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<!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Lecture 1 - NNML 2020</title>
<meta name="description" content="NNML">
<meta name="author" content="Emre Neftci">
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<body>
<div class="reveal">
<!-- Any section element inside of this container is displayed as a slide -->
<div class="slides">
<section data-external="title.html" data-vertical-align-top data-background-color=#B2BA67 >
</section>
<section data-markdown data-vertical-align-top><textarea data-template>
## Example: Keyword Detection
- Every second, the microphone will collect roughly 44,000 samples. Each sample is a measurement of the amplitude of the sound wave
- How to detect "Alexa" , "OK, Google" from the raw audio?
</textarea>
</section>
<section data-markdown data-vertical-align-top><textarea data-template>
## Example: Face Detection
- Every image is a collection of numbers indicating the intensity of the color channel at a given pixel
- How to detect the presence of a face in a picture?
</textarea>
</section>
<section data-markdown data-vertical-align-top><textarea data-template>
## Machine Learning Approach
<ul>
<li /> The goal of Machine Learning (ML) is to learn to solve a problem by extracting patterns from raw data
<li class=fragment /> The performance of ML models depends heavily on the representation of the data
<li class=fragment /> Neural Networks are particularly efficient at learning these representations
</ul>
</textarea></section>
<section data-markdown data-vertical-align-top><textarea data-template>
## History of Artificial Intelligence and Neural Networks
<div class=fragment>
<p>Early AI shortcomings: </p>
<ul>
<li/> Symbol based processing lacks domain-specific knowledge
<li/> Combinatorial explosion: solutions to small problems did not scale to exponentially large problems.
<li/> Solving a problem in principle is very different than solving it practically
</ul>
</div>
</textarea>
</section>
<section data-markdown><textarea data-template>
## AI's Moonshot
<img src=images/image.I6QZC0.png class="stretch"/>
</textarea>
</section>
<section>
<h2> Modern Artificial Intelligence and Machine Learning </h2>
<img src=images/image.X8Q2C0.png />
<img src=images/image.0IJ5C0.png />
<div class="fragment fade-in"><p class="pl">A lot of progress in machine learning can be attributed to better hardware and
more data</p></div>
</section>
<section>
<h2>Connectionism and Neural Networks</h2><ul>
<img src="images/connectionnism.png"/>
<li/>At the heart of deep learning, there is an <b>artificial neural network</b></li>
<li/>Artificial neural networks are a subset of machine learning approaches using networks of simple (neuron-like) units.</li>
</ul>
</section>
<section data-markdown><textarea data-template>
## Machine Learning / Artificial Intelligence
<img src="images/venn_ml.png" class=stretch />
<blockquote>Deep learning is a kind of representation learning, which is in turn a kind of machine learning, which is used for many but not all approaches to AI" </blockquote>
<p class='ref'>(Goodfellow et al. 2016)</p>
</textarea>
</section>
<section data-markdown><textarea data-template>
## How does deep learning differ from other AI systems?
<img src=images/ml_flowchart.png class="stretch"/>
<p class='ref'>(Goodfellow et al. 2016)</p>
</textarea>
</section>
<section data-markdown><textarea data-template>
## Learning from Examples
- Machine learning typically uses (large) ***datasets*** to learn to
- Recognize patterns (Classification)
- Generate patterns (Generation)
- Take Actions (Reinforcement Learning)
</textarea>
</section>
<section data-markdown><textarea data-template>
## Visual Recognition
- Visual Recognition (Image Classification) is the most common task performed by neural networks
- Neural networks are trained using a dataset consisting of pairs of images and labels
</textarea>
</section>
<section data-markdown><textarea data-template>
## Example Dataset: MNIST and CIFAR
<div class="row">
<div class="column">
<p style="text-align:center" >MNIST</p>
<img src=images/mnist.png />
</div>
<div class="column">
<p style="text-align:center" >CIFAR10</p>
<img src=images/CIFAR-10.png />
</div>
</div>
- MNIST/CIFAR10 are considered as the "Hello, World" example of deep learning
</textarea>
</section>
<section data-markdown><textarea data-template>
## Example Dataset: ImageNet
- ImageNet: 1M images, 1000 classes, 469x387 pixels
<img src=images/ilsvrc.png class=small />
[](http://www.image-net.org/)
</textarea></section>
<section data-markdown><textarea data-template>
## Example Dataset: CelebA
- CelebA: large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations.
