Deep Learning by Aegis & NVIDIA

Application fee : 0 USD


Certification Body: NVIDIA and Aegis School of Data Science
Location: On-campus (India, Mumbai, Pune, Bangalore )
Type: Certificate course
Coordinator: Ritin Joshi
Language: English
Course fee: 0 USD
GST: 18%
Total course fee: 0 USD
Application deadline: Aug 10, 2017
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Course Details

Aegis School of Data Science & Telecommunication and NVIDIA announced a strategic partnership at the Data Science Congress to conduct imparting training on Artificial Intelligence (AI) for Corporates and Individuals. The purpose of this partnership is to build a large pool of skilled manpower in the Deep Learning space to fill the skill gaps.


This initiative will feature several courses for DL, ML and AI, facilitated by Aegis School of Data Science and powered by NVIDIA Deep Learning Institute. The students will also be given access to NVIDIA’s proprietary technologies, software, labs, subject matter experts, engineers, and data scientists. This will help them develop skills for the future as well as today’s industry requirement. 

Vishal Dhupar, Managing Director, South Asia, Nvidia said, “We stand on the brink of a technological revolution that will fundamentally alter the way we live, work, and relate to one another. There is a huge need for developers who not only understand AI, but know how to apply it. As an AI compute company, our cutting-edge technological innovation finds new ways of training the developers as well as the teaching and learning experience. Through this partnership, we aspire to create and develop skills that will help solve some of the world’s most challenging problems.”

Aegis and NVIDIA provides practical training on the use of Artificial Intelligence, Deep Learning and Machine Learning tools & technology, training developers, data scientists, researchers, Analytics, Big Data, ML & AI professionals and existing Aegis students. The training covers fundamental tenets of deep learning such as using AI for object detection or image classification, applying this to determine the best approach to cancer treatment; Natural language processing, speech recognition etc.

The NVIDIA and Aegis offers hands-on training for developers, data scientists, and researchers looking to solve the world’s most challenging problems with deep learning.

Through self-paced online labs and instructor-led workshops, DLI provides training on the latest techniques for designing, training, and deploying neural networks across a variety of application domains. Students will explore widely used open-source frameworks as well as NVIDIA’s latest GPU-accelerated deep learning platforms.

Mission: Helping the world to solve the most challenging problems using AI and deep learning.

We help developers, data scientists and engineers to get started in training, optimizing, and deploying neural networks to solve real-world problems in diverse disciplines such as self-driving cars, healthcare, consumer services and robotics.

What is Deep learning?

Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is the study of artificial neural networks and related machine learning algorithms that contain more than one hidden layer.

Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. 

Thanks to the era of NVIDIA’s GPU computing, training deep neural networks is more efficient than ever in terms of both time and resource cost. The result is an AI boom that has given machines the ability to perceive — and understand — the world around us in ways that mimic, and even surpass, our own.

Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Some representations are better than others at simplifying the learning task e.g. face recognition or facial expression recognition. One of the promises of deep learning is replacing handcrafted features with efficient algorithms for unsupervised or semi-supervised feature learning and hierarchical feature extraction.

Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data. Some of the representations are inspired by advances in neuroscience and are loosely based on interpretation of information processing and communication patterns in a nervous system, such as neural coding which attempts to define a relationship between various stimuli and associated neuronal responses in the brain.

Various deep learning architectures such as deep neural networks, convolutional deep neural networks, deep belief networks and recurrent neural networks have been applied to fields like computer vision, automatic speech recognition, natural language processing, audio recognition and bioinformatics where they have been shown to produce state-of-the-art results on various tasks.

Difficulty level: Intermediate


  • Basic knowledge on Machine Learning
  • Python programming

Hardware and OS requirements:

  • 40 GB free disk space
  • 4 GB (8 GB preferred)
  • Any recent Intel or AMD multi-core processor should be sufficient.
  • Any recent NVIDIA GPU (preferred)
  • Windows 7 (64-bit) Operating System or later
  • High speed Internet connectivity

Software requirement: Any browser application IE or Chrome or Firefox

Objective: To help participants, for solving real world problem by using Deep Learning concepts


  • Introduction to Neural Networks
  • Forward and backward propagation
  • Introduction to Deep Learning
  • Convolution Neural Network
  • Recurrent Neural Network
  • Tools : Tensorflow, Keras, NVIDIA DIGITS
  • Applications : Face Recognition, Face Emotion Detection, Object Detection, Image Segmentation, Text Classification, Sentiment Analysis, etc