Deep Learning And Artificial Intelligence

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• Completely Hands-on & Project Oriented
• Experiments on all Major Machine Learning & Deep Learning Concepts, Natural Language Processing PLUS Reinforcement Learning
• You get all tools and data on two Virtual Machines
• Yes, We start from Very Basics—No worry on that count

About the Program

Concepts of Deep Learning and AI are easy to learn and implement—Technology is no longer restricted to the realm of experts. The program assumes no prior Machine Learning (ML) background on your part. We initiate you in programming (with python) and then go on to apply deep learning techniques on tabular data, images, sensor data and sequential (time-series) data. We work with Natural Languages both using traditional ML tools as also Deep Learning tools. We cover reinforcement learning in sufficient depth.

Program Objectives

Deep Learning has been a subject of study since the inception of neural networks. With advancement in technology, especially, Graphical Processing Units (GPUs), the demand for knowledgeable and skilled personnel in this area has received a fillip. Applications of deep learning range from Computer Vision to Speech recognition & translation to marketing and to drug discovery. It is one of the fastest growing fields of Artificial Intelligence. The objectives of the present program are:

• To work on important technologies of Machine Learning, artificial intelligence: Deep learning, Natural Language Processing and Reinforcement Learning
• Practical implementation of the techniques like recurrent neural network and transfer learning on real world applications
• Applications of Deep Learning techniques to problems from a range of disciplines

Download the brochure for detailed syllabus.

FORE School of Management New Delhi along with Tech Mahindra Growth Factories Ltd, will prepare the future Big Data Engineers to fulfill the market demand.

Pedagogy

We are keenly aware that our participants come from varied backgrounds—both college wise and basic-education-wise. We strongly believe that a course in data analytics can only be practice-based rather than theory based. We also believe that a practice based course requires constant interaction with the teacher during lecture hours in real time. As it is an online course, the teaching pedagogy is like this: First the theory part is conceptually explained without getting into mathematics and then a project is undertaken to implement the techniques. Datasets for implementation are made available in advance and so also a copy of code (or hints on it) that we need to execute. The code is numbered and copiously commented so that long after the lecture has finished, students can go back through the code/comments and refresh their knowledge. During the lecture, we execute this code (or prompt students to fill in the gaps), line-by-line and explain the steps. At his end, the student executes the required code on his laptop. Consequently, results are available at our end as also with the Students immediately. In short, both the teacher and students are working on their respective laptops simultaneously; students solve their problems and ask any questions to clarify. The whole experience is just as if everyone is sitting in a laboratory and working together. Our e-learning platform has a wealth of material and articles reflecting latest developments in this field; it is frequently updated. Students are assured of continued access to e-learning site even after the program has finished.

Eligibility

The course is especially designed for executives from corporate, students, faculty, and budding data-scientists and research scholars who are interested in understanding the concepts and practical applications of deep learning and artificial intelligence. A simple programming background would be preferable.

Virtual Machines on Deep Learning

Deep Learning and AI employ an array of technologies. To concentrate on learning and to make it hassle free and smooth, we will be giving to each participant two Virtual Machines with all tools and environment to work on deep learning problems and on Reinforcement Learning. The VMs are to be downloaded from our e-learning platform. These are installable on any laptop. These are released under highly liberal GPL licensing.

Program Dates:

From Last week of June’2020, Thursday and Saturday, 3 hours a day.

Total Program hours: 80.

Participation Fee

Part 1: Complementary for first 20 Registrations

Total session hours: 30 hours. Full details under each may be seen under Topics heading.

• A. Basics of Python and Data Exploration
  1. Python, Numpy and Pandas
  2. Data Visualization
• B. Machine Learning
  1. Clustering techniques
  2. Classification Techniques and Model Optimization

(This last topic is quite detailed even though shown under one heading).

Part 2: Nominal Fees, only for those who have completed part 1 successfully

Total Session hours: 50 hours

• C. Deep Learning
  1. Deep Learning Basics
  2. Neural Networks
  3. Autoencoders
  4. Convolution Neural Networks
  5. Transfer Learning
  6. Recurrent Neural Networks
  7. Natural Language Processing
  8. Data Generation
• D. Reinforcement Learning
  1. Reinforcement Learning--Basics
  2. Q-learning and Deep-Q Learning

Program Faculty:

Prof. Ashok Kumar Harnal
Prof. Lalit K Jiwani