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Online Deep Learning Courses

  1. Deeplearning.ai(coursera): Deep Learning Specialization

  2. MIT Intro. Deep Learning

  3. Stanford CS213n

  4. Reinforcement Learning

    • David Silver: Intro. to Reinforcement Learning

    • OpenAI

Recommended Schedule for Coursera

Coursera lectures in 5 weeks

  • Week 1: Neural Networks and Deep Learning(Course 1)

    • Introduction to deep learning (2hr)

    • Neural Networks Basics (8hr)

    • Shallow neural networks (5hr)

    • Deep Neural Networks (5hr)

  • Week 2: Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization (Course 2)

    • Practical aspects of Deep Learning (8hr)

    • Optimization algorithms (5hr)

    • Hyperparameter tuning, Batch Normalization and Programming Frameworks (5hr)

    • Exercise: Train ImageNet on LeNet with Keras or PyTorch

  • Week 3: Structuring Machine Learning Projects (Course 3)

    • ML Strategy (1) (2hr)

    • ML Strategy (2) (3hr)

  • Week 3, 4: Convolutional Neural Networks (Course 4)

    • Foundations of Convolutional Neural Networks (6hr)

    • Deep convolutional models: case studies (5hr)

    • Object detection(4hr)

    • Special applications: Face recognition & Neural style transfer(5hr)

    • Exercise: Train ImageNet on AlexNet with Keras or PyTorch

  • Week 5: Project

    • Project 1: Vehicle/Pedestrian Detection

    • Project 2: Lane Detection with Deep Learning

    • Project 3: Face Recognition

  • Extra Project

    • End to End Deep Learning for Autonomous Driving on AirSim

    • Lane Detection on AirSim

Youtube Channel on CNN

Lex Fridman​

‌Lex Fridman is a professor at MIT, teachingDeep Learning.‌ The videos on this channel include conversations with extraordinary individuals that are pioneers or top researchers of AI fields‌ Also, includes MIT lectures series on Deep Learning: Deep Reinforcement Learning, RNN, CNN , Self-Driving Cars etc​

Yannic Kilcher​

‌You can get an excellent explanation on research papers covering the state of the art machine learning techniques, including DETR, DQN.​Yannic KilcherI make videos about machine learning research papers, programming, and issues of the AI community and the broader impact of AI in society. Twitter: https://t...www.youtube.com‌

3Blue1Brown​

‌Excellent explanations on topics such as linear algebra, calculus, and partial differentiation. When studying Neural Networks. It is also essential to understand concepts such as backpropagation, gradient descent, and neural networks in general.​

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