The premier global professional body of data science and machine learning professionals — the Association of Data Scientists (ADaSci), has come out with its much-awaited Deep Learning DevCon 2021 (DLDC). The two-day virtual conference on deep learning will be held on 23rd and 24th September, bringing influential professionals and researchers in the deep learning domain on a single platform.

There will be seminars, paper presentations, exhibitions, and hackathons at the summit. A full-day training on deep learning will also be offered, with attendees receiving a certificate of attendance. Moreover, it provides you with the unique opportunity to network with fellow attendees, talk to them, and meet companies virtually. DLDC 2021 has a strong lineup of speakers. Below are a few sessions that one must not miss:

1| State of AI and Deep Learning

When: 23 September, 09:45 – 10:30

By: Mohan Silaparasetty, Head – Technology Programs, Times Professional Learning

Artificial Intelligence and Deep Learning are evolving rapidly with some new and exciting advances every day. There is also a race among the countries to establish a dominant position in AI. This keynote is about the latest advances in Deep Learning and the latest applications of AI worldwide.

2| Understanding and Leveraging Differential Privacy

When: 23 September, 10:35 – 11:15

By: Manoj Kumar Rajendran, Principal Data Scientist, MiQ Digital India

With privacy being the buzzword in data collection and analysis, how should the tech world be prepared for a differentially private world? In this session, Manoj will present how Differential privacy allows digital companies to acquire and share aggregate information about user habits while protecting individual users’ privacy.

3| Lap Estimate Optimizer: Transforming race-day strategy with AI

When: 23 September, 11:20 – 12:00

By: Vikas Behrani, Vice President – Data Science, Genpact

Formula E has gained popularity as a sustainability-conscious sport that originates innovations to improve electric vehicles. The premise behind Formula E is not only that the cars are fully electric, but that the 11 teams, each with two drivers, compete in identically set-up, electric battery-powered race cars. The purpose of this exercise is to define the approach to use historical data to predict the number of laps a car would finish in 45min for an upcoming race. The team at Genpact built an ensemble model with a combination of an intuitive mathematical model and an instinctive deep learning model to predict the number of laps at the end of every race.

4| Dealing with Data imbalance in classification problems

When: 23 September, 14:00 – 14:40

By: Raghavendra Nagaraja Rao Data Science Academic Lead at Times Professional Learning

Most of the real-world data around classification problems are cursed with the imbalance of the target column. ML models are biased towards the majority class and result in incorrect predictions. Different techniques like up-sampling, down-sampling, SMOTE etc. are used to deal with such imbalance data which in turn enhances the performance of the classification model

5| To data prep or to data science. That’s the question

When: 23 September, 14:45 – 15:25

By: Swagata Maiti, Technology Architect, IP & Data Products & Shaji Thomas Vice President, Cloud & Data Engineering, both at Ugam, A Merkle Company

Both the experts, Shaji Thomas and Swagata Maiti from Ugam, a Merkle company, will deep-dive into seven techniques that will help data scientists build a scalable data platform. These techniques include automated data validation, reusable feature stores, streaming ingestion, the transformation of IoT sensor data, and more. Join the session to get an understanding of challenges faced by data scientists, how to address these challenges, and Snowflake capabilities that simplify building a scalable cloud data platform.

6| AI-Powered Document Intelligence for Enterprises

When: 23 September, 16:15 – 16:55

By: Rahul Ghosh, VP of AI Research and Services, American Express AI Labs

A huge volume of the information in an enterprise flows through documents and understanding the structure of documents allows extracting relevant and meaningful information. The focus of the talk is on Document AI, i.e., AI-powered automated analysis of documents. He will share the R&D efforts at American Express and demonstrate how Document AI-enabled products can drive innovation and efficiency at scale.

7| [Paper Presentation] Time Expression Extraction and Normalization in Industrial Setting

When: 24 September, 12:05 – 12:25

By: Piyush Arora, Senior AI researcher, American Express AI Labs

We present TEEN, an industry-grade solution to the problem of time expression extraction and normalization (Timex). Extraction and normalization of temporal units is a challenging problem due to several factors, e.g.,

• same time units may be expressed in different ways
• inherent ambiguity in natural languages leading to multiple interpretations
• context-sensitive nature of natural languages

8| [Workshop] Industrializing AI/ML: Hands-on Model Deployment

When: 24 September, 14:10 – 16:10

By: Jatindra Singh Deo, Senior Technical Architect & Abhilash NVS, Data Scientist, Genpact

This working session will look at a hands-on approach to pipelines and their orchestration using TFX/Airflow. API/SDK approach to model deployment as a web service with Flask Pre-requisite: Laptop with a minimum of 8 GB ram with Windows/Linux/macOS Anaconda (individual edition) installed good internet connection to download coding stubs and pretrained model Docker desktop installed Basic familiarity with google collab with a google account.

9| [Paper Presentation] Global-Local Scalable Explanations Using Linear Model Tree

When: 24 September, 16:40 – 17:00

By: Narayanan Unny E., Head of Machine Learning Research, American Express AI Lab

A Generative Adversarial Network is employed for generating synthetic data, while a piecewise linear model in the form of Linear Model Trees is used as the surrogate model. The combination of these two techniques provides a powerful yet intuitive data structure to explain complex machine learning models. The novelty of this data structure is that it provides an explanation in the form of both decision rules and feature attributions.

10| [Paper Presentation] Predicting Custom Ad Performance Metric using Contextual Features

When: 24 September, 17:05 – 17:25

By: Divyaprabha M, Data Scientist, MiQ Digital

Digital advertising enables advertisers to promote their products on various online and digital channels. Real-Time Bidding is an advanced advertising method that allows advertisers to target potential buyers and acquire ad space on websites in the form of programmatic auctions. The paper proposes a machine learning-based approach to predicting future ad-campaign performance by focusing on contextual features such as browser, operating system, device type, and so on.

Grab the chance to interact and learn from the experienced bunch of data scientists going to present their sessions in the coming days. For more details and schedules, one can visit here.