Saurabh Jha
Deep Learning Architect
Dell
location_on India
Member since 3 years
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Saurabh Jha
Specialises In
Currently authoring a book on "Deep Learning with PyTorch" with Apress. Has overall around 12 years of experience working in Data Science. Currently leads the Deep Learning team at Dell IT working out of CFO office(Tom Sweet) on mandate to transform Cash Application towards making back office Finance frictionless- Automating the data entry through Deep Learning. Set up a deep learning team in Dell from scartch. Previously head of Advanced Analytics for Infocepts build the analytics team from scratch. He has vast experience working with terabyte scale data and has been developing solutions in AI space. Have worked for consulting companies in India (PwC &vTeradata Professional Services) delivering data science solutions. Has strong experience in building data science practice from scratch and has diverse skills ranging from data engineering, bridge between business and technology, Information Modeling, data science. Has experience working with Banking and Insurance, FMCG & Retail. His research interests include Deep Learning, Computer Vision, Information Retrieval. . Has earlier presented in World Machine Learning summit India organized by 1.21GWs, December 2018 on Semantic Segmentation and a worksop on Medical Imaging and gave talk at Microstrategy world on Advanced Analytics in 2016.
Achievements -
1. Was selected in Microsoft Academic Project Program(MSAPP) by MSDN for my work on HR Anallytics for SME IT, 2006.
2. Employee Excellence Award - TCS, 2009
3. Head of Asia for Citi Genesis Project- Recognition Award for Data Analysis, 2011
4. Awarded from Dell CFO(Tom Sweet) on automating the Data Entry work using AI, 2018.
5. Build AI practice by getting funding approved on the basis on the AI work we did for them.
6. Designed largest banking data warehouse for Citi- Genesis Program for Wholesale and Retail Banking products, 2011.
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Hands on Deep Learning for Computer Vision
Saurabh JhaDeep Learning ArchitectDellRohan Shravan
Usha RengarajuPrincipal Data ScientistMysuru Consulting Groupschedule 3 years ago
480 Mins
Workshop
Intermediate
Computer Vision has lots of applications including medical imaging, autonomous
vehicles, industrial inspection and augmented reality. Use of Deep Learning for
computer Vision can be categorized into multiple categories for both images and
videos – Classification, detection, segmentation & generation.
Having worked in Deep Learning with a focus on Computer Vision have come
across various challenges and learned best practices over a period
experimenting with cutting edge ideas. This workshop is for Data Scientists &
Computer Vision Engineers whose focus is deep learning. We will cover state of
the art architectures for Image Classification, Segmentation and practical tips &
tricks to train a deep neural network models. It will be hands on session where
every concepts will be introduced through python code and our choice of deep
learning framework will be PyTorch v1.0 and Keras.Given we have only 8 hours, we will cover the most important fundamentals,
current techniques and avoid anything which is obsolete or not being used by
state-of-art algorithms. We will directly start with building the intuition for
Convolutional Neural Networks, and focus on core architectural problems. We
will try and answer some of the hard questions like how many layers must be
there in a network, how many kernels should we add. We will look at the
architectural journey of some of the best papers and discover what each brought
into the field of Vision AI, making today’s best networks possible. We will cover 9
different kinds of Convolutions which will cover a spectrum of problems like
running DNNs on constrained hardware, super-resolution, image segmentation,
etc. The concepts would be good enough for all of us to move to harder problems
like segmentation or super-resolution later, but we will focus on object
recognition, followed by object detections. We will build our networks step by
step, learning how optimizations techniques actually improve our networks and
exactly when should we introduce them. We hope the leave you in confidence
which will help you read research papers like your second nature. Given we have
8 hours, and we want the sessions to be productive, we will instead of introducingall the problems and solutions, focus on the fundamentals of modern deep neural
networks. -
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