TensorFlow-basic-tutorial
basic tutorial for tensorflow1.x
and also for TensorFlow2.x
How to read these tutorials
This tutorial is divided into 10 parts, from linear regression to logistic regression. The framework used includes TensorFlow 1.x version and TensorFlow 2.x low-level API and its application of high-level API Keras
Each part is described in detail on my corresponding CSDN blog
Tensorflow 笔记 Ⅰ——TensorFlow 编程基础
Tensorflow 笔记 Ⅱ——单变量线性回归
Tensorflow 笔记 Ⅲ——多元线性回归
Tensorflow 笔记 Ⅳ——mnist手写数字识别
Tensorflow 笔记 Ⅴ——mnist全连接神经网络
Tensorflow 笔记 Ⅵ——Titanic Keras建模与应用
Tensorflow 笔记 Ⅶ——cifar10卷积神经网络
Tensorflow 笔记 Ⅷ——TensorFlow 数据增强
Tensorflow 笔记 Ⅸ——Deepdream
Tensorflow 笔记 Ⅹ——Transfer Learning for Kaggle Dataset
Tensorflow 笔记 Ⅺ——NLP 实现电影评论情感分析
Tensorflow 笔记 Ⅻ——再梳理猫狗数据集 Transfer Learning 与 TFRecord
Tensorflow 笔记 XIII——“百无聊赖”：深挖 mnist 数据集与 fashion-mnist 数据集的读取原理，经典数据的读取你真的懂了吗？
Tensorflow 笔记 XIV——生成时对抗网络：GAN 与 CGAN
Result from tutorials
The compilation environment is win10, which uses the TensorFlow-GPU version, of which TensorFlow1.15.2 used by TensorFlow1.x and TensorFlow2.0.0 used by TensorFlow2.x, some of the larger models are trained by Google Colab, and all corresponding notebooks are all Given, this part is mainly GAN and CGAN, the following will show some of the results obtained in this tutorial
How to contact me
If you encounter any problems in this tutorial notebook, you can contact me, my common QQ mailbox: [email protected] and my Google mailbox [email protected], you can also privately mail me on my CSDN blog or Raise issues on github
Finally, I briefly introduce myself as follows: My name is Guo Quanhao, winner of the national first prize in the National University Optoelectronic Design Competition, 2016 postgraduate recommendation of the University of Electronic Science and Technology, a member of the Momi Visual Lab, familiar with C, Python, stm32, deep learning, if my blog Helpful to everyone, welcome everyone’s attention