Last Date For Registration
30th September 2019
Learn Deep Learning from IIT Madras Professors who are subject matter experts in the field.
Learn to build your own back propagation model using Deep Learning Library - PyTorch and solve real world problems using cutting edge techniques.
Go from Zero to Hero in Deep Learning.
- Very high quality content with Hands-on Google Colab
- Basic Mathematics and Python are the only prerequisites for taking up the course
- Kaggle contests will be conducted with your course-mates
- Solve real world problems and build solutions(Vernacular Language Signboard Translation) as a final project for the course
- Dedicated Forum Support from the Instructors
- Certificate from GUVI after successful completion
- Get access to our community of over 5000 DL students and enthusiasts here: https://network.onefourthlabs.com
Mitesh M. Khapra is an Assistant Professor in the Department of Computer Science and Engineering at IIT Madras. He researches in the areas of Deep Learning, Multimodal Multilingual Processing, Dialog systems and Question Answering. He holds masters and Ph.D degrees from IIT Bombay. He has worked for over 4.5 years at IBM Research and published over 25 papers. He was a recipient of IBM PhD Fellowship and the Microsoft Rising Star Award. He is also a recipient of the Google Faculty Research Award, 2018.
Pratyush is an Assistant Professor at the Department of Computer Science and Engineering at IIT Madras since April 2018. He received his Bachelors and Masters of Technology in Electrical Engineering from IIT Bombay in 2009. He then completed his PhD in Computer Engineering from ETH Zurich in 2014. He then spent over 2.5 years at IBM Research, Bangalore and a few months consulting for machine learning and startups. His current research focus is on hardware-software co-design of deep learning systems. He has authored over 35 research papers and has applied for over 20 patents.
- A working knowledge of Python.
- Time commitment of 4 hours a week.
- Laptop/desktop with internet access.
- Data-Driven Everything - Deep Learning can be applied to ANY domain at some capacity, so long as there are volumes of data generated to train the models.
- Highly Accessible - Advancements in software, hardware and the open-source community of Deep Learning Practitioners have made DL the most accessible it’s ever been since its inception.
- Math and Python - As this point is titled, high-school math and basic knowledge of Python syntax is all you need to begin your journey as a deep learning practitioner.
- 0 to 60 in 0.5 - With around 5-8 hours of study per week and around 6 months of time, learners can progress rapidly from novice to intermediate/adept levels.
"This course is the best as it focuses both on the theory and hands on.This course introduced me to Kaggle competitions and I got addicted to it.I feel more confident that I can contribute to real world projects involving deep learning after taking this course."
- Praveen R
"Learned a lot from this course. Before starting this course, I have no knowledge of deep learning but after learning this course I am pretty confident."
- Vudata Rohit
"This course can turn you into a deep learning enthusiast if you have the urge to learn something new (even if you don't know anything about deep learning at all)."
- Kanumuri Sri Naga Sai Ajith