tensorflow2.4使用CUDA11.1+cudnn时，发现问题缺少cusolver64_10.dll，报错如下。为了帮助大家解决，把这个dll放上来。 Could not load dynamic library ‘cusolver64_10.dll’; dlerror: cusolver64_10.dll not found

资源主要包括课设(毕设)手写数字识别系统完整项目代码和课设报告，下载后遇到问题可以私信作者。系统描述见链接：https://blog.csdn.net/shooter7/article/details/113337835

python setup.py install 方式安装fasttext，避免报错

史诗级干货，机器学习实战+Python3实现

零基础入门学习Python+全套源码课件-资源共享吧收集整理

This book attempts to simplify and present the concepts of deep learning in a very comprehensive manner, with suitable, full-fledged examples of neural network architectures, such as Recurrent Neural Networks (RNNs) and Sequence to Sequence (seq2seq), for Natural Language Processing (NLP) tasks. The book tries to bridge the gap between the theoretical and the applicable. It proceeds from the theoretical to the practical in a progressive manner, first by presenting the fundamentals, followed by the underlying mathematics, and, finally, the implementation of relevant examples. The first three chapters cover the basics of NLP, starting with the most frequently used Python libraries, word vector representation, and then advanced algorithms like neural networks for textual data. The last two chapters focus entirely on implementation, dealing with sophisticated architectures like RNN, Long Short-Term Memory (LSTM) Networks, Seq2seq, etc., using the widely used Python tools TensorFlow and Keras. We have tried our best to follow a progressive approach, combining all the knowledge gathered to move on to building a questionand- answer system. The book offers a good starting point for people who want to get started in deep learning, with a focus on NLP. All the code presented in the book is available on GitHub, in the form of IPython notebooks and scripts, which allows readers to try out these examples and extend them in interesting, personal ways.

这是小甲鱼python教学视频的课后习题以及答案的整理文档，大家可以学到什么： 0.Python3的所有常用语法 1.面向对象编程思维 2.运用模块进行编程 3.游戏编程 4.计算机仿真

许多行业专家认为，无人监督学习人工智能的下一个前沿，这可能是人工智能研究的关键，即所谓的一般人工智能。由于世界上大多数数据都没有标记，因此无法应用传统的监督学习;这就是无监督学习的用武之地。无监督学习可以应用于未标记的数据集，以发现埋藏在数据深处的有意义的模式，人类几乎不可能发现这些模式。 作者Ankur Patel使用两个简单的，生产就绪的Python框架 - scikit-learn和使用Keras的TensorFlow，提供了有关如何应用无监督学习的实用知识。通过提供实际操作示例和代码，您将识别难以发现的数据模式，获得更深入的业务洞察力，检测异常，执行自动特征工程和选择，以及生成合成数据集。您只需要编程和一些机器学习经验即可开始使用。

Web安全之强化学习与GAN，绝无仅有的资源，网上必须要花钱买，我就无私奉献吧

【《Python强化学习实战》随书代码】’Hands-On Reinforcement Learning with Python, published by Packt'