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  • Liked Dr. Vikas Agrawal
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    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.

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

    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 Sayak Paul
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    Sayak Paul - Interpretable Machine Learning - Fairness, Accountability and Transparency in ML systems

    Sayak Paul
    Sayak Paul
    Data Science Instructor
    DataCamp
    schedule 1 day ago
    Sold Out!
    45 Mins
    Talk
    Beginner

    The good news is building fair, accountable, and transparent machine learning systems is possible. The bad news is it’s harder than many blogs and software package docs would have you believe. The truth is nearly all interpretable machine learning techniques generate approximate explanations, that the fields of eXplainable AI (XAI) and Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) are very new, and that few best practices have been widely agreed upon. This combination can lead to some ugly outcomes!

    This talk aims to make your interpretable machine learning project a success by describing fundamental technical challenges you will face in building an interpretable machine learning system, defining the real-world value proposition of approximate explanations for exact models, and then outlining the following viable techniques for debugging, explaining, and testing machine learning models:

    • Model visualizations including decision tree surrogate models, individual conditional expectation (ICE) plots, partial dependence plots, and residual analysis.
    • Reason code generation techniques like LIME, Shapley explanations, and Tree-interpreter. *Sensitivity Analysis. Plenty of guidance on when, and when not, to use these techniques will also be shared, and the talk will conclude by providing guidelines for testing generated explanations themselves for accuracy and stability.
  • Liked Dipanjan Sarkar
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    Dipanjan Sarkar - Explainable Artificial Intelligence - Demystifying the Hype

    Dipanjan Sarkar
    Dipanjan Sarkar
    Data Scientist
    Red Hat
    schedule 3 weeks ago
    Sold Out!
    45 Mins
    Tutorial
    Intermediate

    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!

  • Liked Maulik Soneji
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    Maulik Soneji - Using ML for Personalizing Food Recommendations

    Maulik Soneji
    Maulik Soneji
    Product Engineer
    Go-jek
    schedule 6 days ago
    Sold Out!
    45 Mins
    Talk
    Beginner

    GoFood, the food delivery product of Gojek is one of the largest of its kind in the world. This talk summarizes the approaches considered and lessons learnt during the design and successful experimentation of a search system that uses ML to personalize the restaurant results based on the user’s food and taste preferences .

    We formulated the estimation of the relevance as a Learning To Rank ML problem which makes the task of performing the ML inference for a very large number of customer-merchant pairs the next hurdle.
    The talk will cover our learnings and findings for the following:
    a. Creating a Learning Model for Food Recommendations
    b. Targetting experiments to a certain percentage of users
    c. Training the model from real time data
    d. Enriching Restaurant data with custom tags

    Our story should help the audience in making design decisions on the data pipelines and software architecture needed when using ML for relevance ranking in high throughput search systems.

  • Liked Nirav Shah
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    Nirav Shah / Ananth Bala - Data Analysis, Dashboards and Visualization - How to create powerful visualizations like a Zen Master

    480 Mins
    Workshop
    Intermediate

    In today’s data economy and disruptive business environment, data is the new oil and data analysis with data visualization is vital for professionals and companies to stay competitive. Data Analysis and developing useful and interactive visualizations which provide insights may seem complex for a non -data professional. That should not be the case, thanks to various BI & data visualization tools. Tableau is one of the most popular one and widely used in various industries by individual users to enterprise roll out.

    In this complete hands-on training session (slides, workbooks and data-sets will be distributed in advance), you will learn to turn your data into interactive dashboards, how to create stories with data and share these dashboards with your audience. We will begin with a quick refresher of basics about design and information literacy and discussions about practices for creating charts and storytelling utilizing best visual practices. Whether your goal is to explain an insight or let your audience explore data insights, using Tableau’s simple drag-and-drop user interface makes the task easy and enjoyable.

    You will learn to use functionalities like Table Calculations, Sets, Filters, Level of Detail expressions, Animations, predictive analytics using forecast functions and understanding Clustering. You will learn to integrate R and Tableau and how to use R within Tableau. Learn advance charts such as Waterfall charts, Pareto charts, Gantt Charts, Control Charts and Box and Whisker’s plot. You will also learn mapping, using parameters and other visual functionalities. You will learn about data preparation – joins, blending, union and Tableau Prep.

  • Liked Dipanjan Sarkar
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    Dipanjan Sarkar - A Hands-on Introduction to Natural Language Processing

    Dipanjan Sarkar
    Dipanjan Sarkar
    Data Scientist
    Red Hat
    schedule 3 weeks ago
    Sold Out!
    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 data, which is usually unstructured like text,
    speech and so on. In this workshop, we will be looking at tried and tested
    strategies, techniques and workflows which can be leveraged by
    practitioners and data scientists to extract useful insights from text data.


    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.


