Online Lecture
Online Deep Learning Courses
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​
‌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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