location_city Bengaluru schedule Aug 8th 01:45 - 02:30 PM IST place Grand Ball Room 2 people 253 Interested

The field of Artificial Intelligence powered by Machine Learning and Deep Learning has gone through some phenomenal changes over the last decade. Starting off as just a pure academic and research-oriented domain, we have seen widespread industry adoption across diverse domains including retail, technology, healthcare, science and many more. More than often, the standard toolbox of machine learning, statistical or deep learning models remain the same. New models do come into existence like Capsule Networks, but industry adoption of the same usually takes several years. Hence, in the industry, the main focus of data science or machine learning is more ‘applied’ rather than theoretical and effective application of these models on the right data to solve complex real-world problems is of paramount importance.

A machine learning or deep learning model by itself consists of an algorithm which tries to learn latent patterns and relationships from data without hard-coding fixed rules. Hence, explaining how a model works to the business always poses its own set of challenges. There are some domains in the industry especially in the world of finance like insurance or banking where data scientists often end up having to use more traditional machine learning models (linear or tree-based). The reason being that model interpretability is very important for the business to explain each and every decision being taken by the model.However, this often leads to a sacrifice in performance. This is where complex models like ensembles and neural networks typically give us better and more accurate performance (since true relationships are rarely linear in nature).We, however, end up being unable to have proper interpretations for model decisions.

To address and talk about these gaps, I will take a conceptual yet hands-on approach where we will explore some of these challenges in-depth about explainable artificial intelligence (XAI) and human interpretable machine learning and even showcase with some examples using state-of-the-art model interpretation frameworks in Python!

 
 

Outline/Structure of the Tutorial

The focus of this session is to demystify the hype behind the term 'Explainable AI' and talk about tangible concepts which can be leveraged using state-of-the-art tools and techniques to build human-interpretable models. We will be giving a conceptual overview of what Explainable AI or XAI entails followed by major strategies around XAI techniques. Once the audience gets some foundational knowledge around XAI, we will showcase some case-studies using hands-on examples in Python to build machine learning and deep learning models and leverage model interpretation and explanation strategies. Overall the talk will be structured as follows.

Part 1: The Importance of Human Interpretable Machine Learning

  • Understanding Machine Learning Model Interpretation
  • Importance of Machine Learning Model Interpretation
  • Criteria for Model Interpretation Methods
  • Scope of Model Interpretation

Part 2: Model Interpretation Strategies

  • Traditional Techniques for Model Interpretation
  • Challenges and Limitations of Traditional Techniques
  • The Accuracy vs. Interpretability trade-off
  • Model Interpretation Techniques

Part 3: Hands-on Model Interpretation — A comprehensive Guide

  • Hands-on guides on using the latest state-of-the-art model interpretation frameworks
  • Features, concepts and examples of using frameworks like ELI5, Skater and SHAP
  • Explore concepts and see them in action — Feature importances, partial dependence plots, surrogate models, interpretation and explanations with LIME, SHAP values
  • Hands-on Machine Learning Model Interpretation on a supervised learning example

Part 4: Hands-on Advanced Model Interpretation

  • Hands-on Model Interpretation on Unstructured Datasets
  • Advanced Model Interpretation on Deep Learning Models

Learning Outcome

Key Takeaways from this talk\tutorial

- Understand what is Explainable Artificial Intelligence

- Learn the latest and best techniques for building interpretable models and unbox the opacity of complex black-box models

- Learn how to leverage state-of-the-art model interpretation frameworks in Python

- Understand how to interpret models on both structured and unstructured data

Target Audience

Data Scientists, Engineers, Managers, AI Enthusiasts

Prerequisites for Attendees

Participants are expected to know what is AI, Machine Learning and Deep Learning. Some basics around the Data Science lifecycle including data, features, modeling and evaluation.

Examples will be shown in Python so having a basic knowledge of Python helps.

Slides


Video


schedule Submitted 4 years ago

  • Dat Tran
    keyboard_arrow_down

    Dat Tran - Image ATM - Image Classification for Everyone

    Dat Tran
    Dat Tran
    Head of AI
    Axel Springer AI
    schedule 4 years ago
    Sold Out!
    45 Mins
    Talk
    Intermediate

    At idealo.de we store and display millions of images. Our gallery contains pictures of all sorts. You’ll find there vacuum cleaners, bike helmets as well as hotel rooms. Working with huge volume of images brings some challenges: How to organize the galleries? What exactly is in there? Do we actually need all of it?

