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

CMU_11-785_Intro_Deep_Learning_Spring2019 (22 files)
F18 Lecture 1 - Introduction to Deep Learning-aPY-KC6zeeI.mp4 148.82MB
F18 Lecture 2 - The Neural Net as a Universal Approximator-eeq2aG9TKY8.mp4 178.28MB
F18 Lecture 3 - Neural Network Training-x9rO7U6wA54.mp4 170.26MB
F18 Lecture 4 - Backpropagation-hm_Zg0PgUN8.mp4 184.20MB
F18 Lecture 5 - Backpropagation (cont.)-1XgBMUMAQPU.mp4 196.39MB
F18 Lecture 6 - Optimization Part 1-qpKkBzBZcJ8.mp4 120.97MB
F18 Lecture 7 - Optimization Part 2-m5gRfFsSuS4.mp4 171.69MB
F18 Lecture 8 - Convolutional Neural Networks (Part 1)-rr1vJizA1qE.mp4 206.07MB
F18 Lecture 9 - Convolutional Neural Networks (Part 2)-H2B0TrpDW_M.mp4 207.45MB
F18 Lecture 10 - Recurrent Neural Networks (RNNs) (Part 1)-FgwjF6rCsz8.mp4 215.29MB
F18 Lecture 11 - Recurrent Neural Networks (RNNs) (Part 2)-BZsMQhq74d0.mp4 202.57MB
F18 Lecture 12 - Loss functions and sequence prediction for RNNs-s4QBIBVsW18.mp4 214.65MB
F18 Logistics-QrtrF_w2LuY.mp4 63.03MB
F18 Recitation 0 (1_2) - Python Primer-XlaIQ9kljJI.mp4 46.74MB
F18 Recitation 0 (2_2) - Numpy Primer-HL4MTgbZvlg.mp4 26.54MB
F18 Recitation 1 - Amazon Web Services-9_KReiIZwLE.mp4 184.13MB
F18 Recitation 2 - Your First Deep Learning Code-mWPNS4WQ900.mp4 123.13MB
F18 Recitation 4 - Tensorboard and Understanding Data-LcaRZCY1WIA.mp4 167.65MB
F18 Recitation 6 - HW2 Primer-7SEdt9Nw1xU.mp4 110.96MB
F18 Recitation 7- RNNs-Mr5dHOcgD5Q.mp4 190.93MB
F18 Recitation 8 - Connectionist Temporal Classification (CTC)-GxtMbmv169o.mp4 31.44MB
F18 Recitation 9 - Attention Networks HW4 Primer-aJvw9aBFE70.mp4 112.17MB
Type: Course

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= {}

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