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Live Webinar: Essentials of Deep Learning

10 Registered Feb, 2019 11:00 AM 2 Hrs

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About the Instructor:

He is an OPTIMISTIK INFOSYSTEMS Accredited Instructor with, more than 10 years of experience in Delivering Training to Corporates. In Classroom an Online mode. He is a Data scientist, Statistical Modeler Analyst & Big Data trainer in the field of statistical analysis (with management & operational prospective) using technological driven tools & technics including Data Science. AI, BI, Data Analytics, Text mining, NLP, Machine & Deep learning, Cloud computing, IOT, Social media mining, Visual analytics, Big-data set-up like Hadoop, Mahout, Spark etc.

Trainer for Analytic & Statistical modelling on Predictive & Prescriptive Analysis , Time-Series & Demand forecasting, Regression Analysis (Linear to Neural Network to Market Basket analysis), Classification(supervised) & Clustering(unsupervised), Churn,Conjoint & Link Analysis, Market Mix & Market Segmentation, Text mining, Multi-Dimensional scaling, Index creation with tools like Excel, SAS (EG & EM),R,R-Studio, Rev R, SPSS, Python, Rapid Miner, MS azure ML studio, Big-Data, Hadoop, Spark, Mahout, AIML, Cloud Computing.

Machine & Deep learning, NLP & Text mining implementer with model deployment using web service. Cloud Computing consultant for SAAS, PAAS, IAAS, NAAS Public, Private & Hybrid cloud models.

Previously Asst. Professor for Decision Science Area. Visiting faculty for Distributing & cloud computing,Research Methodology, Operational research management, Project management, DW/Data & Advance data mining using SAS, SPSS, R,Python & ML azure ML studio/Business Intelligence, E-Services in Digital Marketing, Network Economy,Web & Google Analytic, Website analysis, Social media extraction & mining, NLP & Text mining, Building dashboard applications using R, Structural equation modelling using AMOS & LISREL, Face-book & twitter mining, Semantic search(web 3.0). Conducted workshop in corporate along with global certification programs in SAS(Base , Advance, SQL & Macros), SPSS & R, Rapid Miner, Social media impact & leverage,NLP, NLG, Text Mining,Open Text,Cloud Computing, Big-Data, Hadoop, Spark, Data Mining SMAC (Social media, Mobile, Analytic & Cloud computing).



What is Deep Learning ?

Machine learning (ML) is a category of algorithm that allows software applications to become more accurate in predicting outcomes without being explicitly programmed. The basic premise of machine learning is to build algorithms that can receive input data and use statistical analysis to predict an output while updating outputs as new data becomes available.


To understand how Deep Learning solves problems that which machine learning cannot.

Event Schedule:

Session 1 - Introduction (60 mins):

  1. Deep Learning: A revolution in Artificial Intelligence
  2. Limitations of Machine Learning
  3. What is Deep Learning?
  4. Advantage of Deep Learning over Machine learning
  5. TheMath behind Deep Learning: Linear Algebra like Scalars, Vectors, Matrices, Tensors, Hyperplanes.


  1. How to select best DL algorithm/solution for your problem featuring more than two dimensional data.
  2. Infrastructural challenges along with pros & cons for your customized DL solution.
  3. GPU understanding to ease DL solution.

Session 2: Case Study( 60 mins)

  • Scope- Use case on Deep Learning in FMCG ( Content Moderation).

Other Events:

Target Audience:

Must have understanding of Machine Learning / Must have attended OI webinar on ‘Essentials of Machine Learning'

Delivery Mode:

  • Live Online
  • Instructor Led

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