    To tackle these problems you first need to label all the pictures. In 2018 our Data Science team completed four projects in the area of image classification. In 2019 there were many more to come. Therefore, we decided to automate this process by creating a software we called Image ATM (Automated Tagging Machine). With the help of transfer learning, Image ATM enables the user to train a Deep Learning model without knowledge or experience in the area of Machine Learning. All you need is data and spare couple of minutes!

    In this talk we will discuss the state-of-art technologies available for image classification and present Image ATM in the context of these technologies. We will then give a crash course of our product where we will guide you through different ways of using it - in shell, on Jupyter Notebook and on the Cloud. We will also talk about our roadmap for Image ATM.

  • Dr. Vikas Agrawal
    keyboard_arrow_down

    Dr. Vikas Agrawal - Non-Stationary Time Series: Finding Relationships Between Changing Processes for Enterprise Prescriptive Systems

    45 Mins
    Talk
    Intermediate

    It is too tedious to keep on asking questions, seek explanations or set thresholds for trends or anomalies. Why not find problems before they happen, find explanations for the glitches and suggest shortest paths to fixing them? Businesses are always changing along with their competitive environment and processes. No static model can handle that. Using dynamic models that find time-delayed interactions between multiple time series, we need to make proactive forecasts of anomalous trends of risks and opportunities in operations, sales, revenue and personnel, based on multiple factors influencing each other over time. We need to know how to set what is “normal” and determine when the business processes from six months ago do not apply any more, or only applies to 35% of the cases today, while explaining the causes of risk and sources of opportunity, their relative directions and magnitude, in the context of the decision-making and transactional applications, using state-of-the-art techniques.

    Real world processes and businesses keeps changing, with one moving part changing another over time. Can we capture these changing relationships? Can we use multiple variables to find risks on key interesting ones? We will take a fun journey culminating in the most recent developments in the field. What methods work well and which break? What can we use in practice?

    For instance, we can show a CEO that they would miss their revenue target by over 6% for the quarter, and tell us why i.e. in what ways has their business changed over the last year. Then we provide the prioritized ordered lists of quickest, cheapest and least risky paths to help turn them over the tide, with estimates of relative costs and expected probability of success.

  • Avishkar Gupta
    keyboard_arrow_down

    Avishkar Gupta / Dipanjan Sarkar - Leveraging AI to Enhance Developer Productivity & Confidence

    45 Mins
    Tutorial
    Intermediate

    A major approach to the application of AI is leveraging it to create a safer world around us, as well as that of helping people make choices. With the open source revolution having taken the world by a storm and developers relying on various upstream third party dependencies (too many to chose from!:http://www.modulecounts.com/) to develop applications moving petabytes of sensitive data and mission critical code that can lead to disastrous failures, it is required now more than ever to build better developer tooling to help developers make safer, better choices in terms of their dependencies as well as providing them with more insights around the code they are using. Thanks to deep learning, we are able to tackle these complex problems and this talk would be covering two diverse and interesting problems we have been trying to solve leveraging deep learning models (recommenders and NLP).

    Though we are data scientists, at heart we are also developers building intelligent systems powered by AI. We, the Redhat developer group through our “Dependency Analytics” platform and extension, seek to do the same. We call this, 'AI-based insights for developers by developers'!

    In this session we would be going into the details of the deep learning models we have implemented and deployed to solve two major problems:

    1. Dependency Recommendations: Recommend dependencies to a user for their specific application stack by trying to guess their intent by leveraging deep learning based recommender models.
    2. Pro-active Security and Vulnerability Analysis: We would also touch upon how our platform aims to make developer applications safer by way of CVE (Common Vulnerabilities and Exposures) analyses and the experimental deep learning models we have built to proactively identify potential vulnerabilities. We will talk about how we leveraged deep learning models for NLP to tackle this problem.

    This shall be followed by a short architectural overview of the entire platform.

    If we have enough time, we intend to showcase some sample code as a part of a tutorial of how we built these deep learning models and do a walkthrough of the same!

  • Subhasish Misra
    keyboard_arrow_down

    Subhasish Misra - Causal data science: Answering the crucial ‘why’ in your analysis.

    45 Mins
    Talk
    Intermediate

    Causal questions are ubiquitous in data science. For e.g. questions such as, did changing a feature in a website lead to more traffic or if digital ad exposure led to incremental purchase are deeply rooted in causality.

