Computer Vision has lots of applications including medical imaging, autonomous
vehicles, industrial inspection and augmented reality. Use of Deep Learning for
computer Vision can be categorized into multiple categories for both images and
videos – Classification, detection, segmentation & generation.
Having worked in Deep Learning with a focus on Computer Vision have come
across various challenges and learned best practices over a period
experimenting with cutting edge ideas. This workshop is for Data Scientists &
Computer Vision Engineers whose focus is deep learning. We will cover state of
the art architectures for Image Classification, Segmentation and practical tips &
tricks to train a deep neural network models. It will be hands on session where
every concepts will be introduced through python code and our choice of deep
learning framework will be PyTorch v1.0 and Keras.

Given we have only 8 hours, we will cover the most important fundamentals,
current techniques and avoid anything which is obsolete or not being used by
state-of-art algorithms. We will directly start with building the intuition for
Convolutional Neural Networks, and focus on core architectural problems. We
will try and answer some of the hard questions like how many layers must be
there in a network, how many kernels should we add. We will look at the
architectural journey of some of the best papers and discover what each brought
into the field of Vision AI, making today’s best networks possible. We will cover 9
different kinds of Convolutions which will cover a spectrum of problems like
running DNNs on constrained hardware, super-resolution, image segmentation,
etc. The concepts would be good enough for all of us to move to harder problems
like segmentation or super-resolution later, but we will focus on object
recognition, followed by object detections. We will build our networks step by
step, learning how optimizations techniques actually improve our networks and
exactly when should we introduce them. We hope the leave you in confidence
which will help you read research papers like your second nature. Given we have
8 hours, and we want the sessions to be productive, we will instead of introducing

all the problems and solutions, focus on the fundamentals of modern deep neural
networks.

 
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Outline/Structure of the Workshop

Outline/Structure of the workshop-

  1. Background and Basics – Machine Learning Intuition, Background and Basics of Convolutional Neural Networks. Receptive Fields makes the grounding framework for modern DNNs, and we will start with RFs.
  2. Neural Architecture – Exhaustive insights in to Neural Architectures covering layers, kernels, maxpooling and troubles we face due to multiple channels.
  3. First Neural Networks – We then let you build your first neural network to solve a specific problem. By this time you have learned how many layers your network needs, and how do we reduce them. But you will get stuck at this point, and we will explain the cause and the solutions. There is nothing better than hands on experimentation.
  4. Real DNN Architecture – We then take you through a journey of 9 neural networks, starting from scratch and slowly introducing concepts like Channel Reduction (Point wise Convolutions), Resolution Reduction (MaxPooling), Batch Normalization, SoftMax, Concept of Transition Layers, DropOuts, Batch Size, Overfitting, and things to remember “not to do” just before the prediction layers. We also learn and implement Learning Rate Schedulers
  5. Advanced Convolutions and Data Augmentation – we will then introduce advanced convolution to solve some specific problems as well as data augmentation to improve our networks
  6. Object Detection Networks – We will then cover Yolo V2 and look at some of the advanced concepts it introduced to the world of DNN. Some of these concepts have made things like Dense Layers obsolete and Skip-connection the “must-have” member for new architectures.
  7. Finally we introduce the state-of-art network which will combine all the basics we have learned, and we’d realise that nearly always, the solution is always simple and hence beautiful.

Learning Outcome

  1. Start with Computer Vision and Deep Learning also cover traditional computer vision techniques
  2. Learn the fundamental building blocks of neural network programming
  3. Learn PyTorchv1.0 deep learning framework
  4. CNN architecture and tips and tricks to train deep neural network models
  5. Learn to design experiments to train deep learning models
  6. Learn Semantic Segmentation state of the art algorithm
  7. Projects – Medical Datasets

Target Audience

This workshop is designed for Computer Vision Engineers and all beginners to advanced data scientist and ml/dl engineers who wants to get deeper into Deep Learning

Prerequisites for Attendees

  1. Working knowledge of python programming, numpy and matplotlib.
  2. A laptop with python installed and browser.
schedule Submitted 3 months ago

Public Feedback

comment Suggest improvements to the Speaker
  • Dr. Vikas Agrawal
    By Dr. Vikas Agrawal  ~  2 months ago
    reply Reply

    Dear Saurabh and Usha: Your proposal is interesting with a lot of material to be covered. Is there a way to cover a few topics in depth so that the audience comes out with a deeper hands-on understanding of the topic?

