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How to architect Deep Learning

This document outlines various neural network architectures for solving different types of problems using deep learning. It discusses classification, regression, multimodal classification, video question answering, image question answering, and image-to-image translation problems. For each problem type, it provides examples of possible neural network architectures that could be used to map input data to output classifications or predictions. The goal is to help attendees understand how to design neural network architectures suited to their specific problems and mapping input to output.

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Design How to solve a problem using Deep Learning AI for Good Workshop July 2019 Session 3 https://sites.google.com/view/AIforEveryone
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key Learning objective for Day 1 How to solve a problem with Deep Learning ? How to design an neural network architecture?
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz #1 Is it possible to map hand signs for deaf  speech  Hand signs   Speech
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone A B •
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz #2 Is it possible to “see the depth” for visual impaired  Yes / no ?
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz #2 Is it possible to feel the depth for visual impaired
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Topics in this presentation •New way to think from today ! • How to use Deep Learning to solve any problem • How to model & architect Neural Networks
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone New way to think from today !
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone New way to think from today ! Do you have  Large dataset?  Compute resource (can pay easy to cloud)  Ability to use frameworks such as Tensorflow
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key Learning objective for Day 1 How to design an neural network architecture?
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key Learning objective for Day 1How to design an neural network architecture? How to map x to y? What architecture can connect x to y?
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key Learning objective for Day 1How to design an neural network architecture? Predicted class How to map x to y? What architecture can map x to y?
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Deep Learning idea-a-thon: How to solve a problem by designing novel neural network architectures? CNN RNN Fully Connected Merge CNN is suitable architecture to extract features out of any data that has spatial relationships. For example, An photo image spatial info..or info spread across space In general, a recurrent neural network could be considered as the best neural network model for extracting features out of temporal data. A temporal data is basically a data that varies over time such as ECG, music, speech, sentences, words. LSTM is a advanced RNN To classify / predict To combine the learnings of two neural networks. (to combine the power of 2 people’s brains) Attention To learn to focus on the most important aspects to achieve a particular task Generative Deep Learning To model creative tasks as to compose a music or to come up a painting
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone How to use Deep learning to learn to connect A and B
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Learning objective: How to learn to map A  B Deep Learning A B Rules or math function to find B given A. B = f(A) What should be f()?
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone How to translate A to B
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Learn to map Xy • Examples of x, y
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Learn to map Xy
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Learn to map A  B
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Fun activity
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Learning objective: How do solve a problem using Deep Learning? Case study: self driving car
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key Learning objective for Day 1 How to solve a problem with Deep Learning ? How to design an neural network architecture?
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #1 classification problems CNN Fully Connected Prediction of what of objects is in this photo
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #1 CNN Fully Connected Turn car left Turn car right Don’t Turn the car
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Learn to map x->y
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #1
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #1
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Camera view ? steering angle • Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car Credits: Nvidia
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz what the neural network learns depends upon its purpose • How to set the purpose of a “learning machine”?
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone •
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone What is the function to map x->y •
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #2 Regression problems CNN Fully Connected Continuous number
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #2 Regression problems CNN Fully Connected Turn the car by 10 degrees
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #2 Regression problems
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #2 Regression problems •
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #3 multimodal classification CNN Fully Connected Predicted class RNN Merge
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #3 multimodal classification
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #3 multimodal classification
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz Neural Network Design Pattern #3 multimodal classification • Quiz: What was x and y in the problem?
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz Neural Network Design Pattern #3 • Quiz: What was x and y in the problem?
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz multimodal classification
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Answering Visual Questions from Blind People
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #3 multimodal classification
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #3 multimodal classification •
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #3
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz/Thinking activity how to classify a genomic sequence healthcare/genomic sequence classification
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone How to Solve this problem with deep learning: how to classify a genomic sequence ? ? ? Credits: DeepMotif (ICLR’16) http://www.cs.virginia.edu/yanjun/paperA14/2017_demo_slides.pdf This is a classification problem. A: Sequence of characters B: Category
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone How to Solve this problem with deep learning: how to classify a genomic sequence ? ? ?
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone how to classify a genomic sequence
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #4 video question answering CNN Fully Connected Predicted class RNN Merge Video Question RNN
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #4 video question answering
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #4 video question answering •
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #4 video question answering
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #4 video question answering
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #4 video question answering
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #4 video question answering
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #4 video question answering
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #5 image question answering CNN Answer (text) RNN Merge Image (photo) Question (text) RNN
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #5 image question answering
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #6
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #6
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #7 image to image translation CNN Image (text) Image (photo) CNN Features of the image
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #7 image to image translation
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #7 image to image translation
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #7 image to image translation
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #7 image to image translation
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz Can a neural network output 2 types of output? • How to architect Multi output network?