    Randomized tests are considered to be the gold standard when it comes to getting to causal effects. However, experiments in many cases are unfeasible or unethical. In such cases one has to rely on observational (non-experimental) data to derive causal insights. The crucial difference between randomized experiments and observational data is that in the former, test subjects (e.g. customers) are randomly assigned a treatment (e.g. digital advertisement exposure). This helps curb the possibility that user response (e.g. clicking on a link in the ad and purchasing the product) across the two groups of treated and non-treated subjects is different owing to pre-existing differences in user characteristic (e.g. demographics, geo-location etc.). In essence, we can then attribute divergences observed post-treatment in key outcomes (e.g. purchase rate), as the causal impact of the treatment.

    This treatment assignment mechanism that makes causal attribution possible via randomization is absent though when using observational data. Thankfully, there are scientific (statistical and beyond) techniques available to ensure that we are able to circumvent this shortcoming and get to causal reads.

    The aim of this talk, will be to offer a practical overview of the above aspects of causal inference -which in turn as a discipline lies at the fascinating confluence of statistics, philosophy, computer science, psychology, economics, and medicine, among others. Topics include:

    • The fundamental tenets of causality and measuring causal effects.
    • Challenges involved in measuring causal effects in real world situations.
    • Distinguishing between randomized and observational approaches to measuring the same.
    • Provide an introduction to measuring causal effects using observational data using matching and its extension of propensity score based matching with a focus on the a) the intuition and statistics behind it b) Tips from the trenches, basis the speakers experience in these techniques and c) Practical limitations of such approaches
    • Walk through an example of how matching was applied to get to causal insights regarding effectiveness of a digital product for a major retailer.
    • Finally conclude with why understanding having a nuanced understanding of causality is all the more important in the big data era we are into.
  • Dr. Saptarsi Goswami
    keyboard_arrow_down

    Dr. Saptarsi Goswami - Mastering feature selection: basics for developing your own algorithm

    45 Mins
    Tutorial
    Beginner

    Feature selection is one of the most important processes for pattern recognition, machine learning and data mining problems. A successful feature selection method facilitates improvement of learning model performance and interpretability as well as reduces computational cost of the classifier by dimensionality reduction of the data. Feature selection is computationally expensive and becomes intractable even for few 100 features. This is a relevant problem because text, image and next generation sequence data all are inherently high dimensional. In this talk, I will discuss about few algorithms we have developed in last 5/6 years. Firstly, we will set the context of feature selection ,with some open issues , followed by definition and taxonomy. Which will take about 20 odd minutes. Then in next 20 minutes we will discuss couple of research efforts where we have improved feature selection for textual data and proposed a graph based mechanism to view the feature interaction. After the talk, participants will be appreciate the need of feature selection, the basic principles of feature selection algorithm and finally how they can start developing their own models

  • Dr. C.S.Jyothirmayee
    keyboard_arrow_down

    Dr. C.S.Jyothirmayee / Usha Rengaraju / Vijayalakshmi Mahadevan - Deep learning powered Genomic Research

    90 Mins
    Workshop
    Advanced

    The event disease happens when there is a slip in the finely orchestrated dance between physiology, environment and genes. Treatment with chemicals (natural, synthetic or combination) solved some diseases but others persisted and got propagated along the generations. Molecular basis of disease became prime center of studies to understand and to analyze root cause. Cancer also showed a way that origin of disease, detection, prognosis and treatment along with cure was not so uncomplicated process. Treatment of diseases had to be done case by case basis (no one size fits).

    With the advent of next generation sequencing, high through put analysis, enhanced computing power and new aspirations with neural network to address this conundrum of complicated genetic elements (structure and function of various genes in our systems). This requires the genomic material extraction, their sequencing (automated system) and analysis to map the strings of As, Ts, Gs, and Cs which yields genomic dataset. These datasets are too large for traditional and applied statistical techniques. Consequently, the important signals are often incredibly small along with blaring technical noise. This further requires far more sophisticated analysis techniques. Artificial intelligence and deep learning gives us the power to draw clinically useful information from the genetic datasets obtained by sequencing.

    Precision of these analyses have become vital and way forward for disease detection, its predisposition, empowers medical authorities to make fair and situationally decision about patient treatment strategies. This kind of genomic profiling, prediction and mode of disease management is useful to tailoring FDA approved treatment strategies based on these molecular disease drivers and patient’s molecular makeup.

    Now, the present scenario encourages designing, developing, testing of medicine based on existing genetic insights and models. Deep learning models are helping to analyze and interpreting tiny genetic variations ( like SNPs – Single Nucleotide Polymorphisms) which result in unraveling of crucial cellular process like metabolism, DNA wear and tear. These models are also responsible in identifying disease like cancer risk signatures from various body fluids. They have the immense potential to revolutionize healthcare ecosystem. Clinical data collection is not streamlined and done in a haphazard manner and the requirement of data to be amenable to a uniform fetchable and possibility to be combined with genetic information would power the value, interpretation and decisive patient treatment modalities and their outcomes.

