technique comes a long that does so well on such a broad set of problems, you have to pay attention. The problem is where do you start with deep learning? I created this book because I thought that there was no gentle way for Python machine learning practitioners to quickly get started developing deep learning models. In developing the lessons in this book, I chose the best of breed Python deep learning library called Keras that abstracted away all of the complexity, ruthlessly leaving you an API containing only what you need to know to efficiently develop and evaluate neural network models. This is the guide that I wish I had when I started apply deep learning to machine learning problems. I hope that you find it useful on your own projects and have as much fun applying deep learning as I did in creating this book for you.

Learning Concurrency in Python(pdf+epub+mobi+code_files).zip

Learning Python Application Development Learning Python Application Development