Deep learning can be considered as a subset of machine learning which improves on its own by examining computer algorithms. While machine learning uses simpler concepts, deep learning works with deep neural networks, which are designed to imitate how humans think and learn. The goal of this course is to give learners a basic understanding of modern neural networks and their applications in computer vision and natural language understanding.
- Comprehend gradient descent, stochastic gradient descent, regularization, overfitting
- Acquire overview and basic knowledge about deep neural network
- Introducing Deep Learning in Computer Vision.
- Be able to apply CNN and transfer learning to computer vision tasks
- Acquire basic knowledge about unsupervised representation learning in deep learning – Use deep learning for string data
- Acquire knowledge of RNN, LSTM, GRU, Backpropagation and practice
Module 1 – Simple Neural Network 0/0
Module 2 – Deep Learning in Computer Vision 0/0
Assignment 1: Build a CNN model that classifies images into categories 0/0
Module 3 – Unsupervised Representation Learning 0/0
Module 4 – Deep Learning in Natural Language Processing 0/0
Assignment 2: Develop models that identify and flag toxic and misleading content on a website using deep learning 0/0