Certification Course on Deep Learning

About the Course

Deep Learning course builds a solid foundation by covering the most popular and widely used Deep Learning technologies and its applications including Computer vision, Artificial neural networks, convolutional neural networks for the students who are interested in machine learning, Artificial Intelligence and who also have knowledge in Python programming. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like Mobiles, tablets, TVs, and hands-free speakers.

Deep learning differs from traditional machine learning techniques (like classification, clustering etc.) in a way that they can automatically learn representations from data such as images, video or text, without introducing hand-coded rules or human domain knowledge. Deep learning changes how you think about representing the problem that you’re solving with analytics. It moves from telling the computer how to solve a problem to training the computer to solve the problem itself.

Course Objective:

  • To understand the concept of artificial neural networks, convolutional neural networks, and recurrent neural networks.
  • To learn the foundations of Deep Learning, including how to build neural networks and machine learning projects.
  • To learn deep learning methodologies to process not only image based datasets but also raw text, numbers etc.

Course Outcome:

Upon successful completion of this course, students will be able to

  1. Identify the deep learning algorithms which are more appropriate for various types of learning tasks in various domains.
  2. Develop ability to independently solve business problems using deep learning techniques.
  3. Apply such deep learning mechanisms to various learning and real world problems.

Course Duration: 3 Months

Course Content

S.NoCourse Content
1Introduction to Deep learning
2Neural Networks Basics
3Shallow neural networks
4Deep Neural Networks
5Practical aspects of Deep Learning Optimization algorithms
6Hyperparameter tuning
7Batch Normalization and Programming Frameworks
8Machine Learning Strategies
9Foundations of Convolutional Neural Networks
10Deep Convolutional models: case studies Object detection
11Special applications: Face recognition & Neural style transfer
12Recurrent Neural Networks
13Natural Language Processing & Word Embeddings
14Sequence models & Attention mechanism