Deep Learning (DL)
One of the most in-demand skills in AI is DL. You will study the fundamentals of DL, how to construct neural networks, and how to lead successful machine learning projects in this course. Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and other topics will be discussed.
What Will You Learn?
- Understand what Artificial Neural Networks are and how they work.
- Incorporate Artificial Neural Networks into your daily routine.
- Convolutional Neural Networks: What They Are and How They Work
- Incorporate Convolutional Neural Networks into your daily routine.
- Recognize the logic behind Recurrent Neural Networks.
- Incorporate Recurrent Neural Networks into your daily routine.
- Recognize the logic behind Self-Organizing Maps.
- Incorporate Self-Organizing Maps into your design
Lecture 1 – Class Introduction and Logistics00:00
Lecture 2 – Deep Learning Intuition00:00
Lecture 3 – Full-Cycle Deep Learning Projects00:00
Lecture 4 – Adversarial Attacks / GANs00:00
Lecture 5 – AI + Healthcare00:00
Lecture 6 – Deep Learning Project Strategy00:00
Lecture 7 – Interpretability of Neural Network00:00
Lecture 8 – Career Advice / Reading Research Papers00:00
Lecture 9 – Deep Reinforcement Learning01:20:19
Lecture 10 – Chatbots / Closing Remarks00:00