    There is hugh inflow of medical data from emerging human wearable technologies, along with other health data integrated with ability to do quickly carry out complex analyses on rich genomic databases over the cloud technologies … would revitalize disease fighting capability of humans. Last but still upcoming area of application in direct to consumer genomics (success of 23andMe).

    This road map promises an end-to-end system to face disease in its all forms and nature. Medical research, and its applications like gene therapies, gene editing technologies like CRISPR, molecular diagnostics and precision medicine could be revolutionized by tailoring a high-throughput computing method and its application to enhanced genomic datasets.

  • Badri Narayanan Gopalakrishnan
    keyboard_arrow_down

    Badri Narayanan Gopalakrishnan / Shalini Sinha / Usha Rengaraju - Lifting Up: How AI and Big data can contribute to anti-poverty programs

    45 Mins
    Case Study
    Intermediate

    Ending poverty and zero hunger are top two goals United Nations aims to achieve by 2030 under its sustainable development program. Hunger and poverty are byproducts of multiple factors and fighting them require multi-fold effort from all stakeholders. Artificial Intelligence and Machine learning has transformed the way we live, work and interact. However economics of business has limited its application to few segments of the society. A much conscious effort is needed to bring the power of AI to the benefits of the ones who actually need it the most – people below the poverty line. Here we present our thoughts on how deep learning and big data analytics can be combined to enable effective implementation of anti-poverty programs. The advancements in deep learning , micro diagnostics combined with effective technology policy is the right recipe for a progressive growth of a nation. Deep learning can help identify poverty zones across the globe based on night time images where the level of light correlates to higher economic growth. Once the areas of lower economic growth are identified, geographic and demographic data can be combined to establish micro level diagnostics of these underdeveloped area. The insights from the data can help plan an effective intervention program. Machine Learning can be further used to identify potential donors, investors and contributors across the globe based on their skill-set, interest, history, ethnicity, purchasing power and their native connect to the location of the proposed program. Adequate resource allocation and efficient design of the program will also not guarantee success of a program unless the project execution is supervised at grass-root level. Data Analytics can be used to monitor project progress, effectiveness and detect anomaly in case of any fraud or mismanagement of funds.

  • Juan Manuel Contreras
    keyboard_arrow_down

    Juan Manuel Contreras - How to lead data science teams: The 3 D's of data science leadership

    Juan Manuel Contreras
    Juan Manuel Contreras
    Data Science Manager
    Uber
    schedule 4 years ago
    Sold Out!
    45 Mins
    Talk
    Advanced

    Despite the increasing number of data scientists who are asked to take on leadership roles as they grow in their careers, there are still few resources on how to lead data science teams successfully.

    In this talk, I will argue that an effective data science leader has to wear three hats: Diplomat (understand the organization and their team and liaise between them), Diagnostician (figure out how what organizational needs can be met by their team and how), and Developer (grow their and their team's skills as well as the organization's understanding of data science to maximize the value their team can drive).

    Throughout, I draw on my experience as a data science leader both at a political party (the Democratic Party of the United States of America) and at a fintech startup (Even.com).

    Talk attendees will learn a framework for how to manage data scientists and lead a data science practice. In turn, attendees will be better prepared to tackle new or existing roles as data science leaders or be better able to identify promising candidates for these roles.

  • Johnu George
    keyboard_arrow_down

    Johnu George / Ramdoot Kumar P - A Scalable Hyperparameter Optimization framework for ML workloads

    20 Mins
    Demonstration
    Intermediate

    In machine learning, hyperparameters are parameters that governs the training process itself. For example, learning rate, number of hidden layers, number of nodes per layer are typical hyperparameters for neural networks. Hyperparameter Tuning is the process of searching the best hyper parameters to initialize the learning algorithm, thus improving training performance.

    We present Katib, a scalable and general hyper parameter tuning framework based on Kubernetes which is ML framework agnostic (Tensorflow, Pytorch, MXNet, XGboost etc). You will learn about Katib in Kubeflow, an open source ML toolkit for Kubernetes, as we demonstrate the advantages of hyperparameter optimization by running a sample classification problem. In addition, as we dive into the implementation details, you will learn how to contribute as we expand this platform to include autoML tools.

