Hands－On_Natural_Language_Processing_with_Python 2018 a lot of examples

Abstract—Transfer learning aims at improving the performance of target learners on target domains by transferring the knowledge contained in different but related source domains. In this way, the dependence on a large number of target domain data can be reduced for constructing target learners. Due to the wide application prospects, transfer learning has become a popular and promising area in machine learning. Although there are already some valuable and impressive surveys on transfer learning, these surveys introduce approaches in a relatively isolated way and lack the recent advances in transfer learning. As the rapid expansion of the transfer learning area, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing transfer learning researches, as well as to summarize and interpret the mechanisms and the strategies in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. Different from previous surveys, this survey paper reviews over forty representative transfer learning approaches from the perspectives of data and model. The applications of transfer learning are also briefly introduced. In order to show the performance of different transfer learning models, twenty representative transfer learning models are used for experiments. The models are performed on three different datasets, i.e., Amazon Reviews, Reuters-21578, and Office-31. And the experimental results demonstrate the importance of selecting appropriate transfer learning models for different applications in practice.

Matlab Cuda 编程 官方教程。Matlab Cuda 编程 官方教程。

迁移学习（Transfer learning）作为机器学习的一大分支，已经取得了长足的进步。本手册简明地介绍迁移学习的概念与基本方法，并对其中的领域自适应问题中的若干代表性方法进行讲述。最后简要探讨迁移学习未来可能的方向。

LSTM（Long Short-Term Memory）是长短期记忆网络，是一种时间递归神经网络，适合于处理和预测时间序列中间隔和延迟相对较长的重要事件。 LSTM 已经在科技领域有了多种应用。基于 LSTM 的系统可以学习翻译语言、控制机器人、图像分析、文档摘要、语音识别图像识别、手写识别、控制聊天机器人、预测疾病、点击率和股票、合成音乐等等任务。本文档是基于LSTM原理的简单实现，有助于理解其原理。

book- by Adrian at PyImageSearch, including book 'Python and OpenCV'+'Deep learning for computer vision with python'（全三本,注意看目录）。

丰富的背景和纹理图像的生成是各类生成模型追求的终极目标，ImageNet 的生成已然 成为检验生成模型好坏的一个指标。 在各类生成模型中，GAN 是这几年比较突出的，18 年新出的 SNGAN [1]、 SAGAN [2] 让 GAN 在 ImageNet 的生成上有了长足的进步，其中较好的 SAGAN 在 ImageNet 的128x128 图像生成上的 I n c e p t i o n S c o r e ( I S ) [3] 达到了 52 分。 BigGAN 在 SAGAN 的基础上一举将 IS 提高了 100 分，达到了 166 分（真实图片也 才 233 分），可以说 BigGAN 是太秀了

使用详情见 本人博客【深度学习模型训练】使用自己的数据训练 Fast r-cnn Faster r-cnn YOLOv3 或是文件里的 DOREAD.txt

Google在YouTube中的推荐系统论文，提出了Deep&Wide;的方式