Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms., This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included., Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments., What You Will Learn, Leverage deep learning frameworks in Python namely, Keras, Theano, and CaffeGain the fundamentals of deep learning with mathematical prerequisitesDiscover the practical considerations of large scale experimentsTake deep learning models to production, Who This Book Is ForSoftware developers who want to try out deep learning as a practical solution to a particular problem.Software developers in a data science team who want to take deep learning models developed by data scientists to production.

适合初学者

本文工作基于faster RCNN , 区别在于 1.改进了rpn，anchor产生的window的宽度固定为3。 2.rpn后面不是直接接全连接+分类/回归，而是再通过一个LSTM，再接全连接层。 3.坐标仅仅回归一个y，而不是x1, y1, x2, y2 4.添加 side-refinement offsets（可能这个就是4个回归值中的其中2个）

吴恩达深度学习deeplearning第一课课后测验及编程作业（含答案）

因为吴恩达老师深度学习课程第四课第三周作业中需要用到yolo.h5，于是自己利用yolov2.cfg与yolov2.weights自己生成的yolo.h5，亲测可用。 没分的可以去我的github看看，下面有具体方法以及百度网盘下载地址，网址为：https://github.com/freenowill/Object-Detection

深度学习入门到精通必备

USSS_ICCV19 ICCV 2019接受通用半监督语义分割代码。 全文见 。 要求 Python> = 2.6 PyTorch> = 1.0.0 ImageNet预训练的模型是从的存储库下载的。 数据集 城市景观： ： IDD： ： 怎么跑 python segment.py --basedir --lr 0.001 --num-epochs 200 --batch-size 8 --savedir --datasets [ ..] --num-samples --alpha 0 --beta 0 --resnet --model drnet 致谢 大量代码是从Dilated Residual Networks（ ）和IDD Dataset（ ）的官方代码版本中大量借用的。

吴恩达的DeepLearning.ai课程学习Jupyter Notebook作业。Coursera上DeepLearning.ai课程的Jupyter Notebook的练习题和资源共享

UFLDL的深度学习教程翻译，适合入门。

pytorch-pocket-reference.rar