AI6 Cycle 1 Learning Materials


Pytorch Learning materials for AI6 Pytorch.
Stanford STAT385 Theories of Deep Learning For discussions about the weekly lectures and readings for Stanford STAT385 Theories of Deep Learning (<a href="https://stats385.github.io">https://stats385.github.io</a>). Links to the original recorded lecture videos and readings for each week will be posted here.
Berkeley CS294 Deep Reinforcement Learning For discussions about the materials from UC Berkeley CS294 on Deep Reinforcement Learning (<a href="http://rll.berkeley.edu/deeprlcoursesp17/">http://rll.berkeley.edu/deeprlcoursesp17/</a>)
Fast.AI <a href="http://fast.ai">fast.ai</a> v2 has their own forums so we would be repeating it here, but here’s the link to <a href="http://fast.ai">fast.ai</a> v2 resources: <a href="http://forums.fast.ai/t/welcome-to-part-1-v2/5787">http://forums.fast.ai/t/welcome-to-part-1-v2/5787</a>

About the AI6 Cycle 1 Learning Materials category [AI6 Cycle 1 Learning Materials] (1)
Deeplearning.ai Quizzes to do live during the session [AI6 Cycle 1 Learning Materials] (1)
Fast.ai Quizzes to do live during the session [Fast.AI] (1)
Lesson 10 Resources [Fast.AI] (1)
KL: Pytorch Lesson Series [Pytorch] (1)
Lesson 2 notebook skeleton [Fast.AI] (1)
[AI6 KL] Mid-term Project [Fast.AI] (7)
Potential AI6 Learning Materials [AI6 Cycle 1 Learning Materials] (1)
Kernel not responding [Fast.AI] (8)
Lesson 5 AI-KL Summary and Post Questions [Fast.AI] (1)
Structuring image data. Train test valid sample split [Fast.AI] (3)
Official launch of Practical Deep Learning for Coders 2018 [Fast.AI] (2)
Lesson 4 AI6 KL Summary and Post-Questions [Fast.AI] (3)
Lesson 3 AI6 KL Summary [Fast.AI] (1)
Lagrangian formulation of backpropagation [Stanford STAT385 Theories of Deep Learning] (9)
Wiki: Lecture 2 – Overview of Deep Learning from a Practical Point of View [Stanford STAT385 Theories of Deep Learning] (3)
Wiki: Discussion on STAT385 Lecture 1 Video and Readings [Stanford STAT385 Theories of Deep Learning] (1)
Wiki: Lecture 10 – CNNs in view of Sparse Coding [Stanford STAT385 Theories of Deep Learning] (1)
Wiki: Lecture 9 – What's Missing in Deep Learning [Stanford STAT385 Theories of Deep Learning] (1)
Wiki: Lecture 8 – Topology and Geometry of Half-rectified Network Optimization [Stanford STAT385 Theories of Deep Learning] (1)
Wiki: Lecture 7 – Understanding and Improving Deep Learning With Random Matrix Theory [Stanford STAT385 Theories of Deep Learning] (1)
Wiki: Lecture 6 – Views of Deep Networks from Reproducing Kernel Hilbert Spaces [Stanford STAT385 Theories of Deep Learning] (1)
Wiki: Lecture 5 – When Can Deep Networks Avoid the Curse of Dimensionality [Stanford STAT385 Theories of Deep Learning] (1)
Wiki: Lecture 4 – Covnets from First Principles [Stanford STAT385 Theories of Deep Learning] (1)
Wiki: Lecture 3 – Harmonic Analysis of Deep Convolutional Neural Networks [Stanford STAT385 Theories of Deep Learning] (1)