Keras notes on deep learning

Recently, I found a good resource on Github, which is to use Keras to learn notes of deep learning. The notes are full of content and the data is perfect. I personally practiced all the examples in the notes and felt that I gained a lot. There’s an Easter egg at the end

The premise that

This resource is by ErhWen Kuo

Natural language processing meets deep learning

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Resources is introduced

The Github repository contains ErhWen Kuo’s notes and exercises on Keras. I hope I can find some good information and examples in the learning process and help students who want to learn how to use Keras to solve problems. Notebooks are the result of running Python 3.6 and Keras 2.1.1 on a Windows 10 machine with Nivida 1080Ti, but some of the notebooks are interlaced with Tensorflow and other functional libraries. For those of you who want to Deeplearning, it is recommended to have a GPU!

Resource outline

This xiaobian specially for the resource in the form of thinking big map made a resource outline, I hope to be able to bring you a more comprehensive understanding.

Main Contents \

Image data set/tool API

0.0: COCO API commentary and simple examples

0.1: image data set of homemade playing cards for earth cannon

0.2: Use Pillow for image processing

1. Keras API example

1.0: Use image enhancement for deep learning

1.1: How to use Keras functional API for deep learning

1.2: Build a VGG network from scratch to learn Keras

1.3: Use pre-trained models to classify objects in photos

1.4: Use image enhancement to train small data sets

1.5: Use the pre-trained convolutional network model

1.6: What visualization does the convolutional network model learn

1.7: Build Autoencoder

1.8: Sequential to sequential (SEQ-to-SEQ) learning Introduction

1.9: One-hot coding tool introduction

1.10: Circular Neural network (RNN) introduction

1.11: Difference between LSTM return sequence and return state

1.12: Use LSTM to learn English alphabetical order

2. Image Classification

2.0: Julia (Chars74K) letter image recognition

2.1: Traffic sign image recognition

2.2 character identification of Simpson cartoon image

2.3: Fashion clothing image recognition

2.4: Face key point recognition

2.5: Captcha Verification code identification

2.6: Mnist Handwritten Image Recognition (MLP)

2.7: Mnist Handwritten Image Recognition (CNN)

3. Object Recognition

3.0: YOLO object detection algorithm concept and introduction

3.1: YOLOv2 object detection example

3.2: Racoon detection -YOLOv2 model training and adjustment

3.3: Racoon detection – use of YOLOv2 model

3.4: Kangaroo detection -YOLOv2 model training and adjustment

3.5: Hands detection -YOLOv2 model training and adjustment

3.6: Simpson character detection -YOLOv2 model training and adjustment

3.7: MS COCO image detection -YOLOv2 model training and adjustment

4. Object Segmentation

5. Keypoint Detection

6. Image Caption

7. Face Detection and Recognition

7.0: Face Detection – OpenCV (Haar Feature Classifier)

7.1: Face detection – MTCNN (Multi-Task Cascaded Convolutional Networks)

7.2: Face recognition – Face detection, alignment & cropping

7.3: Face recognition – Human face feature extraction & Face classifier

7.4: Face recognition – conversion, alignment, cropping, feature extraction and comparison

7.5: Face Keypoint Detection (DLIB)

7.6: Head Pose Estimation (DLIB)

8. Natural Language Processing

8.0: Word embeddings

8.1: Use jieba to break Chinese words

8.2: Basic concepts for Word2vec word embeddings

8.3: Use jieba to analyze lyrics

8.4: Using Gensim to train Chinese word vector (WORD2vec)

Eggs part

Resource Address:

Github.com/erhwenkuo/d…

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  • Introduction to Deep Learning – Python Deep Learning, annotated version of the original code in Chinese and ebook

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