</textarea>
</section>
<section data-markdown><textarea data-template>
## Beyond Image Classification
Machine Learning and neural networks can be applied to a wide variety of problems, such as:
- Image Segmentation and Object Detection
- Text or Audio Classification
- Regression
- Translation
- Anomaly Detection
- Generation (Density Estimation)
</textarea></section>
<section data-markdown><textarea data-template>
## Other (Non-Visual) Datasets:
- Speech Commands: 65,000 one-second long utterances of 30 short words, such as "Yes", "No", "Right", "Stop"
- Penn Tree Bank (PTB) dataset: A text corpus that is parsed and annotated for natural language processing research <!-- .element: class="fragment" -->
- SMS Spam Collection Dataset: 5574 messages, tagged according being ham (legitimate) or spam <!-- .element: class="fragment" -->
</textarea>
</section>
<section data-markdown><textarea data-template>
## Convolutional Neural Networks: Image Classification
<img src="images/image_classification_imagenet.png" class="small"/>
<img src="images/lenet.png" class=stretch />
</textarea></section>
<section data-markdown><textarea data-template>
## YOLO: Object Detection
<img src="images/yolo.png" class="stretch"/>
</textarea></section>
<section data-markdown><textarea data-template>
## U-Nets: Image Segmentation
<img src="images/u-net-segmentation.png" class="small"/>
<img src="images/u-net-architecture.png" class="stretch" />
</textarea></section>
<section data-markdown><textarea data-template>
## Neural Style Transfer
<img src=images/art_style_transfer.png class=stretch />
</textarea></section>
<section data-markdown><textarea data-template>
## Neural Style Transfer: Deep Empathy
<img src=images/deep_empathy_style_transfer.png class="stretch" />
</textarea></section>
<section data-markdown><textarea data-template>
## Image Generation Using Generative Adversarial Networks (GAN)
<img src=images/image_generation_gan.png class=stretch />
</textarea></section>
<section data-markdown><textarea data-template>
## Visual Attention: Captioning
<img src=images/captioning.png class=stretch />
</textarea></section>
<section data-markdown><textarea data-template>
## Deep Reinforcement Learning: Game of Go
<img src=images/game_playing.png class=stretch />
<p class=ref>Silver, et al. 2016</p>
</textarea></section>
<section data-markdown><textarea data-template>
## Sequence Prediction
- Data can have a temporal structure
- Neural networks can be applied to learn and predict sequences
- Recurrent Neural Networks are one such example
<img src=images/sequence_prediction.png />
<p class=ref>C. Olah, 2015</p>
</textarea></section>
<section data-markdown><textarea data-template>
## Robot Control: Solving Rubik's Cube with a Robot Hand
<div class=row>
<div class=column>
<img src="images/openai_rubiks_arch.png" style="height:600px;max-height:600px" />
</div>
<div class=column>
<iframe src="https://www.youtube.com/embed/kVmp0uGtShk" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen style='autoplay; max-height:500px;height:300px;width:100%;translate: scale(.1)'></iframe>
<p class=ref>OpenAI, 2019</p>
</div>
</div>
</textarea></section>
<section data-markdown><textarea data-template>
## WaveNets: Voice and Music Generation
<img src=images/wavenet.png class=stretch />
- Parametric Text-To-Speech <audio controls="" src="https://storage.googleapis.com/deepmind-media/research/WaveNet/US%20English/parametric-1.wav"></audio>
- WaveNet <audio controls="" src="https://storage.googleapis.com/deepmind-media/research/WaveNet/US%20English/wavenet-1.wav"></audio>
<p class=ref>Van den Oord et al. 2016</p>
</textarea></section>
<section data-markdown><textarea data-template>
## Attention Networks: Machine Translation
<img src=images/translation.png />
<img src="images/attention_bahdenau.png" class=stretch />
<p class=ref>Bahdenau, et al. 2015</p>
</textarea></section>
<section data-markdown><textarea data-template>
## ML/NN as Models for Understanding the Brain
<img src="images/comparing_ann_and_brain.png" class=stretch />
<p class=ref>Blake, et al. 2019</p>
- ML/NN attempts to solve tasks that are similar to that of animals.
- Researchers use ML/NN to make hypotheses in the brain.