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz Multi output network
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz how to extract features from multiple sources ? • I have a input of audio and video and text. How to combine the features from all 3?
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz how to extract features from multiple sources ?
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Our inspiration / acknowledgements Friendly approaches : 1) KERAS.io François Chollet’s Book on “Deep Learning with Python” 2) Deeplearning.ai (Coursera.org) Andrew Ng 3) Udacity Deep Learning Free course 4) Google Machine Learning Course https://developers.google.com/machine-learning/crash-course/
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone More inspiration/ acknowledgements Excellent Resources • Stanford cs231 n http://cs231n.stanford.edu • MIT Deep Learning http://introtodeeplearning.com/ https://deeplearning.mit.edu • IIT Madras my classes notes with Prof. Anurag (Deep Learning)
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone What did you learn? (Day 1) How to solve a problem with Deep Learning ? How to design an neural network architecture?
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key ideas to remember You can map any thing to anything with Deep Learning AB • Anything  Machine Learns to map  Anything
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key ideas to remember How to design a architectures to handle various types of A  B How to design this architecture? CNN / RNN ? Fully Connected or RNN ? Predicted class RNN / Fully connected ? Merge / Attention ? Video Question A  --------------------------------------------------------------------------------------------------------------------------  B
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key ideas to remember From architecture to code ? (just learn early glimpse of how code looks like.) Just 1 page of code is enough for a complex problems such as video question answering
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key ideas to remember If you didn’t understand the code*, just don’t worry. • Why not to worry? • Solving the video question answering problem was not possible until 2017. • To solve this problem, it required dozens of experts in Google lab to dedicate 6 months of effort! *code = code in the previous slide
Acknowledgments & Credits are mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Team activity idea-a-ton Goal of this thinking activity How to solve a problem with Deep Learning ? How to design an neural network architecture?

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How to architect Deep Learning

  • 1.
    Design How to solvea problem using Deep Learning AI for Good Workshop July 2019 Session 3 https://sites.google.com/view/AIforEveryone
  • 2.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key Learning objective for Day 1 How to solve a problem with Deep Learning ? How to design an neural network architecture?
  • 3.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz #1 Is it possible to map hand signs for deaf  speech  Hand signs   Speech
  • 4.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone A B •
  • 5.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz #2 Is it possible to “see the depth” for visual impaired  Yes / no ?
  • 6.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz #2 Is it possible to feel the depth for visual impaired
  • 7.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone
  • 8.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Topics in this presentation •New way to think from today ! • How to use Deep Learning to solve any problem • How to model & architect Neural Networks
  • 9.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone New way to think from today !
  • 10.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone New way to think from today ! Do you have  Large dataset?  Compute resource (can pay easy to cloud)  Ability to use frameworks such as Tensorflow
  • 11.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key Learning objective for Day 1 How to design an neural network architecture?
  • 12.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key Learning objective for Day 1How to design an neural network architecture? How to map x to y? What architecture can connect x to y?
  • 13.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key Learning objective for Day 1How to design an neural network architecture? Predicted class How to map x to y? What architecture can map x to y?
  • 14.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Deep Learning idea-a-thon: How to solve a problem by designing novel neural network architectures? CNN RNN Fully Connected Merge CNN is suitable architecture to extract features out of any data that has spatial relationships. For example, An photo image spatial info..or info spread across space In general, a recurrent neural network could be considered as the best neural network model for extracting features out of temporal data. A temporal data is basically a data that varies over time such as ECG, music, speech, sentences, words. LSTM is a advanced RNN To classify / predict To combine the learnings of two neural networks. (to combine the power of 2 people’s brains) Attention To learn to focus on the most important aspects to achieve a particular task Generative Deep Learning To model creative tasks as to compose a music or to come up a painting
  • 15.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone How to use Deep learning to learn to connect A and B
  • 16.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Learning objective: How to learn to map A  B Deep Learning A B Rules or math function to find B given A. B = f(A) What should be f()?
  • 17.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone How to translate A to B
  • 18.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone
  • 19.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Learn to map Xy • Examples of x, y
  • 20.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Learn to map Xy
  • 21.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Learn to map A  B
  • 22.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone
  • 23.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Fun activity
  • 24.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Learning objective: How do solve a problem using Deep Learning? Case study: self driving car
  • 25.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key Learning objective for Day 1 How to solve a problem with Deep Learning ? How to design an neural network architecture?
