Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
Deep Learning
Search
Abhinav Tushar
September 10, 2015
Research
6
260
Deep Learning
Introductory talk on deep learning
Abhinav Tushar
September 10, 2015
Tweet
Share
More Decks by Abhinav Tushar
See All by Abhinav Tushar
the garden of eden
lepisma
0
90
Technology
lepisma
0
69
Bio-Inspired Computing
lepisma
0
89
Maestro
lepisma
0
110
War and Economics
lepisma
0
110
Other Decks in Research
See All in Research
CARMUI-NET:自動運転車遠隔監視のためのバーチャル都市プラットフォームにおける通信品質変動機能の開発と評価 / UBI85
yumulab
0
190
言語モデルによるAI創薬の進展 / Advancements in AI-Driven Drug Discovery Using Language Models
tsurubee
2
320
PhD Defence: Considering Temporal and Contextual Information for Lexical Semantic Change Detection
a1da4
1
160
言語モデルの内部機序:解析と解釈
eumesy
PRO
38
16k
Individual tree crown delineation in high resolution aerial RGB imagery using StarDist-based model
satai
3
210
VAGeo: View-specific Attention for Cross-View Object Geo-Localization
satai
3
220
Trust No Bot? Forging Confidence in AI for Software Engineering
tomzimmermann
1
190
Adaptive fusion of multi-modal remote sensing data for optimal sub-field crop yield prediction
satai
3
130
DPUを用いたマルチタスクDNN表情認識システムのFPGA実装
takuto_andtt
0
160
学生向けアンケート<データサイエンティストについて>
datascientistsociety
PRO
0
1.1k
Weekly AI Agents News!
masatoto
33
64k
AWS 音声基盤モデル トーク解析AI MiiTelの音声処理について
ken57
0
250
Featured
See All Featured
Rebuilding a faster, lazier Slack
samanthasiow
81
9k
The Straight Up "How To Draw Better" Workshop
denniskardys
233
140k
Scaling GitHub
holman
459
140k
Practical Tips for Bootstrapping Information Extraction Pipelines
honnibal
PRO
19
1.2k
Bash Introduction
62gerente
612
210k
No one is an island. Learnings from fostering a developers community.
thoeni
21
3.3k
個人開発の失敗を避けるイケてる考え方 / tips for indie hackers
panda_program
105
19k
Raft: Consensus for Rubyists
vanstee
137
6.9k
Cheating the UX When There Is Nothing More to Optimize - PixelPioneers
stephaniewalter
280
13k
How STYLIGHT went responsive
nonsquared
100
5.5k
How to Create Impact in a Changing Tech Landscape [PerfNow 2023]
tammyeverts
52
2.4k
Thoughts on Productivity
jonyablonski
69
4.6k
Transcript
D E E P L E A R N I
N G
models AE / SAE RBM / DBN CNN RNN /
LSTM Memnet / NTM agenda questions What ? Why ? How ? Next ?
what why how next What ? AI technique for learning
multiple levels of abstractions directly from raw information
what why how next Primitive rule based AI Tailored systems
Hand Crafted Program Output Input
what why how next Classical machine learning Learning from custom
features Hand Crafted Features Learning System Output Input
what why how next Deep Learning based AI Learn everything
Learned Features (Lower Level) Learned Features (Higher Level) Learning System Output Input
None
https://www.youtube.com/watch?v=Q70ulPJW3Gk PPTX PDF (link to video below)
With the capacity to represent the world in signs and
symbols, comes the capacity to change it Elizabeth Kolbert (The Sixth Extinction) “
Why The buzz ?
what why how next Google Trends Deep Learning
what why how next
Crude timeline of Neural Networks 1950 1980 1990 2000 Perceptron
Backprop & Application NN Winter
2010 Stacking RBMs Deep Learning fuss
HUGE DATA Large Synoptic Survey Telescope (2022) 30 TB/night
HUGE CAPABILITIES GPGPU ~20x speedup Powerful Clusters
HUGE SUCCESS Speech, text understanding Robotics / Computer Vision Business
/ Big Data Artificial General Intelligence (AGI)
How its done ?
what why how next Shallow Network ℎ ℎ = (,
0) = ′(ℎ, 1) = (, ) minimize
what why how next Deep Network
what why how next Deep Network More abstract features Stellar
performance Vanishing Gradient Overfitting
what why how next Autoencoder ℎ Unsupervised Feature Learning
what why how next Stacked Autoencoder Y. Bengio et. all;
Greedy Layer-Wise Training of Deep Networks
what why how next Stacked Autoencoder 1. Unsupervised, layer by
layer pretraining 2. Supervised fine tuning
what why how next Deep Belief Network 2006 breakthrough Stacking
Restricted Boltzmann Machines (RBMs) Hinton, G. E., Osindero, S. and Teh, Y.; A fast learning algorithm for deep belief nets
Rethinking Computer Vision
what why how next Traditional Image Classification pipeline Feature Extraction
(SIFT, SURF etc.) Classifier (SVM, NN etc.)
what why how next Convolutional Neural Network Images taken from
deeplearning.net
what why how next Convolutional Neural Network
what why how next Convolutional Neural Network Images taken from
deeplearning.net
what why how next Convolutional Neural Network
what why how next The Starry Night Vincent van Gogh
Leon A. Gatys, Alexander S. Ecker and Matthias Bethge; A Neural Algorithm of Artistic Style
what why how next
what why how next Scene Description CNN + RNN Oriol
Vinyals et. all; Show and Tell: A Neural Image Caption Generator
Learning Sequences
what why how next Recurrent Neural Network Simple Elman Version
ℎ ℎ = ( , ℎ−1 , 0, 1) = ′(ℎ , 2)
what why how next Long Short Term Memory (LSTM) add
memory cells learn access mechanism Sepp Hochreiter and Jürgen Schmidhuber; Long short-term memory
None
what why how next
what why how next Fooling Deep Networks Anh Nguyen, Jason
Yosinski, Jeff Clune; Deep Neural Networks are Easily Fooled
Next Cool things to try
what why how next Hyperparameter optimization bayesian Optimization methods adadelta,
rmsprop . . . Regularization dropout, dither . . .
what why how next Attention & Memory NTMs, Memory Networks,
Stack RNNs . . . NLP Translation, description
what why how next Cognitive Hardware FPGA, GPU, Neuromorphic Chips
Scalable DL map-reduce, compute clusters
what why how next Deep Reinforcement Learning deepmindish things, deep
Q learning Energy models RBMs, DBNs . . .
https://www.reddit.com/r/MachineLearning/wiki
Theano (Python) | Torch (lua) | Caffe (C++) Github is
a friend
@AbhinavTushar ?