Name: CMU 11-785 Introduction to Deep Learning Spring 2019
Creator: Bhiksha Raj
License: https://academictorrents.com/nolicensespecified

Info hash

9bd4c5afe4f80a0b0e519923ff1eb5c76e5c07a9

Last mirror activity

4d,20:51:32 ago

Size

3.27GB (3,273,360,608 bytes)

Added

2019-02-19 21:38:57

Views

1569

Hits

14322

4119

Type

multi

Downloaded

21904 time(s)

Uploaded by

joecohen

Folder

CMU_11-785_Intro_Deep_Learning_Spring2019

Num files

22 files

File list
[Hide list]

Path	Size
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

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:

@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/}
}

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

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