Probably the most comprehensive collection of Tensorflow learning resources ever

In the previous Tensorflow series, we taught you the installation of Tensorflow, Tensorflow syntax, basic operations, some principles of CNN, and project practice. This article will summarize the Tensorflow pure dry learning resources for beginners. If you want to learn more about Tensorflow, please click the blue word at the top and follow our wechat official account.

Tensorflow tutorial resources

Tensorflow tutorial and code examples for beginners:

https://github.com/aymericdamien/TensorFlow-Examples

This tutorial not only provides some classic data sets, but also takes you from the simplest implementation of “Hello World”, to classical machine learning algorithms, to common neural network models. It is the best tutorial for beginners to learn Tensorflow.

2) From Tensorflow basics to interesting project applications:

https://github.com/pkmital/tensorflow_tutorials

Is also suitable for novice tutorials, from installation to project combat, teach you to build a neural network of their own.

3) TensorFlow tutorial with Jupyter Notebook

https://github.com/sjchoi86/Tensorflow-101

Jupyter Notebook is an interactive development tool that supports over 40 programming languages, running code in real time, sharing documents, data visualization, markdown support, and more. Suitable for machine learning, statistical modeling data processing, feature extraction and other fields.

4) Build your first TensorFlow Android app:

https://omid.al/posts/2017-02-20-Tutorial-Build-Your-First-Tensorflow-Android-App.html

This tutorial helps you introduce a tensor flow model into an Android application from scratch.

5)Tensorflow code exercises

https://github.com/terryum/TensorFlow_Exercises

An easy to hard Tensorflow code exercise manual. Ideal for those learning Tensorflow.

Here are some of the best Tensorflow videos:

Tensorflow video Resources

1)TF Girls Training Guide:

https://www.youtube.com/watchv=TrWqRMJZU8A&list=PLwY2GJhAPWRcZxxVFpNhhfivuW0kX15yG&index=2

Tensorflow is a public video course starting from scratch. The course is basic and basic, but the knowledge points are very detailed.

Tensorflow Tensorflow

https://www.youtube.com/watchv=eAtGqz8ytOI&list=PLjSwXXbVlK6IHzhLOMpwHHLjYmINRstrk

Very good course, recommended to everyone.

3) Of course, li Hongyi’s deep learning course at ** Li University is also worth recommending to you:

https://www.bilibili.com/video/av9770302/

4) For those who are good at English, I would like to recommend some English courses to you.

https://www.youtube.com/watch?v=vq2nnJ4g6N0;

http://bit.ly/1OX8s8Y;

5) How can you miss the Tensorflow series of Stanford University courses?!

Without further ado, go straight to the link:

https://www.youtube.com/watch?v=g-EvyKpZjmQ&index=1&list=PLIDllPt3EQZoS8gCP3cw273Cq9puuPLTg

Course Home page:

http://web.stanford.edu/class/cs20si/index.html

Download all PPT and notes for this course.

https://pan.baidu.com/s/1o8uOQpW

Github address:

chiphuyen/tf-stanford-tutorials

6) Finally, how can you forget the video tutorials posted on Tensorflow website? They are very helpful for beginners to get started with Tensorflow:

https://developers.google.cn/machine-learning/crash-course/

Now that you have a solid foundation in Tensorflow from the resource documentation and video tutorials above, why not do some more advanced practical projects to improve yourself? So next we recommend some project actual combat resources.

Tensorflow Project Resources:

1) A case to implement random handwriting generation of Alex Graves’ paper:

https://github.com/hardmaru/write-rnn-tensorflow

2) Generation against text to image synthesis based on Tensorflow:

https://github.com/zsdonghao/text-to-image

As shown below, the project is based on Tensorflow’s DCGAN model and teaches you step by step from confrontation to text generation to image synthesis.

3) Attention-based image caption generator:

https://github.com/yunjey/show-attend-and-tell

The model introduces an attention-based image title generator. You can turn its attention to the relevant parts of the image while generating each word.

4) Neural network coloring grayscale image:

https://github.com/pavelgonchar/colornet

A very interesting and very wide application scenarios of a project, the use of neural networks to color grayscale images.

5) Simple embedded text classifier based on FastText in Facebook:

https://github.com/apcode/tensorflow_fasttext

The project grew out of an idea for FastText in Facebook and was implemented in Tensorflow. FastText is a FastText classifier that provides a simple and efficient method for text classification and representation learning.

6) Realizing “Convolutional Neural Network Based on Sentence Classification” with Tensorflow:

https://github.com/dennybritz/cnn-text-classification-tf

7) Train TensorFlow neural network using OpenStreetMap function and satellite images:

https://github.com/jtoy/awesome-tensorflow

The project trained neural networks using OpenStreetMap (OSM) data to classify features in satellite images.

8) Tenflow implementing YOLO: real-time object detection, and to support real time in mobile devices running on a small project https://github.com/thtrieu/darkflow, researchers in the field of computer vision best benefits.