Top 10 Deep Learning Sessions To Look Forward To At DVDC 2020


The Deep Learning DevCon 2020, DLDC 2020, has thrilling talks and periods across the newest developments within the discipline of deep studying, that won’t solely be attention-grabbing for professionals of this discipline but additionally for the lovers who’re prepared to make a profession within the discipline of deep studying. The two-day convention scheduled for 29th and 30th October will host paper displays, tech talks, workshops that can uncover some attention-grabbing developments in addition to the most recent analysis and development of this space. Further to this, with deep studying gaining huge traction, this convention will spotlight some fascinating use instances the world over.

Here are ten attention-grabbing talks and periods of DLDC 2020 that one ought to positively attend:

Also Read: Why Deep Learning DevCon Comes At The Right Time



Adversarial Robustness in Deep Learning

By Dipanjan Sarkar

About: Adversarial Robustness in Deep Learning is a session offered by Dipanjan Sarkar, a Data Science Lead at Applied Materials, in addition to a Google Developer Expert in Machine Learning. In this session, he’ll deal with the adversarial robustness within the discipline of deep studying, the place he talks about its significance, various kinds of adversarial assaults, and can showcase some methods to coach the neural networks with adversarial realisation. Considering summary deep studying has introduced us large achievements within the fields of laptop imaginative and prescient and pure language processing, this speak might be actually attention-grabbing for folks working on this space. With this session, the attendees could have a complete understanding of adversarial perturbations within the discipline of deep studying and methods to take care of them with widespread recipes.

Read an interview with Dipanjan Sarkar.

Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER

By Divye Singh

About: Imbalance Handling with Combination of Deep Variational Autoencoder and NEATER is a paper presentation by Divye Singh, who has a masters in expertise diploma in Mathematical Modeling and Simulation and has the curiosity to analysis within the discipline of synthetic intelligence, learning-based programs, machine studying, and many others. In this paper presentation, he’ll speak in regards to the widespread downside of sophistication imbalance in medical analysis and anomaly detection, and the way the issue will be solved with a deep studying framework. The speak focuses on the paper, the place he has proposed a synergistic over-sampling technique producing informative artificial minority class knowledge by filtering the noise from the over-sampled examples. Further, he can even showcase the experimental outcomes on a number of real-life imbalanced datasets to show the effectiveness of the proposed technique for binary classification issues.

Default Rate Prediction Models for Self-Employment in Korea utilizing Ridge, Random Forest & Deep Neural Network

By Dongsuk Hong

About: This is a paper presentation given by Dongsuk Hong, who’s a PhD in Computer Science, and works within the massive knowledge centre of Korea Credit Information Services. This speak will introduce the attendees with machine studying and deep studying fashions for predicting self-employment default charges utilizing credit score data. He will speak in regards to the research, the place the DNN mannequin is applied for 2 functions — a sub-model for the choice of credit score data variables; and works for cascading to the ultimate mannequin that predicts default charges. Hong’s most important analysis space is knowledge evaluation of credit score data, the place she is especially fascinated about evaluating the efficiency of prediction fashions based mostly on machine studying and deep studying. This speak might be attention-grabbing for the deep studying practitioners who’re prepared to make a profession on this discipline.

S&P 500 Stock’s Movement Prediction utilizing Deep Learning

By Rahul Gupta

About: This is once more a paper presentation offered by Rahul Gupta, who’s working as a senior supervisor at Quinnox Inc. He will discuss predicting the motion of inventory that consists of the S&P 500 index. Traditionally, many approaches have been utilized utilizing varied strategies that may predict the inventory motion, nevertheless, the success of synthetic neural community has paved the way in which to allow prediction utilizing cutting-edge analysis within the ML and deep studying discipline. In this speak, Rahul Gupta will discuss these papers which have carried out an important job in implementing and explaining the advantages of those new applied sciences. This speak will present a complete understanding of such friends and the way these go into the complexity of the monetary knowledge and largely utilise single dimension knowledge, however had been profitable in creating the bottom for future analysis.

Deep Learning For Tabular Data

By Luca Massaron

About: Deep Learning For Tabular Data is a tech speak offered by Luca Massaron, who’s a knowledge scientist and an writer of the favored dummies collection on machine studying and synthetic intelligence. Along with that, Massaron can also be a Google Developer skilled and in addition a former prime 10 Kaggle grasp. Tabular knowledge is a sort of information that’s structured into rows, every of which consists of details about one thing. In these knowledge sorts, the cells throughout the identical column are designed to offer values for a similar property of the issues described by every row, which is totally completely different from the opposite line-oriented codecs. In this speak, Massaron will share his experience on how deep studying can be utilized for such forms of tabular knowledge.

