Deep Learning: Python,OpenCV,CNN,RNN,LST

Deep Learning with Python/ Keras
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
Requirements
Python language
Description
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

Keras
Dataset, Applying Keras to cases studies, over fitting / under fitting

Artificial Neural Networks (ANN)
Activation functions
Loss functions
Gradient Descent
Optimizer

Image Processing
Convnets (CNN), hands-on with CNN

Text and Sequences
Text data, Language Processing
Recurrent Neural Network (RNN)
LSTM
Bidirectional RNN
Gradients and Back Propagation - Mathematics
Gradient Descent
Mathematics

Image Processing  / CV - Advanced
Image Data Generator
Image Data Generator - Data Augmentation
Pre-trained network

Functional API
Intro to Functional API
Multi Input Multi Output Model
The videos are concepts and hands-on implementation of topics

Who this course is for:
Beginner Python developers, Data Science students, Students who have some exposure to Machine Learning

More Info