  • 26.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #1 classification problems CNN Fully Connected Prediction of what of objects is in this photo
  • 27.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #1 CNN Fully Connected Turn car left Turn car right Don’t Turn the car
  • 28.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Learn to map x->y
  • 29.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #1
  • 30.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #1
  • 31.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Camera view ? steering angle • Explaining How a Deep Neural Network Trained with End-to-End Learning Steers a Car Credits: Nvidia
  • 32.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz what the neural network learns depends upon its purpose • How to set the purpose of a “learning machine”?
  • 33.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone •
  • 34.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone What is the function to map x->y •
  • 35.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #2 Regression problems CNN Fully Connected Continuous number
  • 36.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #2 Regression problems CNN Fully Connected Turn the car by 10 degrees
  • 37.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #2 Regression problems
  • 38.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #2 Regression problems •
  • 39.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #3 multimodal classification CNN Fully Connected Predicted class RNN Merge
  • 40.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #3 multimodal classification
  • 41.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #3 multimodal classification
  • 42.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz Neural Network Design Pattern #3 multimodal classification • Quiz: What was x and y in the problem?
  • 43.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz Neural Network Design Pattern #3 • Quiz: What was x and y in the problem?
  • 44.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz multimodal classification
  • 45.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Answering Visual Questions from Blind People
  • 46.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #3 multimodal classification
  • 47.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #3 multimodal classification •
  • 48.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #3
  • 49.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz/Thinking activity how to classify a genomic sequence healthcare/genomic sequence classification
  • 50.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone How to Solve this problem with deep learning: how to classify a genomic sequence ? ? ? Credits: DeepMotif (ICLR’16) http://www.cs.virginia.edu/yanjun/paperA14/2017_demo_slides.pdf This is a classification problem. A: Sequence of characters B: Category
  • 51.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone How to Solve this problem with deep learning: how to classify a genomic sequence ? ? ?
  • 52.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone how to classify a genomic sequence
  • 53.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #4 video question answering CNN Fully Connected Predicted class RNN Merge Video Question RNN
  • 54.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #4 video question answering
  • 55.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #4 video question answering •
  • 56.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #4 video question answering
  • 57.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #4 video question answering
  • 58.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #4 video question answering
  • 59.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #4 video question answering
  • 60.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #4 video question answering
  • 61.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #5 image question answering CNN Answer (text) RNN Merge Image (photo) Question (text) RNN
  • 62.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #5 image question answering
  • 63.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #6
  • 64.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #6
  • 65.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Architecture patterns Neural Network Design Pattern #7 image to image translation CNN Image (text) Image (photo) CNN Features of the image
  • 66.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #7 image to image translation
  • 67.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #7 image to image translation
  • 68.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #7 image to image translation
  • 69.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Neural Network Design Pattern #7 image to image translation
  • 70.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz Can a neural network output 2 types of output? • How to architect Multi output network?
  • 71.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz Multi output network
  • 72.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz how to extract features from multiple sources ? • I have a input of audio and video and text. How to combine the features from all 3?
  • 73.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Quiz how to extract features from multiple sources ?
  • 74.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Our inspiration / acknowledgements Friendly approaches : 1) KERAS.io François Chollet’s Book on “Deep Learning with Python” 2) Deeplearning.ai (Coursera.org) Andrew Ng 3) Udacity Deep Learning Free course 4) Google Machine Learning Course https://developers.google.com/machine-learning/crash-course/
  • 75.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone More inspiration/ acknowledgements Excellent Resources • Stanford cs231 n http://cs231n.stanford.edu • MIT Deep Learning http://introtodeeplearning.com/ https://deeplearning.mit.edu • IIT Madras my classes notes with Prof. Anurag (Deep Learning)
  • 76.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone What did you learn? (Day 1) How to solve a problem with Deep Learning ? How to design an neural network architecture?
  • 77.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key ideas to remember You can map any thing to anything with Deep Learning AB • Anything  Machine Learns to map  Anything
  • 78.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key ideas to remember How to design a architectures to handle various types of A  B How to design this architecture? CNN / RNN ? Fully Connected or RNN ? Predicted class RNN / Fully connected ? Merge / Attention ? Video Question A  --------------------------------------------------------------------------------------------------------------------------  B
  • 79.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key ideas to remember From architecture to code ? (just learn early glimpse of how code looks like.) Just 1 page of code is enough for a complex problems such as video question answering
  • 80.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Key ideas to remember If you didn’t understand the code*, just don’t worry. • Why not to worry? • Solving the video question answering problem was not possible until 2017. • To solve this problem, it required dozens of experts in Google lab to dedicate 6 months of effort! *code = code in the previous slide
  • 81.
    Acknowledgments & Creditsare mentioned to inspirational resources presented in the end of presentation. For more like this, https://sites.google.com/view/AIforEveryone Team activity idea-a-ton Goal of this thinking activity How to solve a problem with Deep Learning ? How to design an neural network architecture?