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

深度SVDD的PyTorch实现 该存储库提供了我们的ICML 2018论文“深度一类分类”中介绍的Deep SVDD方法的实现。 引用与联系 您可以在找到《深层一类分类ICML 2018》论文的PDF。 如果您使用我们的作品，也请引用以下文章： @InProceedings{pmlr-v80-ruff18a, title = {Deep One-Class Classification}, author = {Ruff, Lukas and Vandermeulen, Robert A. and G{\"o}rnitz, Nico and Deecke, Lucas and Siddiqui, Shoaib A. and Binder, Alexander and M{\"u}ller, Emmanuel and Kloft, Marius}, bookti

– Volume 2 – 20 Deep Learning 21 Convolutional Neural Nets (CNNs) 22 Recurrent Nerual Nets (RNNs) 23 Keras Part 1 24 Keras Part 2 25 Autoencoders 26 Reinforcement Learning 27 Generative Adversarial Networks (GANs) 28 Creative Applications 29 Datasets 30 Glossary

SincNet SincNet是用于处理原始音频样本的神经体系结构。 这是一种新颖的卷积神经网络（CNN），它鼓励第一个卷积层发现更多有意义的滤波器。 SincNet基于参数化的Sinc函数，这些函数实现了带通滤波器。 与学习每个滤波器的所有元素的标准CNN相比，所提出的方法只能从数据中直接学习低和高截止频率。 这提供了一种非常紧凑而有效的方式来导出专门针对所需应用进行了调整的定制滤波器组。 该项目发布了一系列代码和实用程序，可通过SincNet进行说话人识别。 使用TIMIT数据库提供了说话人识别的示例。 如果您对应用于语音识别的SincNet感兴趣，可以查看PyTorch-Kaldi

　　如果你是一名有经验的程序员，迅速阅读此书可以大体了解Python语言的核心。掌握了Python语言的核心，想再深入了解它的面向对象特性和编程技巧，可以看其他的Python大部头，或者最直接也是最有效的方式，下载并安装Python，在它的“Shell”里边用边学，这样可以事半功倍；如果你英语够好，python.org网站将是你挖宝的必经之地。此书也讲到了Python的这一易学特性，只要你仔细认真，定会从学习中得到乐趣。 　　《Python语言入门》曾是我大学时期读过的专业类好书之一，现在在我的同学中传阅。译者翻译得比较准确、通顺。在Python的入门级图书中，《Python语言入门》不失为一部经典之作。

Deep_Learning_for_Computer_Vision_with_Python，作者Adrian Rosebrock, 资料包含Starter, Practitioner, ImageNet Bundle三本书。