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
70
Bio-Inspired Computing
lepisma
0
89
Maestro
lepisma
0
110
War and Economics
lepisma
0
110
Other Decks in Research
See All in Research
LLM-as-a-Judge: 文章をLLMで評価する@教育機関DXシンポ
k141303
3
750
Cross-Media Information Spaces and Architectures
signer
PRO
0
210
3D Gaussian Splattingによる高効率な新規視点合成技術とその応用
muskie82
0
710
インドネシアのQA事情を紹介するの
yujijs
0
190
Batch Processing Algorithm for Elliptic Curve Operations and Its AVX-512 Implementation
herumi
0
160
Collaborative Development of Foundation Models at Japanese Academia
odashi
2
550
AIによる画像認識技術の進化 -25年の技術変遷を振り返る-
hf149
6
1.3k
BtoB プロダクトにおけるインサイトマネジメントの必要性 現場ドリブンなカミナシがインサイトマネジメントに取り組むワケ / Why field-driven Kaminashi is working on insight management
kaminashi
1
420
SkySense : A Multi-Modal Remote Sensing Foundation Model Towards Universal Interpretation for Earth Observation Imagery
satai
3
160
コーパスを丸呑みしたモデルから言語の何がわかるか
eumesy
PRO
11
3.6k
DeepSeek-R1の論文から読み解く背景技術
personabb
3
600
自然由来エネルギーの揺らぎによるワークロード移動を想定した超個体データセンターシステムの検討進捗状況
kikuzo
0
110
Featured
See All Featured
How to Create Impact in a Changing Tech Landscape [PerfNow 2023]
tammyeverts
52
2.7k
Code Review Best Practice
trishagee
68
18k
Rails Girls Zürich Keynote
gr2m
94
13k
Statistics for Hackers
jakevdp
799
220k
Fantastic passwords and where to find them - at NoRuKo
philnash
51
3.2k
We Have a Design System, Now What?
morganepeng
52
7.6k
[Rails World 2023 - Day 1 Closing Keynote] - The Magic of Rails
eileencodes
34
2.2k
[RailsConf 2023 Opening Keynote] The Magic of Rails
eileencodes
29
9.5k
Refactoring Trust on Your Teams (GOTO; Chicago 2020)
rmw
34
3k
Learning to Love Humans: Emotional Interface Design
aarron
273
40k
Making Projects Easy
brettharned
116
6.2k
StorybookのUI Testing Handbookを読んだ
zakiyama
30
5.7k
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 ?