分享在哥伦比亚大学上的机器学习在Python中的实践课的作业以及Solutions，希望大家能够通过做这些作业以及参考答案来学习机器学习以及Python的知识点。这是一门实践性非常强的课。使用Python和相对应的例子来理解机器学习和深度学习的知识点。这门课让我学到不少东西，强烈推荐大家可以学完整个课程

分享在哥伦比亚大学上的机器学习在Python中的实践课的作业以及Solutions，希望大家能够通过做这些作业以及参考答案来学习机器学习以及Python的知识点。这是一门实践性非常强的课。使用Python和相对应的例子来理解机器学习和深度学习的知识点。这门课让我学到不少东西，强烈推荐大家可以学完整个课程

使用3000张安全帽训练的.h5模型，精准度很高

使用8000张规则和不规则的火焰数据集训练的.pth火焰模型,识别度高达百分之99.9，可以使用深度学习框架识别各种规则和不规则的火焰

DeepFace一文依旧是沿着“检测-对齐-人脸表示-分类”这一人脸识别技术路线来的，其贡献在于对人脸对齐和人脸表示环节的改进。1）在人脸对齐环节，引入了3D人脸模型对有姿态的人脸就行分片的仿射对齐。2）在人脸表示环节，利用一个9层的深度卷积在包含4000人、400万张人脸的数据集上学习人脸表示，这个9层的DCNN网络有超过1.2亿个参数。本文的模型在LFW数据集上取得了97.25 的平均精度（逼近了人类97.5 的极限），同时在Youtube数据集上取得了当前最好的结果，比之前的NO.1整整高出了12.7 。

已经标注好了的1200张口罩数据集，包括.jpg文件和生成的.xml文件，可供训练出非常准确的智能识别的口罩模型，可用于基本的深度学习yolov3框架，yolov4框架和yolov5框架，标注不易所以是付费资源，谢谢。

本方法被用于进行交通状态预测。里面包含完整的代码以及数据。

《深度学习入门：基于python的理论与实践》源码.rar

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

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.