  • Ishita Mathur
    keyboard_arrow_down

    Ishita Mathur - How GO-FOOD built a Query Semantics Engine to help you find food faster

    Ishita Mathur
    Ishita Mathur
    Data Scientist
    Gojek Tech
    schedule 4 years ago
    Sold Out!
    45 Mins
    Case Study
    Beginner

    Context: The Search problem

    GOJEK is a SuperApp: 19+ apps within an umbrella app. One of these is GO-FOOD, the first food delivery service in Indonesia and the largest food delivery service in Southeast Asia. There are over 300 thousand restaurants on the platform with a total of over 16 million dishes between them.

    Over two-thirds of those who order food online using GO-FOOD do so by utilising text search. Search engines are so essential to our everyday digital experience that we don’t think twice when using them anymore. Search engines involve two primary tasks: retrieval of documents and ranking them in order of relevance. While improving that ranking is an extremely important part of improving the search experience, actually understanding that query helps give the searcher exactly what they’re looking for. This talk will show you what we are doing to make it easy for users to find what they want.

    GO-FOOD uses the ElasticSearch stack with restaurant and dish indexes to search for what the user types. However, this results in only exact text matches and at most, fuzzy matches. We wanted to create a holistic search experience that not only personalised search results, but also retrieved restaurants and dishes that were more relevant to what the user was looking for. This is being done by not only taking advantage of ElasticSearch features, but also developing a Query semantics engine.

    Query Understanding: What & Why

    This is where Query Understanding comes into the picture: it’s about using NLP to correctly identify the search intent behind the query and return more relevant search results, it’s about the interpretation process even before the results are even retrieved and ranked. The semantic neighbours of the query itself become the focus of the search process: after all, if I don’t understand what you’re trying to ask for, how will I give you what you want?

    In the duration of this talk, you will learn about how we are taking advantage of word embeddings to build a Query Understanding Engine that is holistically designed to make the customer’s experience as smooth as possible. I will go over the techniques we used to build each component of the engine, the data and algorithmic challenges we faced and how we solved each problem we came across.

  • Sujoy Roychowdhury
    keyboard_arrow_down

    Sujoy Roychowdhury - Building Multimodal Deep learning recommendation Systems

    20 Mins
    Talk
    Intermediate

    Recommendation systems aid in consumer decision making processes
    like what to buy, which books to read or movies to watch.
    Recommendation systems are specially useful in e-commerce websites
    where a user has to navigate through several hundred items
    in order to get to what they’re looking for . The data on how users
    interact with the systems can be used to analyze user behaviour and
    make recommendations that are in line with users’ preferences of
    certain item attributes over others. Collaborative filtering has, until
    recently, been able to achieve personalization through user based
    and item based collaborative filtering techniques. Recent advances
    in the application of Deep Learning in research as well as industry
    has led people to apply these techniques in recommendation systems.
    Many recommendation systems use product features for recommendations.
    However textual features available on products are
    almost invariably incomplete in real-world datasets due to various
    process related issues. Additionally, product features even when
    available cannot describe completely a certain feature. These limit
    the success of such recommendation techniques. Deep learning
    systems can process multi-modal data like text, images, audio and
    thus is our choice in implementing multi-modal recommendation
    system.
    In this talk we show a real-world application of a fashion recommendation
    system. This is based on a multi-modal deep learning system which is able to address the problem of poor annotation in the product data. We evaluate different deep learning architectures
    to process multi-modal data and compare their effectiveness. We
    highlight the trade-offs seen in a real-world implementation and
    how these trade-offs affect the actual choice of the architecture.

  • Venkata Pingali
    keyboard_arrow_down

    Venkata Pingali - Accelerating ML using Production Feature Engineering Platform

    Venkata Pingali
    Venkata Pingali
    Co-Founder & CEO
    Scribble Data
    schedule 4 years ago
    Sold Out!
    45 Mins
    Talk
    Intermediate

    Anecdotally only 2% of the models developed are productionized, i.e., used day to day to improve business outcomes. Part of the reason is the high cost and complexity of productionization of models. It is estimated to be anywhere from 40 to 80% of the overall work.

    In this talk, we will share Scribble Data’s insights into productionization of ML, and how to reduce the cost and complexity in organizations. It is based on the last two years of work at Scribble developing and deploying production ML Feature Engineering Platform, and study of platforms from major organizations such as Uber. This talk expands on a previous talk given in January.

