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| Added | 2019-02-19 21:38:57 |
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| ID | 4119 |
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| Uploaded by |  joecohen |
| Folder | CMU_11-785_Intro_Deep_Learning_Spring2019 |
| Num files | 22 files [See full list] |
| Mirrors | 17 complete, 1 downloading = 18 mirror(s) total [Log in to see full list] |
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
Tags:
Bibtex:
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/}
}
