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

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Price: 799$

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 CNNText and Sequences Text data, Language Processing Recurrent Neural Network (RNN)LSTMBidirectional 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 APIIntro to Functional APIMulti Input Multi Output Model The videos are concepts and hands-on implementation of topics