50% Early bird discount for professionals before Dec 3rd 2016 & Special student rate available

One Day Workshop on
Deep Learning and Applications


Getting Started with Deep Learning (End-to-end Series Part 1)
Time:- 9:00 A.M. - 11:00 A.M.

Deep learning is giving machines near human levels of visual recognition capabilities and disrupting many applications by replacing hand-coded software with predictive models learned directly from data.  This lab introduces the machine learning workflow and provides hands-on experience with using deep neural networks (DNN) to solve a real-world image classification problem. You will walk through the process of data preparation, model definition, model training and troubleshooting, validation testing and strategies for improving model performance. You’ll also see the benefits of GPU acceleration in the model training process. On completion of this lab you will have the knowledge to use NVIDIA DIGITS to train a DNN on your own image classification dataset.


Time:- 11:00 a.m. - 11:15 A.M.
Deep Learning for Object Detection (End-to-end Series Part 2)
Time:- 11:15 A.M. - 1:15 P.M.

Building upon the foundational understanding of how deep learning is applied to image classification, this lab explores different approaches to the more challenging problem of detecting if an object of interest is present within an image and recognizing its precise location within the image.  Numerous approaches have been proposed for training deep neural networks for this task, each having pros and cons in relation to model training time, model accuracy and speed of detection during deployment. On completion of this lab, you will understand each approach and their relative merits. You’ll receive hands-on training applying cutting edge object detection networks trained using NVIDIA DIGITS on a challenging real-world dataset.

Prerequisites: Basic knowledge of data science and machine learning
Audience Level: Beginner

Enterprise & Corporate- Rs. 5000 (Early Bird Discount of 50% for registrations made before December 3 2016)
Students & Researchers - Rs. 750 (Uptil December 4 2016)

Time:- 1:15 P.M. - 2:30 P.M.


Deep Learning Network Deployment (End-to-end Series Part 3)
Time:- 2:30 P.M. - 4:30 P.M.

Deep learning software frameworks leverage GPU acceleration to train deep neural networks (DNNs). But what do you do with a DNN once you have trained it? The process of applying a trained DNN to new test data is often referred to as ‘inference’ or ‘deployment’. In this lab you will test three different approaches to deploying a trained DNN for inference. The first approach is to directly use inference functionality within a deep learning framework, in this case DIGITS and Caffe. The second approach is to integrate inference within a custom application by using a deep learning framework API, again using Caffe but this time through it’s Python API. The final approach is to use the NVIDIA TensorRT™ which will automatically create an optimized inference run-time from a trained Caffe model and network description file. You will learn about the role of batch size in inference performance as well as various optimizations that can be made in the inference process. You’ll also explore inference for a variety of different DNN architectures trained in other DLI labs.

Prerequisites: C++ programming experience
Audience Level: Intermediate

Enterprise & Corporate - Rs. 3000 (Early Bird Discount of 50% for registrations made before December 3 2016)
Students & Researchers - Rs. 500 (Uptil December 4 2016)

  1. If you are taking any of these Deep Learning and Applications, online prepayment is a MUST to book your seat, there WILL NOT be onsite registration & payment.
  2. Students must present their college ID
  3. Any query Please contact Rohit Biddappa at +91-98450-16525
  4. If you are facing any difficulty in making payment please contact parvez.raima@shobizexperience.com
  5. You MUST bring your own laptop. Laptops WILL NOT be provided at the class. Minimum laptop specification is as follows:
    Feature Minimum Requirement
    CPU Core i3 / i5 @ 2 GHz
    Memory 8 GB RAM
    Browser IE 11, Chrome or Firefox