    In my journey in this field so far, I have struggled with various problems,
    faced many challenges, and learned various lessons over time. This
    workshop will contain a major chunk of the knowledge I’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. Perhaps the biggest problem with regard to learning text
    analytics is not a lack of information but too much information, often
    called information overload. There are so many resources,
    documentation, papers, books, and journals containing so much content
    that they often overwhelm someone new to the field. 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 my prior knowledge and learnings from publishing a couple of books in this domain, this workshop should help readers avoid the pressing
    issues I’ve faced in my journey so far and learn the strategies to master NLP.


    This workshop follows a comprehensive and structured approach. First it
    tackles the basics of natural language understanding and Python for
    handling text data in the initial chapters. Once you’re familiar with the
    basics, we cover text processing, parsing and understanding. Then, we
    address interesting problems in text analytics in each of the remaining
    chapters, including text classification, clustering and similarity analysis,
    text summarization and topic models, semantic analysis and named
    entity recognition, sentiment analysis and model interpretation. The last
    chapter is an interesting chapter on the recent advancements made in
    NLP thanks to deep learning and transfer learning and we cover an
    example of text classification with universal sentence embeddings.

  • Liked JAYA SUSAN MATHEW
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    JAYA SUSAN MATHEW - Breaking the language barrier: how do we quickly add multilanguage support in our AI application?

    20 Mins
    Talk
    Intermediate

    With the need to cater to a global audience, there is a growing demand for applications to support speech identification/translation/transliteration from one language to another. This session aims at introducing the audience to the topic and how to quickly use some of the readily available APIs to identify, translate or even transliterate speech/text within their application.

  • Liked Kumar Nityan Suman
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    Kumar Nityan Suman - Beating BERT at NER For E-Commerce Products

    Kumar Nityan Suman
    Kumar Nityan Suman
    Data Scientist
    Youplus Inc.
    schedule 1 week ago
    Sold Out!
    45 Mins
    Tutorial
    Intermediate

    Natural Language Processing is a messy and complicated affair but modern advanced techniques are offering increasingly impressive results. Embeddings are a modern machine learning technique that has taken the natural language processing world by storm.

    This hands-on tutorial will showcase the advantage of learning custom Word and Character Embeddings for natural language problems over pre-trained vectors like ELMo and BERT using a Named Entity Recognition case study over e-commerce data.

  • Liked Solomon T
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    Solomon T - Digital Epidemiologist - How to become Dr. Sherlock Holmes of 21st Century ?

    20 Mins
    Talk
    Beginner
    Please write to me, for more information
    Digital Epidemiology is "the study of what is upon netizens using their digital footprint".

    Digital - relating to computer technology, especially the internet

    Epidemiology - "Epidemiology" literally means "the study of what is upon the people." The word comes form the Greek epi, meaning "upon," demos, meaning "people," and logos, meaning "study."

    Netizen - a person who uses the internet

    Digital Footprint - the information about a particular person that exists on the Internet as a result of their online activities

    Digital Epidemiology, as defined by Solomon Thirumurugan, is "the study of what is upon netizens using their digital footprint."

    Sources :

    1. https://dictionary.cambridge.org/dictionary/english/digital
    2. https://www.iwh.on.ca/what-researchers-mean-by/epidemiology
    3. https://dictionary.cambridge.org/dictionary/english/netizen
    4. https://www.oxfordlearnersdictionaries.com/definition/english/digital-footprint
  • Liked Anoop Kulkarni
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    Anoop Kulkarni - Generative Adversarial Networks in healthcare

    Anoop Kulkarni
    Anoop Kulkarni
    CEO
    Innotomy Consulting
    schedule 4 weeks ago
    Sold Out!
    45 Mins
    Tutorial
    Advanced

    Deep learning and machine learning have infested every known field in last couple of years. Healthcare has not remained immune to it either. This tutorial discusses trends of using deep learning in healthcare especially role of generative adversarial networks (GAN) in radiology and genomics.

    This tutorial will showcase a primer of using deep learning and GAN and chart out a roadmap of what lies ahead.

  • Liked Anoop Kulkarni
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    Anoop Kulkarni - Success of deep learning in radiology

    Anoop Kulkarni
    Anoop Kulkarni
    CEO
    Innotomy Consulting
    schedule 4 weeks ago
    Sold Out!
    45 Mins
    Tutorial
    Advanced

    Deep learning and machine learning have infested every known field in last couple of years. Healthcare has not remained immune to it either. This tutorial discusses trends of using deep learning in radiological applications.

    This tutorial will present a primer of using deep learning in radiology applications such as detection of pneumonia from lung images, diabetic retinopathy in eye images, detection of lung cancer nodules and detection of cell nuclei among others.

  • Liked Anoop Kulkarni
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    Anoop Kulkarni - Deep Learning in Healthcare

    Anoop Kulkarni
    Anoop Kulkarni
    CEO
    Innotomy Consulting
    schedule 1 month ago
    Sold Out!
    45 Mins
    Tutorial
    Advanced

    Deep learning and machine learning have infested every known field in last couple of years. Healthcare has not remained immune to it either. This tutorial discusses trends of using deep learning in healthcare, from image radiolographs to timeseries ECG and EEG and to genomics.

    This tutorial will showcase a primer of using deep learning in genomics and chart out a roadmap of what lies ahead.

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