CMU 11-785 Introduction to Deep Learning Spring 2019
Bhiksha Raj



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CMU_11-785_Intro_Deep_Learning_Spring2019 (22 files)
F18 Lecture 1 - Introduction to Deep Learning-aPY-KC6zeeI.mp4148.82MB
F18 Lecture 2 - The Neural Net as a Universal Approximator-eeq2aG9TKY8.mp4178.28MB
F18 Lecture 3 - Neural Network Training-x9rO7U6wA54.mp4170.26MB
F18 Lecture 4 - Backpropagation-hm_Zg0PgUN8.mp4184.20MB
F18 Lecture 5 - Backpropagation (cont.)-1XgBMUMAQPU.mp4196.39MB
F18 Lecture 6 - Optimization Part 1-qpKkBzBZcJ8.mp4120.97MB
F18 Lecture 7 - Optimization Part 2-m5gRfFsSuS4.mp4171.69MB
F18 Lecture 8 - Convolutional Neural Networks (Part 1)-rr1vJizA1qE.mp4206.07MB
F18 Lecture 9 - Convolutional Neural Networks (Part 2)-H2B0TrpDW_M.mp4207.45MB
F18 Lecture 10 - Recurrent Neural Networks (RNNs) (Part 1)-FgwjF6rCsz8.mp4215.29MB
F18 Lecture 11 - Recurrent Neural Networks (RNNs) (Part 2)-BZsMQhq74d0.mp4202.57MB
F18 Lecture 12 - Loss functions and sequence prediction for RNNs-s4QBIBVsW18.mp4214.65MB
F18 Logistics-QrtrF_w2LuY.mp463.03MB
F18 Recitation 0 (1_2) - Python Primer-XlaIQ9kljJI.mp446.74MB
F18 Recitation 0 (2_2) - Numpy Primer-HL4MTgbZvlg.mp426.54MB
F18 Recitation 1 - Amazon Web Services-9_KReiIZwLE.mp4184.13MB
F18 Recitation 2 - Your First Deep Learning Code-mWPNS4WQ900.mp4123.13MB
F18 Recitation 4 - Tensorboard and Understanding Data-LcaRZCY1WIA.mp4167.65MB
F18 Recitation 6 - HW2 Primer-7SEdt9Nw1xU.mp4110.96MB
F18 Recitation 7- RNNs-Mr5dHOcgD5Q.mp4190.93MB
F18 Recitation 8 - Connectionist Temporal Classification (CTC)-GxtMbmv169o.mp431.44MB
F18 Recitation 9 - Attention Networks HW4 Primer-aJvw9aBFE70.mp4112.17MB
Type: Course
Tags:

Bibtex:
@article{,
title= {CMU 11-785 Introduction to Deep Learning Spring 2019},
keywords= {},
author= {Bhiksha Raj},
abstract= {“Deep Learning” systems, typified by deep neural networks, are increasingly taking over all AI tasks, ranging from language understanding, and speech and image recognition, to machine translation, planning, and even game playing and autonomous driving. As a result, expertise in deep learning is fast changing from an esoteric desirable to a mandatory prerequisite in many advanced academic settings, and a large advantage in the industrial job market.

In this course we will learn about the basics of deep neural networks, and their applications to various AI tasks. By the end of the course, it is expected that students will have significant familiarity with the subject, and be able to apply Deep Learning to a variety of tasks. They will also be positioned to understand much of the current literature on the topic and extend their knowledge through further study.},
terms= {},
license= {},
superseded= {},
url= {http://deeplearning.cs.cmu.edu/}
}