    First, we discuss the complexity of production ML systems, and where time and effort goes. Second, we give an overview of feature engineering, which is an expensive ML task, and the associated challenges Third, we suggest an architecture for Production Feature Engineering platform. Last, we discuss how one could go about building one for your organization

  • Ramanathan R
    keyboard_arrow_down

    Ramanathan R / Gurram Poorna Prudhvi - Time Series analysis in Python

    240 Mins
    Workshop
    Intermediate

    “Time is precious so is Time Series Analysis”

    Time series analysis has been around for centuries helping us to solve from astronomical problems to business problems and advanced scientific research around us now. Time stores precious information, which most machine learning algorithms don’t deal with. But time series analysis, which is a mix of machine learning and statistics helps us to get useful insights. Time series can be applied to various fields like economy forecasting, budgetary analysis, sales forecasting, census analysis and much more. In this workshop, We will look at how to dive deep into time series data and make use of deep learning to make accurate predictions.

    Structure of the workshop goes like this

    • Introduction to Time series analysis
    • Time Series Exploratory Data Analysis and Data manipulation with pandas
    • Forecast Time series data with some classical method (AR, MA, ARMA, ARIMA, GARCH, E-GARCH)
    • Introduction to Deep Learning and Time series forecasting using MLP and LSTM
    • Forecasting using XGBoost
    • Financial Time Series data

    Libraries Used:

    • Keras (with Tensorflow backend)
    • matplotlib
    • pandas
    • statsmodels
    • sklearn
    • seaborn
    • arch
  • Anuj Gupta
    keyboard_arrow_down

    Anuj Gupta - Natural Language Processing Bootcamp - Zero to Hero

    480 Mins
    Workshop
    Intermediate

    Data is the new oil and unstructured data, especially text, images and videos contain a wealth of information. However, due to the inherent complexity in processing and analyzing this data, people often refrain from spending extra time and effort in venturing out from structured datasets to analyze these unstructured sources of data, which can be a potential gold mine. Natural Language Processing (NLP) is all about leveraging tools, techniques and algorithms to process and understand natural language based unstructured data - text, speech and so on.

    Being specialized in domains like computer vision and natural language processing is no longer a luxury but a necessity which is expected of any data scientist in today’s fast-paced world! With a hands-on and interactive approach, we will understand essential concepts in NLP along with extensive case- studies and hands-on examples to master state-of-the-art tools, techniques and frameworks for actually applying NLP to solve real- world problems. We leverage Python 3 and the latest and best state-of- the-art frameworks including NLTK, Gensim, SpaCy, Scikit-Learn, TextBlob, Keras and TensorFlow to showcase our examples. You will be able to learn a fair bit of machine learning as well as deep learning in the context of NLP during this bootcamp.

    In our journey in this field, we have struggled with various problems, faced many challenges, and learned various lessons over time. This workshop is our way of giving back a major chunk of the knowledge we’ve gained in the world of text analytics and natural language processing, where building a fancy word cloud from a bunch of text documents is not enough anymore. You might have had questions like ‘What is the right technique to solve a problem?’, ‘How does text summarization really work?’ and ‘Which are the best frameworks to solve multi-class text categorization?’ among many other questions! Based on our prior knowledge and learnings from publishing a couple of books in this domain, this workshop should help readers avoid some of the pressing issues in NLP and learn effective strategies to master NLP.

    The intent of this workshop is to make you a hero in NLP so that you can start applying NLP to solve real-world problems. We start from zero and follow a comprehensive and structured approach to make you learn all the essentials in NLP. We will be covering the following aspects during the course of this workshop with hands-on examples and projects!

    • Basics of Natural Language and Python for NLP tasks
    • Text Processing and Wrangling
    • Text Understanding - POS, NER, Parsing
    • Text Representation - BOW, Embeddings, Contextual Embeddings
    • Text Similarity and Content Recommenders
    • Text Clustering
    • Topic Modeling
    • Text Summarization
    • Sentiment Analysis - Unsupervised & Supervised
    • Text Classification with Machine Learning and Deep Learning
    • Multi-class & Multi-Label Text Classification
    • Deep Transfer Learning and it's promise
    • Applying Deep Transfer Learning - Universal Sentence Encoders, ELMo and BERT for NLP tasks
    • Generative Deep Learning for NLP
    • Next Steps

    With over 10 hands-on projects, the bootcamp will be packed with plenty of hands-on examples for you to go through, try out and practice and we will try to keep theory to a minimum considering the limited time we have and the amount of ground we want to cover. We hope at the end of this workshop you can takeaway some useful methodologies to apply for solving NLP problems in the future. We will be using Python to showcase all our examples.