     

    Warm Regards

    Vikas

    • Saurabh Jha
      By Saurabh Jha  ~  2 months ago
      reply Reply

      Dear Vikas,

      Yes the idea is to discuss deeper ideas around image segmentation with a hands on approach using Pytorchv1.0. 

      We will cover below topics in detail-

      1. Image processing using openCV

      2. Creating data loaders in pytorchv1.0 for Image Segmentation 

      3. Hands implementation of CNN architecture (Resnet preferably), FCN in pytorchv1.0 and in depth practical discussion around auto encoders and Conv Autoencoders. 

      4. Discuss and implement U-Net on standard dataset. 

      5. Discuss practical know how to write a training loop - callbacks, one cycle policy, practical tricks and tips to efficiently train deep neural networks in pytorch. 

      6. Discuss state of the art architectures in semantic segmentation. 

      Hope this answers your question, in case you need additional detail information will be happy to provide here. 

       

      Regards

      Saurabh Jha 

       

  • Prajeesh Nair
    By Prajeesh Nair  ~  3 months ago
    reply Reply

    Good coverage of Computer Vision

  • Nikita Salunkhe
    By Nikita Salunkhe  ~  3 months ago
    reply Reply

     Excellent Workshop,looking forward to it.


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    Intro

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    Learn good research practices like organizing code and modularizing output for productive data wrangling to improve algorithm performance.

    Knowledge Graph at Embibe

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    Most machine learning models assume independent and identically distributed (i.i.d) data. Graphical models can capture almost arbitrarily rich dependency structures between variables. They encode conditional independence structure with graphs. Bayesian network, a type of graphical model describes a probability distribution among all variables by putting edges between the variable nodes, wherein edges represent the conditional probability factor in the factorized probability distribution. Thus Bayesian Networks provide a compact representation for dealing with uncertainty using an underlying graphical structure and the probability theory. These models have a variety of applications such as medical diagnosis, biomonitoring, image processing, turbo codes, information retrieval, document classification, gene regulatory networks, etc. amongst many others. These models are interpretable as they are able to capture the causal relationships between different features .They can work efficiently with small data and also deal with missing data which gives it more power than conventional machine learning and deep learning models.

    In this session, we will discuss concepts of conditional independence, d- separation , Hammersley Clifford theorem , Bayes theorem, Expectation Maximization and Variable Elimination. There will be a code walk through of simple case study.

  • Liked Maryam Jahanshahi
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    Maryam Jahanshahi - Applying Dynamic Embeddings in Natural Language Processing to Analyze Text over Time

    Maryam Jahanshahi
    Maryam Jahanshahi
    Research Scientist
    TapRecruit
    schedule 6 months ago
    Sold Out!
    45 Mins
    Case Study
    Intermediate

    Many data scientists are familiar with word embedding models such as word2vec, which capture semantic similarity of words in a large corpus. However, word embeddings are limited in their ability to interrogate a corpus alongside other context or over time. Moreover, word embedding models either need significant amounts of data, or tuning through transfer learning of a domain-specific vocabulary that is unique to most commercial applications.

    In this talk, I will introduce exponential family embeddings. Developed by Rudolph and Blei, these methods extend the idea of word embeddings to other types of high-dimensional data. I will demonstrate how they can be used to conduct advanced topic modeling on datasets that are medium-sized, which are specialized enough to require significant modifications of a word2vec model and contain more general data types (including categorical, count, continuous). I will discuss how my team implemented a dynamic embedding model using Tensor Flow and our proprietary corpus of job descriptions. Using both categorical and natural language data associated with jobs, we charted the development of different skill sets over the last 3 years. I will specifically focus the description of results on how tech and data science skill sets have developed, grown and pollinated other types of jobs over time.

  • Liked Anant Jain
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    Anant Jain - Adversarial Attacks on Neural Networks

    Anant Jain
    Anant Jain
    Co-Founder
    Compose Labs, Inc.
    schedule 5 months ago
    Sold Out!
    20 Mins
    Talk
    Intermediate

    Since 2014, adversarial examples in Deep Neural Networks have come a long way. This talk aims to be a comprehensive introduction to adversarial attacks including various threat models (black box/white box), approaches to create adversarial examples and will include demos. The talk will dive deep into the intuition behind why adversarial examples exhibit the properties they do — in particular, transferability across models and training data, as well as high confidence of incorrect labels. Finally, we will go over various approaches to mitigate these attacks (Adversarial Training, Defensive Distillation, Gradient Masking, etc.) and discuss what seems to have worked best over the past year.