Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to compute

这是论文Visual Attribute Transfer through Deep Image Analogy的原文，内含有小编本人的理解与注释，谨以此分享个人经验和向广大大牛请教，如有错误，请不吝指出，万分感谢！

Modest 的经典书籍Radiative_Heat_Transfer第三版，辐射换热理论经典

深度学习风格迁移(style transfer)，python代码，可直接运行run.py

代码中实现了两个用于颜色转化的论文 1.《Color Transfer Between Images》对应类HistogramCT.h 2.《Color Transfer Based on Normalized Cumulative Hue Histograms》对应类HistogramCT.h

Windows远程连接linux SSH Secure Shell Client + SSH Secure File Transfer Client

迁移学习Python实战 Hands on transfer learning with Python

迁移学习的一些基础概念和研究领域分类整理

用于图像风格转化（image style transfer)的代码实现。

博客：TensorFlow 迁移学习（transfering learning）[TensorFlow 迁移学习（transfering learning）]的数据集和权值文件，由于文件有700多M，因此给出了存入了百度网盘，大家可以下载后，从ReadMe文件中获取下载链接和提取码。（文件链接长期有效，失效了可给我发私信）