  • Joy Mustafi
    keyboard_arrow_down

    Joy Mustafi / Aditya Bhattacharya - Person Identification via Multi-Modal Interface with Combination of Speech and Image Data

    90 Mins
    Workshop
    Intermediate

    Multi-Modalities

    Having multiple modalities in a system gives more affordance to users and can contribute to a more robust system. Having more also allows for greater accessibility for users who work more effectively with certain modalities. Multiple modalities can be used as backup when certain forms of communication are not possible. This is especially true in the case of redundant modalities in which two or more modalities are used to communicate the same information. Certain combinations of modalities can add to the expression of a computer-human or human-computer interaction because the modalities each may be more effective at expressing one form or aspect of information than others. For example, MUST researchers are working on a personalized humanoid built and equipped with various types of input devices and sensors to allow them to receive information from humans, which are interchangeable and a standardized method of communication with the computer, affording practical adjustments to the user, providing a richer interaction depending on the context, and implementing robust system with features like; keyboard; pointing device; touchscreen; computer vision; speech recognition; motion, orientation etc.

    There are six types of cooperation between modalities, and they help define how a combination or fusion of modalities work together to convey information more effectively.

    • Equivalence: information is presented in multiple ways and can be interpreted as the same information
    • Specialization: when a specific kind of information is always processed through the same modality
    • Redundancy: multiple modalities process the same information
    • Complimentarity: multiple modalities take separate information and merge it
    • Transfer: a modality produces information that another modality consumes
    • Concurrency: multiple modalities take in separate information that is not merged

    Computer - Human Modalities

    Computers utilize a wide range of technologies to communicate and send information to humans:

    • Vision - computer graphics typically through a screen
    • Audition - various audio outputs

    Project Features

    Adaptive: They MUST learn as information changes, and as goals and requirements evolve. They MUST resolve ambiguity and tolerate unpredictability. They MUST be engineered to feed on dynamic data in real time.

    Interactive: They MUST interact easily with users so that those users can define their needs comfortably. They MUST interact with other processors, devices, services, as well as with people.

    Iterative and Stateful: They MUST aid in defining a problem by asking questions or finding additional source input if a problem statement is ambiguous or incomplete. They MUST remember previous interactions in a process and return information that is suitable for the specific application at that point in time.

    Contextual: They MUST understand, identify, and extract contextual elements such as meaning, syntax, time, location, appropriate domain, regulation, user profile, process, task and goal. They may draw on multiple sources of information, including both structured and unstructured digital information, as well as sensory inputs (visual, gestural, auditory, or sensor-provided).

    Project Demos

    Multi-Modal Interaction: https://www.youtube.com/watch?v=jQ8Gq2HWxiA

    Gesture Detection: https://www.youtube.com/watch?v=rDSuCnC8Ei0

    Speech Recognition: https://www.youtube.com/watch?v=AewM3TsjoBk

    Assignment (Hands-on Challenge for Attendees)

    Real-time multi-modal access control system for authorized access to work environment - All the key concepts and individual steps will be demonstrated and explained in this workshop, and the attendees need to customize the generic code or approach for this assignment or hands-on challenge.

  • Dr. Rahee Walambe
    keyboard_arrow_down

    Dr. Rahee Walambe / Vishal Gokhale - Processing Sequential Data using RNNs

    480 Mins
    Workshop
    Beginner

    Data that forms the basis of many of our daily activities like speech, text, videos has sequential/temporal dependencies. Traditional deep learning models, being inadequate to model this connectivity needed to be made recurrent before they brought technologies such as voice assistants (Alexa, Siri) or video based speech translation (Google Translate) to a practically usable form by reducing the Word Error Rate (WER) significantly. RNNs solve this problem by adding internal memory. The capacities of traditional neural networks are bolstered with this addition and the results outperform the conventional ML techniques wherever the temporal dynamics are more important.
    In this full-day immersive workshop, participants will develop an intuition for sequence models through hands-on learning along with the mathematical premise of RNNs.

  • Ashay Tamhane
    keyboard_arrow_down

    Ashay Tamhane - Modeling Contextual Changes In User Behaviour In Fashion e-commerce

    Ashay Tamhane
    Ashay Tamhane
    Staff Data Scientist
    Swiggy
    schedule 4 years ago
    Sold Out!
    20 Mins
    Talk
    Intermediate

    Impulse purchases are quite frequent in fashion e-commerce; browse patterns indicate fluid context changes across diverse product types probably due to the lack of a well-defined need at the consumer’s end. Data from fashion e-commerce portal indicate that the final product a person ends-up purchasing is often very different from the initial product he/she started the session with. We refer to this characteristic as a ‘context change’. This feature of fashion e-commerce makes understanding and predicting user behaviour quite challenging. Our work attempts to model this characteristic so as to both detect and preempt context changes. Our approach employs a deep Gated Recurrent Unit (GRU) over clickstream data. We show that this model captures context changes better than other non-sequential baseline models.