</textarea></section>
<section data-markdown><textarea data-template>
## The Explosion of Deep Learning
- Deep Learning has become the de facto solution for any representation learning problem
- Neural Information Processing Systems (NeurIPS) is the most prestigious conference in ML/AI
- 13'000 Participant in 2019
- In 2018, the main conference sold out after 12 minutes
<div class="row">
<div class="column">
<img src=images/neurips_stats.png />
</div>
<div class="column">
<img src=images/neurips2019_photo.JPG />
</div>
</div>
</textarea>
</section>
<section data-markdown><textarea data-template>
## Books
<ul>
<li /> Machine Learning
<ul>
<li/> Ian Goodfellow, Yoshua Bengio, and Aaron Courville. Deep learning. MIT press, 2016.
<li/> C.M. Bishop. Pattern recognition and machine learning. Springer-Verlag New York, Inc. Secaucus, NJ, USA, 2006.
</ul>
<li class=fragment /> Biologically-Inspired Neural Networks
<ul>
<li/> Wulfram Gerstner, Werner M Kistler, Richard Naud, and Liam Paninski. Neuronal dynamics: From single neurons to networks and models of cognition. Cambridge University Press, 4.
<li/> E. O. Neftci, H. Mostafa, and F. Zenke. “Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-Based Optimization to Spiking Neural Networks”. IEEE Signal Processing Magazine 36.6 (Nov. 2019), pp. 51–63.
</ul>
</textarea>
</section>
<section data-markdown><textarea data-template>
## Course Overview (Weeks 1-3)
- Historical perspective and Course logistics (Today)
- Pattern Recognition Basics (1 Week) <!-- .element: class="fragment" -->
- Linear Regression, Classification (k-Nearest Neighbor, Perceptrons, Multilayer Perceptrons)
- Machine Learning and Deep Learning (2 Weeks) <!-- .element: class="fragment" -->
- Software and computer setup
- Deep Neural Networks
- Loss functions
- Optimization, Gradient Backpropagation
</textarea>
</section>
<section data-markdown><textarea data-template>
## Course Overview (Weeks 5-10)
- Applications (4 weeks)
- Visual Recognition: ConvNets
- Pattern Generation: Autoencoders, Variational Autoencoders and Generative Adversarial Networks
- Sequence Learning: Recurrent Neural Networks, LSTM, WaveNet
- Natural Language Processing: Embeddings, Transformer Networks
- From Artificial Neural Networks to Biological Neural Nets: (1 week) <!-- .element: class="fragment" -->
- Spiking Neural Networks
- Surrogate Gradient Learning
- Final Projects Presentation (Last week) <!-- .element: class="fragment" -->
</textarea>
</section>
<section data-markdown><textarea data-template>
## Software and Assignments
This is a hands-on class comprising programming assignments. All programming will be browser-based (no installation necessary):
- Programming Language: Python 3
[](https://scipy-lectures.org/intro/)
- Expected basic scientific programming skills in Python, and familiarity with the concepts in sections 1.1 through 1.4 in the [scipy lecture notes](http://www.scipy-lectures.org/intro/index.html).
- Programming Environment: Jupyter + Google Colaboratory
[](https://colab.research.google.com/notebooks/welcome.ipynb)
- Deep Learning Environment: PyTorch
[](http://pytorch.org)
</textarea>
</section>
<section data-markdown><textarea data-template>
## Final Project
- Topic in ML/NN:
- Apply a neural network to a new dataset, or
- Modify an algorithm on an existing dataset
- Week 10: 10+5 min presentation
- Finals Week: Project report, using NeurIPS conference paper template https://nips.cc/Conferences/2015/PaperInformation/StyleFiles
- Discuss topic with instructor before week 6
- Groups of two OK, but delineate contributions
</textarea>
</section>
<section data-markdown><textarea data-template>
## Final Project: Example Projects
- Visual / Sequence Recognition
- Terrain Classification from Videos
- Agent Localization using Video
- Computational Neuroscience
- Representation Similarity Analysis with Binary Neural Networks
- Classification/Regression on neuroimaging data
- Artifact detection using EEG
- Representation Similarity Analysis using Convnets and behavioral and neuroimaging data
<p class="pl">If possible, choose a topic that relates to your expertise and research</p>
</textarea>
</section>
</section>
</div>
</div>
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