Deep Learning Tutorial

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 resultsArtificial 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 courseExplore building blocks of neural networksData representation, Tensor, Back propagationKerasDataset, Applying Keras to cases studies, over fitting / under fittingArtificial Neural Networks (ANN)Activation functionsLoss functionsGradient DescentOptimizerImage ProcessingConvnets (CNN), hands-on with CNNText and SequencesText data, Language ProcessingRecurrent Neural Network (RNN)LSTMBidirectional RNN Gradients and Back Propagation - MathematicsGradient Descent MathematicsImage Processing  / CV - AdvancedImage Data GeneratorImage Data Generator - Data AugmentationPre-trained network Functional APIIntro to Functional APIMulti Input Multi Output ModelImage SegmentationPoolingMax, Average, GlobalResNet ModelResnet overviewResnet concept modelResnet demoXceptionDepthwise Separable ConvolutionXception overview Xception concept modelXception demoVisualize Convnet filtersThe 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