  • Akshay Bahadur
    keyboard_arrow_down

    Akshay Bahadur - Minimizing CPU utilization for deep networks

    Akshay Bahadur
    Akshay Bahadur
    SDE-I
    Symantec Softwares
    schedule 4 years ago
    Sold Out!
    45 Mins
    Demonstration
    Beginner

    The advent of machine learning along with its integration with computer vision has enabled users to efficiently to develop image-based solutions for innumerable use cases. A machine learning model consists of an algorithm which draws some meaningful correlation between the data without being tightly coupled to a specific set of rules. It's crucial to explain the subtle nuances of the network along with the use-case we are trying to solve. With the advent of technology, the quality of the images has increased which in turn has increased the need for resources to process the images for building a model. The main question, however, is to discuss the need to develop lightweight models keeping the performance of the system intact.
    To connect the dots, we will talk about the development of these applications specifically aimed to provide equally accurate results without using much of the resources. This is achieved by using image processing techniques along with optimizing the network architecture.
    These applications will range from recognizing digits, alphabets which the user can 'draw' at runtime; developing state of the art facial recognition system; predicting hand emojis, developing a self-driving system, detecting Malaria and brain tumor, along with Google's project of 'Quick, Draw' of hand doodles.
    In this presentation, we will discuss the development of such applications with minimization of CPU usage.

  • Venkatraman J
    keyboard_arrow_down

    Venkatraman J - Entity Co-occurence and Entity Reputation scoring from Unstructured data using Semantic Knowledge graph

    20 Mins
    Talk
    Intermediate

    Knowledge representation has been a research for many years in AI world and its continuing further too. Once knowledge is represented, reasoning from that extracted knowledge is done by various inferencing techniques. Initial knowledge bases were built using rules from domain experts and different inferencing techniques like Fuzzy inference, Bayesian inference were applied to extract reasoning from those knowledge bases. Semantic networks is another form of knowledge representation which can represent structured data like WordNet, DBpedia which solves problems in a specific domain by storing entities and relations among entities using onotologies.

    Knowledge graph is another representation technique deeply researched in academia as well as used by businesses in production to augment search relevancy in information retrieval(Google knowledgegraph), improve recommender systems, semantic search applications and also Question answering problems.In this talk i will illustrate the benefits of semantic knowledge graph, how it differs from Semantic ontologies, different technologies involved in building knowledge graph, how i built one to analyse unstructured (twitter data) to discover hidden relationships from the twitter corpus. I will also show how Knowledge graph is data scientist's tool kit to discover hidden relationships and insights from unstructured data quickly.

    In this talk i will show the technology and architecture used to determine entity reputation and entity co-occurence using Knowledge graph.Scoring an entity for reputation is useful in many Natural language processing tasks and applications such as Recommender systems.

  • Suvro Shankar Ghosh
    keyboard_arrow_down

    Suvro Shankar Ghosh - Learning Entity embedding’s form Knowledge Graph

    45 Mins
    Case Study
    Intermediate
    • Over a period of time, a lot of Knowledge bases have evolved. A knowledge base is a structured way of storing information, typically in the following form Subject, Predicate, Object
    • Such Knowledge bases are an important resource for question answering and other tasks. But they often suffer from their incompleteness to resemble all the data in the world, and thereby lack of ability to reason over their discrete Entities and their unknown relationships. Here we can introduce an expressive neural tensor network that is suitable for reasoning over known relationships between two entities.
    • With such a model in place, we can ask questions, the model will try to predict the missing data links within the trained model and answer the questions, related to finding similar entities, reasoning over them and predicting various relationship types between two entities, not connected in the Knowledge Graph.
    • Knowledge Graph infoboxes were added to Google's search engine in May 2012

    What is the knowledge graph?

    ▶Knowledge in graph form!

    ▶Captures entities, attributes, and relationships

    More specifically, the “knowledge graph” is a database that collects millions of pieces of data about keywords people frequently search for on the World wide web and the intent behind those keywords, based on the already available content

    ▶In most cases, KGs is based on Semantic Web standards and have been generated by a mixture of automatic extraction from text or structured data, and manual curation work.

    ▶Structured Search & Exploration
    e.g. Google Knowledge Graph, Amazon Product Graph

    ▶Graph Mining & Network Analysis
    e.g. Facebook Entity Graph

    ▶Big Data Integration
    e.g. IBM Watson

    ▶Diffbot, GraphIQ, Maana, ParseHub, Reactor Labs, SpazioDati

help