Hello, if you are going to dive into machine learning and deep learning, I would suggest you first take a look at the Resources section that I have prepared for you. Good luck with your studies! Always remember why you started learning AI!
Rustam_Z🚀, 18 October 2020
deeplearning.ai Deep Learning Specialization
- Architecture of Neural Network
- Logistic Regression
- Cost function, Forward propagation, Backpropagation, Gradient descent
- Artificial Neural Network
- Logistic Regression vs NN, Activation fanctions, L-layer NN
- Train/dev/test sets
- Regularization, dropout technique, normalizing inputs, gradient checking
- Optimization algos (mini-batch GD, GD with momentum, RMS, Adam optimization)
- Xavier/He initialization
- Hyperparameters tuning (logarithmic scale), batch normalization
- Multiclass classification, TensorFlow introduction
- How to build a successful machine learning projects
- How to prioritize the problem
- ML strategy (satisficing & optimizing metrics)
- Choose a correct train/dev/test split of your dataset
- Human-level performance (avoidable bias)
- Error Analysis
- Mismatched training and dev/test set
- Foundations of Convolutional Neural Networks
- Deep convolutional models: case studies
- Object detection
- Special applications: Face recognition & Neural style transfer
- RNN, LSTM, BRNN, GRU
- Natural Language Processing & Word Embeddings (Word2vec & GloVe)
- Sequence models & Attention mechanism (Speech recognition)
The list of resources you need for this particular specialization:
- In-depth deeplearning.ai specialization review - Daniel, ML engineer, describes about the specialization. The continuation is here.
- TA notes - Better to print out these notes and always have a look to each topic after watching the video.
- GitHub notes
- Drawn notes
- TensorFlow turorial - "What is the tensor?"
Highlighted resources:
- TensorFlow resources for learning ML - A raadmap for learning ML by TensorFlow team
- Google's ML Crash Course - Just for fast recapping
- Google AI
- MIT Deep Learning
- Stanford CS221: Artificial Intelligence
- Stanford CS229: Machine Learning
- CS230: Deep Learning - A class of DL at Stanford by Andrew Ng
- Stanford Online - More courses on Reinforcement Learning, NLP and etc.
- Brandon Rohrer and Luis Serrano - YouTube channels where you can find the great series on ML, CNN, RNN, and Neural Networks
- Krish Naik's complete DL course - In case you get stuck and don't understand the concepts try to find the easy explained video in this playlist
- Krish Naik's complete ML course
- Practical Machine Learning with Python
- freeCodeCamp.org - Deep Learning with PyTorch Course
- Machine Learning Playlist by freeCodeCamp
- Python Machine Learning & AI Mega Course - Learn 4 Different Areas of ML & AI
- TensorFlow 2.0 Complete Course
- OpenAI
- Google DeepMind
- Microsoft Research
- IBM Research
- Stanford AI Lab
- MIT AI Lab
- Google AI - Google AI Blog
- Amazon Machine Learning Guide
- Machine Learning for Humans - All in one, very short explanation of ML
- The Hundred-Page Machine Learning Book Andriy Burkov
- Grokking Deep Learning
- The Mechanics of Machine Learning
- TensorFlow CookBook
- *Python Machine Learning
- *Deep Learning for Coders with fastai and PyTorch (2020)
Must read books:
- Deep Learning with Python
- Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow 2nd edition
- Deep Learning, deeplearningbook.org, "Written by three experts in the field, Deep Learning is the only comprehensive book on the subject." —Elon Musk
- Python for Data Analysis 2nd edition
- Machine Learning Engineering Andriy Burkov
- Machine Learning Yearning Andrew Ng - After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project.
- AI and Machine Learning for Coders (2020) - Laurence Moroney, deeplearning.ai TensorFlow Developer specialization instructor
- Podcast with Andrew Ng about getting started in Deep Learning
- Andrew Ng's Machine Learning Career Advice
- Andrew Ng's Career Advice/Reading Research Papers