Also Read: Top 7 Upcoming Deep Learning Conferences To Watch Out For

Stacking Ensembles to Solve Kaggle: Titanic – Machine Learning from Disaster

By Loveesh Bhatt

About: Stacking Ensembles to Solve Kaggle: Titanic – Machine Learning from Disaster is a workshop session offered by Loveesh Bhatt, who’s a seasoned technical skilled in machine studying, pure language processing, and superior analytics. In this session, he’ll showcase tips on how to construct correct machine studying fashions, which is a continuing endeavour for knowledge scientists. The session will train the attendees tips on how to ensemble modelling, created utilizing completely different Python libraries and tips on how to enhance mannequin accuracy and efficiency. This workshop might be a reside implementation on the extensively participated – Titanic Survival Analysis classification downside and can show how stacked ensembles can outperform conventional algorithms. Further, this workshop would entail constructing ensembles which might be extremely customisable on the particular person algorithm degree. In addition, the attendees would have the ability to learn to experiment with stacked ensembles utilizing some extremely automated libraries from h2o resembling AutoML.

Production of 3D Bioartificial “Organoids” utilizing Quantum-enabled deep studying Neural Networks

By Raul Villamarin Rodriguez

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ResNet50 in PyTorch with TPU

About: Organ failure has been a crucial concern for medical doctors within the present period, and thus there may be an pressing want for organ transplants. However, the dearth of donors might result in a worldwide disaster shortly. In this session, offered by Raul Villamarin Rodriguez, who’s the Dean of the School of Business at Woxsen University, will discuss an optimum answer, i.e. manufacturing of 3D bioartificial organoids leveraging quantum-enabled deep studying neural networks. This is completed utilizing quantum-enabled deep studying CNNs. In this speak, Raul will spotlight the process’s accuracy that may additional be enhanced by implementing a set of options resembling evolutionary and genetic algorithms.

Read a story by Raul Villamarin Rodriguez.

Graph-based Embeddings to Optimise Website Segmentation for Digital Ad Campaigns

By Dushyant Rai Tara 

About: Digital Advertising makes use of the web as a medium of reaching out to prospects, the place the advertisers determine web sites on the web which might be visited by their potential prospects and serve adverts by bidding on the advert slots accessible. The strategy of figuring out the place, and to whom an advertiser ought to serve an advert is known as a focusing on technique. In this speak, Dushyant Rai Tara will speak in regards to the two strategies for a similar — cookie-based focusing on and contextual focusing on. Due to rising privateness issues, this speak will spotlight among the thrilling instances of the identical. The speak will deal with the paper that proposes a data-driven strategy to create a brand new contextual technique from internet site visitors knowledge that segments web sites into teams for focusing on. This paper then compares completely different strategies mentioned utilizing a heuristic criterion to determine probably the most optimum technique for vector illustration.

Applied AI for IoT- enabled mobility platform

By Sanup Haridas

About: Sanup Haridas heads the analytics and knowledge science crew in Vogo, and on this session, Haridas will speak in regards to the automated dock-less scooter rental platform — Vogo, that goals to unravel the issue of last-mile connectivity with an on-demand service that allows customers to commute immediately. The platform has been created with a singular mix of IoT and Bluetooth expertise, and it will be attention-grabbing to know the way it works. The speak will additional deal with how the appliance of AI has created an influence at a number of ranges by making this platform extra clever, decreasing prices and bettering person expertise. This session goals to go in-depth on just a few of those AI-powered engines that mix deep-learning algorithms with IoT knowledge.

Stock Price Prediction Using Deep Learning Models

By Jaydip Sen 

About: Another paper presentation by Jaydip Sen, Stock Price Prediction Using Deep Learning Models, will deal with a collection of deep learning-based fashions for inventory worth prediction. Designing strong and correct predictive fashions for inventory worth prediction has been a crucial analysis space since a very long time the place the present propositions within the literature have demonstrated the correctly designed and optimised predictive fashions that may very precisely and reliably predict future values of inventory costs. The session will speak in regards to the proposition that features two regression fashions constructed on convolutional neural networks, and three long-and-short-term reminiscence network-based predictive fashions. Further, he’ll current detailed outcomes on the forecasting accuracies for all of the proposed fashions.

Know more about DLDC 2020 here.


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