This repo is a collection of self-learning materials for Deep Learning. It includes examples, Notebook, books, lectures and suggestions for learning Deep Learning.
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means recommended.
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Before your started. Pls read AI Career Pathways: Put Yourself on the Right Track, recommended by Andrew NG and YC etc. It will let you know:
- Roles and tasks in AI ecosystem
- Your current role
- Target role (Skill set and tasks)
- Python and Numpy
- Basic Neural Network components:
- Loss functions
- Layers & operations (FC, Relu, CNN, RNN etc)
- Optimizations (SGD, adam)
- Deep Learning Frameworks
- PyTorch, Keras, TensorFlow
- BigDL and Analytics Zoo for big data
- Examples in different areas
- Computer Vision (CV)
- Natural Language Processing (NLP)
- Reinforcement Learning (RL)
- Generative Adversarial Networks (GAN)
- Transfer Learning & Fine-tune
- Computer Vision (CV)
- Natural Language Processing (NLP)
- Serving Trained models (Into production)
- TensorFlow & PyTorch Serving
- KubeFlow
- Analytics-Zoo Web & Cluster Serving
After talking with serveral people, who are learning deep learning by themselves. I found that in this stage, learning too much without a correct direction is very inefficient. To avoid going into wrong directions, I highly recommend them to go over Standford CS231n by Feifei Li. This course will give you an overview & basic knowledge of deep learning, and let you know the mapping between problem & solutions.
Tips: Focus on applications and examples. Search with Google, and try several solutions if possible.
How to: Choose a DL framework, and learn with examples. DIY like building with LeGo. At this stage, Kaggle and Google colab will be your best playground.
- Machine Learning
- Math:
- Numerical Computation
- Line algebra
- Probability and Information Theory
- Detailed deep learning knowledge in different areas
- Computer Vision (CV)
- Natural Language Processing (NLP)
- Reinforcement Learning (RL)
- Algorithms in Deep Learning
- Efficient methods & hardware for Deep Learning
- Distall, low-precision, compression & quantization etc
- Methods for training & inference
- Hardwares for training & inference
Tips: Pls focus on one of them, i.e., become expert in one area (or a few area) rather than know everything but not good at any of them. Pay more attention on Deep Learning papers rather than blogs.
How to: Choose a most interesting or relevant (with your work) area, and dig in. Then, keep tracking this area and become an expert.
Please refer to OpenVINO™ Toolkit - Open Model Zoo repository for more details.
- Analytics-Zoo Streaming Object Detection & Text Classifcation Scala Example
- Analytics-Zoo Streaming Object Detection & Text Classifcation Python Example
- Analytics-Zoo Image Classification with Redis & Redis Streams
- Analytics-Zoo OpenVINO ResNet_v1_50 Image Classifcation Scala Example
- Analytics-Zoo OpenVINO ResNet_v1_50 Image Classifcation Python Example
- Handwritten Recognization: minist
- TensorFlow pre-trained Image Classification models
- TensorFlow Image Classification Preprocessing
- TensorFlow pre-trained Object Detection models
- TensorFlow IMDB
OpenVINO optimizes and loads TensorFlow pre-trained models:
- OpenVINO load TensorFlow resnet_v1_50
- OpenVINO load TensorFlow inception_v3
- OpenVINO load TensorFlow vgg_19
- OpenVINO load TensorFlow mobilenet_v1
- Subscribe on deeplearning.ai, it will send blog and recommended paper throught email.
- Subscribe deep learning related topics on [Medium][https://medium.com/].
- Colah
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Deep Learning with Python by François Chollet, Notebook*
Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville.- Neural Networks and Deep Learning by Michael Nielsen.
- Python for Data Analysis by We McKinney
- Linear Algebra and Learning from Data by Gilbert Strang
- Reinforcement Learning: An Introduction by RichardS Sutton
- Mathematics for Machine Learning
- Dive into Deep Learning
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Stanford CS231n: Convolutional Neural Networks for Visual Recognition, Spring 2017, Youtube Summary- Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning, Code and Notebook
- Google Coding TensorFlow
- deeplearning.ai
- Neural Networks and Deep Learning (Course 1 of the Deep Learning Specialization)
- Improving deep neural networks: hyperparameter tuning, regularization and optimization (Course 2 of the Deep Learning Specialization)
- Structuring Machine Learning Projects (Course 3 of the Deep Learning Specialization)
- Convolutional Neural Networks (Course 4 of the Deep Learning Specialization)
- Stanford CS230: Deep Learning | Autumn 2018
- Stanford CS229: Machine Learning
- MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
- deeplearning.ai
- Neural Networks and Deep Learning (Course 1 of the Deep Learning Specialization)
- Improving deep neural networks: hyperparameter tuning, regularization and optimization (Course 2 of the Deep Learning Specialization)
- Structuring Machine Learning Projects (Course 3 of the Deep Learning Specialization)
- Convolutional Neural Networks (Course 4 of the Deep Learning Specialization)
- Stanford CS224U: Natural Language Understanding | Spring 2019
- Stanford CS224N: Natural Language Processing with Deep Learning | Winter 2019, Youtube
- Stanford CS234: Reinforcement Learning | Winter 2019, Youtube
- Essence of linear algebra