location_city Bengaluru schedule Sep 2nd 10:00 AM - 06:00 PM place Mars people 17 Interested add_circle_outline Notify

You have been hearing about machine learning (ML) and artificial intelligence (AI) everywhere. You have heard about computers recognizing images, generating speech, natural language, and beating humans at Chess and Go.

The objectives of the workshop:

  1. Learn machine learning, deep learning and AI concepts

  2. Provide hands-on training so that students can write applications in AI

  3. Provide ability to run real machine learning production examples

  4. Understand programming techniques that underlie the production software

The concepts will be taught in Julia, a modern language for numerical computing and machine learning - but they can be applied in any language the audience are familiar with.

Workshop will be structured as “reverse classroom” based laboratory exercises that have proven to be engaging and effective learning devices. Knowledgeable facilitators will help students learn the material and extrapolate to custom real world situations.

 
 

Outline/Structure of the Workshop

  • Representing Data with Models. Use of functions and parametric functions to build models.
  • Model Complexity, what is Learning from a Computational point of view. How does a Computer learn?
  • Exploring Data with Unsupervised Learning, Dimensionality reduction for Image Classification.
  • Applications using Unsupervised Machine learning
  • Introduction to Supervised Machine Learning
  • Practical Applications using Supervised Machine Learning, (Object detection etc.)
  • Introduction to Neurons, Learning with a Single Neuron
  • Introduction to Flux.jl, learning with a single neuron using Flux
  • Introduction to Neural Networks, Building single layer neural net with Flux
  • Introduction to Deep Learning, Multi-Layer Neural Network with Flux
  • Handwritten recognition with neural networks

Learning Outcome

  • Participants will walk away feeling comfortable with machine learning and the underlying algorithms.
  • Participants can consider themselves not as consumers of APIs of various ML libraries, but can become comfortable with building the underlying algorithms in Julia and be able to contribute to various ML packages and in general to Julia too!

Target Audience

Aspiring Data Scientists, experienced data scientists who are eager to get better understanding of the implementation of ML algorithms.

Prerequisites for Attendees

  1. Not to shy away from getting into some mathematical concepts

  2. Commitment to strive towards understanding the concepts and program for applications

  3. Active participation in the workshop and strive to solve exercises taking the help of support staff

  4. Commitment to follow on work or projects in order to apply the concepts in real life

schedule Submitted 2 years ago

Public Feedback


    • Favio Vázquez
      keyboard_arrow_down

      Favio Vázquez - Agile Data Science Workflows with Python, Spark and Optimus

      480 Mins
      Workshop
      Intermediate

      Cleaning, Preparing , Transforming and Exploring Data is the most time-consuming and least enjoyable data science task, but one of the most important ones. With Optimus we’ve solve this problem for small or huge datasets, also improving a whole workflow for data science, making it easier for everyone. You will learn how the combination of Apache Spark and Optimus with the Python ecosystem can form a whole framework for Agile Data Science allowing people and companies to go further, and beyond their common sense and intuition to solve complex business problems.

    • Sohan Maheshwar
      keyboard_arrow_down

      Sohan Maheshwar - It's All in the Data: The Machine Learning Behind Alexa's AI Systems

      Sohan Maheshwar
      Sohan Maheshwar
      Alexa Evangelist
      Amazon
      schedule 2 years ago
      Sold Out!
      45 Mins
      Talk
      Intermediate

      Amazon Alexa, the cloud-based voice service that powers Amazon Echo, provides access to thousands of skills that enable customers to voice control their world - whether it’s listening to music, controlling smart home devices, listening to the news or even ordering a pizza. Alexa developers use advanced natural language understanding that to use capabilities like built-in slot & intent training, entity resolution, and dialog management. This natural language understanding is powered by advanced machine learning algorithms that will be the focus of this talk.

      This session will tell you about the rise of voice user interfaces and will give an in-depth look into how Alexa works. The talk will delve into the natural language understanding and how utterance data is processed by our systems, and what a developer can do to improve accuracy of their skill. Also, the talk will discuss how Alexa hears and understands you and how error handling works.

    • Vishal Gokhale
      keyboard_arrow_down

      Vishal Gokhale - Fundamental Math for Data Science

      Vishal Gokhale
      Vishal Gokhale
      Sr. Consultant
      Xnsio
      schedule 2 years ago
      Sold Out!
      480 Mins
      Workshop
      Beginner

      By now it is evident that a solid math foundation is indispensable if one has to get into Data science in an honest-to-goodness way. Unfortunately, for many of us math was just a means to get better scores at school-level and never really a means to understand the world around us.
      That systemic failure (education system) causes many of us to feel a “gap” when learning data science concepts. It is high time that we acknowledge that gap and take remedial action.

      The purpose of the workshop is to develop an intuitive understanding of the concepts.
      We let go the fear of rigorous notation and embrace the rationale behind it.
      The intended key take away for participants is confidence to deal with math.

    • Saurabh Deshpande
      keyboard_arrow_down

      Saurabh Deshpande - Introduction to reinforcement learning using Python and OpenAI Gym

      Saurabh Deshpande
      Saurabh Deshpande
      Sr. Technical Consultant
      SAS
      schedule 2 years ago
      Sold Out!
      90 Mins
      Workshop
      Advanced

      Reinforcement Learning algorithms becoming more and more sophisticated every day which is evident from the recent win of AlphaGo and AlphaGo Zero (https://deepmind.com/blog/alphago-zero-learning-scratch/ ). OpenAI has provided toolkit openai gym for research and development of Reinforcement Learning algorithms.

