Deep learning is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised. The objectives of this course are as follows:
➼ To provide basic technical skills for Deep Learning
➼ To learn and implement Deep Learning algorithms
➼ To introduce important application areas of Deep Learning such as Computer Vision, Natural Language Processing, Speech Analysis, Text Processing, Human Activity Analysis, etc.
➼ To impart necessary research and technical writing skills
➼ To implement end-to-end hands-on Deep Learning projects
➼ Strong theoretical foundation of the fundamental concepts by professors & researchers from IITs, IIITs, NITs, and other institutes of international repute.
➼ Hands-on coding practice and project development in guidance of Industry experts, Research Scholars and Top-level programmers.
➼ Regular interaction, assignments, quizzes, and evaluations.
➼ Networking opportunity with participants reputed institutes and industries.
➼ Opportunity to work on end-to-end projects and assistance in resume, portfolio building.
➼ A chance to publish research papers and participate in international conferences.
➼ Continuous assistance during the course and after that, lifetime access to the learning material, including Slides, software, codes, and other resources.
➼ Historical perspective and evolution of Deep Learning.
➼ Introduction to deep Generative and Discriminative models.
➼ Regularization: Bias Variance Trade off, L2 regularization, Dataset augmentation, Early stopping.
➼ Recent Trends in Deep Learning Architectures, Residual Networks, Fully connected CNN etc .
➼Training and optimizing deep networks.
➼Transfer learning for Computer Vision.
➼ Recurrent Neural Network (RNN) and Sequence to Sequence (Seq2Seq) learning.
➼ Fundamentals of Python Programming.
➼ Introduction to Computer Vision and its applications.
➼ Pre-trained language models (Transformers, BERT, GPT, etc).
By the end of this certification course, the participants should be able to:
➼ Possess essential programming skills for Deep Learning
➼ Understand the basic know-how about Deep Learning
➼ Understand a wide variety of Deep Learning & Deep Learning algorithms
➼ Understand how to pre-process the data and evaluate the models generated from data
➼ Apply these algorithms to real-life problems, communicate their results in technical reports
Moreover, they will also acquire a course competition certificate on completing the assigned project.
Sample Course Certificate: Coming soon...