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

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

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）

Hands-On Transfer Learning with Python（带书签PDF+代码）,迁移学习Python实战，by Dipanjan Sarkar。