What you'll learn:
- The students will be able to understand what is Deep Learning. How to create various model and solve the problems hands-on using Keras.
- As part of various hands-on activities, students will learn how to apply Deep Learning to real world problems
Deep Learning is part of a broader family of machine learning methods based on artificial neural networks.
Deep-learning architectures such as deep neural networks, recurrent neural networks, convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced good results
Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains.
Keras is the most used deep learning framework. Keras follows best practices for reducing cognitive load: it offers APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages.
Following topics are covered as part of the course
Explore building blocks of neural networks
Data representation, Tensor, Back propagation
Dataset, Applying Keras to cases studies, over fitting / under fitting
Artificial Neural Networks (ANN)
Convnets (CNN), hands-on with CNN
Text and Sequences
Text data, Language Processing
Recurrent Neural Network (RNN)
Gradients and Back Propagation - Mathematics
Image Processing / CV - Advanced
Image Data Generator
Image Data Generator - Data Augmentation
Intro to Functional API
Multi Input Multi Output Model
The videos are concepts and hands-on implementation of topics