      In this workshop, we will focus on introduction to the basic concepts and algorithms in Reinforcement Learning and hands on coding.

      Content

      • Introduction to Reinforcement Learning Concepts and teminologies
      • Setting up OpenAI Gym and other dependencies
      • Introducing OpenAI Gym and its APIs
      • Implementing simple algorithms using couple of OpenAI Gym Environments
      • Demo of Deep Reinforcement Learning using one of the OpenAI Gym Atari game

    • Sai Charan J
      keyboard_arrow_down

      Sai Charan J - Self Learning - Data Science

      Sai Charan J
      Sai Charan J
      Data Scientist
      MTW Labs
      schedule 2 years ago
      Sold Out!
      45 Mins
      Workshop
      Beginner

      For people from a non-technical background, I recommend formal academic programs. And then raising the bar comes data-driven scientist - Self Taught Data Scientist! These people are trendsetters, go way deep & play with data. They love data crunching & are seen solving real-time problems!

      If that's you, then let's wave our hands!

    • Harshad Saykhedkar
      keyboard_arrow_down

      Harshad Saykhedkar - Linear Algebra for Machine Learning Workshop

      240 Mins
      Workshop
      Beginner

      Linear Algebra, Optimization & Statistics is base of all machine learning. This workshop will cover required linear algebra for machine learning in a hands on way through short code examples. We will cover basic theory, interesting applications and the big picture.

    • Saurabh Deshpande
      keyboard_arrow_down

      Saurabh Deshpande - Introduction to Natural Language Processing using Python

      Saurabh Deshpande
      Saurabh Deshpande
      Sr. Technical Consultant
      SAS
      schedule 2 years ago
      Sold Out!
      90 Mins
      Workshop
      Intermediate

      Python ecosystem for Natural language processing has evolved in last decade and rich set of open source tools and data sets are now available.

      In this session, we will go over basics of Natural language processing along with sample code demonstration and hands on tutorials using following famous python libraries,

      1. NLTK : One of the oldest and famous library for natural language analysis for researchers
      2. Stanford CoreNLP : Production ready NLP library. (Written in java but has many open source python wrappers)
      3. SpaCy: Comparatively new python NLP toolkit marketed as 'Industrial Strength' python library.

      Session will introduce the various use cases and basic concepts related to the natural language processing with demo and hands on tutorials

      Following NLP fundamentals will be discussed,

      - Syntax Vs. Semantics

      - Regular Expressions (Demo and Hands on)

      - Word Embeddings (Demo and Hands on)

      - Word Tokenization (Demo and Hands on)

      - Part of Speech Tagging (Demo and Hands on)

      - Text Similarity (Demo and Hands on)

      - Text Summarization

      - Named Entity Recognition ((Demo and Hands on))

      - Sentiment Analysis (Demo and Hands on)

    • Venkatraman J
      keyboard_arrow_down

      Venkatraman J - Hands on Data Science. Get hands dirty with real code!!!

      45 Mins
      Workshop
      Intermediate

      Data science refers to the science of extracting useful information from data. Knowledge discovery in data bases, data mining, Information extraction also closely match with data science. Supervised learning,Semi supervised learning,Un supervised learning methodologies are out of Academia and penetrated deep into the industry leading to actionable insights, dashboard driven development, data driven reasoning and so on. Data science has been the buzzword for last few years in industry with only a handful of data scientists around the world. The industry needs more and more data scientists in future to solve problems using statistical techniques. The exponential availability of unstructured data from the web has thrown huge challenges to data scientists to exploit them before driving conclusions.

      Now that's overload of information and buzzwords. It all has to start somewhere? Where and how to start? How to get hands dirty rather than just reading books and blogs. Is it really science or just code?. Let's get into code to talk data science.

      In this workshop i will show the tools required to do real data science rather than just reading by building real models using Deep neural networks and show live demo of the same. Also share some of the key data science techniques every aspiring data scientist should have to thrive in the industry.

    • Bhanu Sharma
      keyboard_arrow_down

      Bhanu Sharma - From "Hello World" to production, running Machine Learning models into production using Tensorflow serving and Kubernetes.

      45 Mins
      Talk
      Intermediate

      Do you have issues running your Machine Learning models in to production or want to learn what are some of the best industry practices in regards to deployment and serving predictions for Machine Learning models.
      In a local environment the task is simple but things become complex when a model is run in production. Scaling, Robustness and reliability are few metrics that one has to take care of while doing so.

      This talk will look into answering that question by taking the audience through an example using best made industry practices.

      Tensorflow serving "is a flexible, high-performance serving system for machine learning models, designed for production environments" . It allows one to deploy newer versions of a model to production without changing either the architecture or the API.
      Configure automatic deployment of a newer model as soon as an update model is available and run it alongside the older one for a no frills transition or scrap the older model completely and run the improved one in the production. With Tensorflow Serving one can easily deploy, manage their models in production to scale and even automate their deployment pipeline.

      Tensorflow Serving Architecture

      image credits: Tensorflow Serving Architecture

      Kubernetes is used to deploy the created Serving, which will enable better scaling and robustness. How properties of a Kubernetes cluster like elasticity and resource managment can be used to serve models to a large number of users and run computationally and data intensive models in production.

      Audience will be taken through each step of the process with in depth insights on the tools used while answering the questions of What, Why and How along the process.