Plan.md

Tài liệu học tập

1. Deep Learning

1.1. Course

• Deep Learning Specialization

  • Neural Networks and Deep Learning

  • Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

  • Structuring Machine Learning Projects

  • Convolutional Neural Networks

  • Sequence Models

  • Course note

    • DeepLearning.ai Courses Notes
    • Deep Learning Specialization by Andrew Ng — 21 Lessons Learned
    • Computer Vision by Andrew Ng — 11 Lessons Learned

• CS294-158 Deep Unsupervised Learning Spring 2019

• 5194.01: Introduction to High-Performance Deep Learning

• CS 236: Deep Generative Models

• CSC 321 Winter 2018 Intro to Neural Networks and Machine Learning

• CMSC 35246 Deep Learning Spring 2017, University of Chicago

• Introduction to Deep Learning Spring 2018

• CS 598 LAZ: Cutting-Edge Trends in Deep Learning and Recognition

• Deep Learning

• CS7015: Deep Learning

1.2. Book

• Sách DL Ian Goodfellow
• Deep Learning Book Series · Introduction

1.3. Other

1.4. Project

1.5. Topic

2. Computer Vision

2.1. Course

• CS231n: Convolutional Neural Networks for Visual Recognition

2.2. Book

2.3. Other

2.4. Project

• Person/Vehicle/Object detection

  • Free Anchor
  • NAS

• Multiple Object Tracking

  • Sort, DeepSort

• Video object detection

• Crowd counting

• Person Re-identification

• Unsupervised Image Classification

• Image retrieval

• Face Recognition

• Video Summarization

• Text detection

2.5. Topic

• Các kiến trúc mạng CNN
• Object Detection
• Face Recognition
• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

3. NLP

3.1. Course

• CS224n: Natural Language Processing with Deep Learning
• CS224U: Natural Language Understanding
• CMU CS 11-747, Spring 2019 Neural Networks for NLP

3.2. Book

3.3. Other

3.4. Project

• Text Summarization

• NER

• IE (Event extraction, relation extraction,...)

• Semantic Role Labeling

• Coreference Resolution

• Semantic Textual Similarity

• Sửa lỗi chính tả VN

3.5. Topic

4. Bigdata

4.1. Course

4.2. Book

4.3. Other

4.4. Project

4.5. Topic

5. Network Analysis

5.1. Course

5.2. Book

5.3. Other

5.4. Project

5.5. Topic

6. Library / Framework

6.1. Tensorflow

6.1.1. Sách

• Hands-on Machine Learning with Scikit-Learn and TensorFlow

6.2.2. Khác

• Tensorflow basic

  • Introduction to Tensorflow
  • Effective TensorFlow
  • Jonathan Hui blog
  • Easy-TensorFlow

• Tensorflow examples

  • TensorFlow Tutorials Hvass-Labs
  • TensorFlow Examples
  • TensorFlow-Tutorials Nlintz

• Tensorflow advanced

  • Highlights from the TensorFlow Developer Summit, 2018
  • TensorFlow Dev Summit 2018
  • TensorFlow Dev Summit 2017
  • Simple tutorials on deep learning using TensorFlow Eager
  • Tensorflow and deep learning without a PhD series

• Course

  • CS 20: Tensorflow for Deep Learning Research
  • Creative Applications of Deep Learning with TensorFlow
  • COGNITIVE CLASS Deep Learning with TensorFlow

• Other

  • Tensorflow Project Template
  • Structuring Your TensorFlow Models
  • How Does The Machine Learning Library TensorFlow Work?
  • Tensorflow 2017 Tutorials

• Reference

  • TensorFlow Guide
  • Awesome TensorFlow

6.2. Keras

6.2.1. Sách

• Deep Learning with Python

6.2.2. Khác

• Keras resources
• The Keras blog

6.3. Gensim

6.4. NLTK

6.5. Spacy

7. Chủ đề khác

7.1. Course

• How to Win a Data Science Competition: Learn from Top Kagglers
  • Materials for "How to Win a Data Science Competition: Learn from Top Kagglers" course
  • AML-Specialization-Exercises-Coursera

7.2. Book

7.3. Other

7.4. Project

7.5. Topic

• ML/DL tips

• Optimizer: Momentum, Adam,...

  • An overview of gradient descent optimization algorithms
  • Why Momentum Really Works

• Dimension Reduction: manifold, tSNE, PCA

  • The Ultimate Guide to 12 Dimensionality Reduction Techniques (with Python codes)
  • Locally Linear Embedding (LLE)
  • Principal Component Analysis
  • An intuitive explanation of PCA (Principal Component Analysis)
  • How to Use t-SNE Effectively

• Clustering: k-mean, gmm, hierarchical

  • The 5 Clustering Algorithms Data Scientists Need to Know
  • Introduction to text clustering

• Tuning Hyper-Parameter

  • An Introductory Example of Bayesian Optimization in Python with Hyperopt

• Metric

• Batch Normalization, Dropout

• Imbalanced Class problem

• Topic model: LDA

• CNN Architectures