Deep Learning Python,Opencv,Cnn,Rnn,Lst

Free Download 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 ModelThe videos are concepts and hands-on implementation of topics
Overview
Section 1: Introduction to Deep Learning
Lecture 1 Course Contents
Lecture 2 Introduction to Deep Learning
Lecture 3 Tensors
Lecture 4 Tensor Operations
Lecture 5 Keras - Overview
Section 2: Python & Numpy
Lecture 6 Python contents
Lecture 7 Development Environment and Installation
Lecture 8 Variables and Numbers in Python (with Practical)
Lecture 9 Strings in Python (with Practical)
Lecture 10 Lists in Python (with Practical)
Lecture 11 Conditional Execution (with Practical)
Lecture 12 Loops (with Practical)
Lecture 13 Functions (with Practical)
Lecture 14 Dictionaries in Python (with Practical)
Lecture 15 Tuples in Python (with Practical)
Lecture 16 Exceptions and it's Handling
Lecture 17 Exceptions and it's Handling (with Practical)
Lecture 18 Iterators (with Strings, List, Dictionary, Tuple)
Lecture 19 Iterators Practical (with Strings, List, Dictionary, Tuple)
Lecture 20 File Support (with Practical) - part 1
Lecture 21 File Support (with Practical) - part 2
Lecture 22 JSON support (with Practical)
Lecture 23 NumPy with Practical (part 1)
Lecture 24 NumPy with Practical (part 2)
Section 3: Artificial Neural Network (ANN)
Lecture 25 ANN, Backpropagation
Lecture 26 ANN- Optimizer and Activation Functions
Lecture 27 Activation function - Demo
Lecture 28 ANN- Loss Functions
Lecture 29 Prerequisite - Dev Environment
Lecture 30 Keras- Getting Started
Lecture 31 Image Classification- Hands-on
Section 4: Handling Images - CNN
Lecture 32 Convolution Neural Network- Image Processing / Computer Vision
Lecture 33 CNN- Hands-on (part1)
Lecture 34 CNN- Hands-on (part2)
Section 5: Handling Sequence Data (/ Time Series Data)
Lecture 35 Handling Text Sequences
Lecture 36 Hands-on with Text Sequences (/ Word Embeddings)
Lecture 37 Recurrent Neural Network (RNN)
Lecture 38 Hands-on with RNN
Lecture 39 LSTM, Bidirectional RNNs
Lecture 40 Hands-on with LSTM
Lecture 41 Hands-on with Bidirectional RNN
Section 6: Fitment - Design Issues
Lecture 42 Over fitting and Under fitting
Section 7: Gradients and Back Propagation - Mathematics
Lecture 43 Gradient and Back propagation (part1)
Lecture 44 Gradient and Back propagation (part2)
Section 8: Image Processing/ Computer Vision - Advanced
Lecture 45 Image Processing/CV - Keras Image Data Generator
Lecture 46 Image Processing/CV - Data Augmentation
Lecture 47 Pre-Trained Network for Image Processing / CV
Lecture 48 Pre-Trained Network (with Practical)
Lecture 49 Improvements in using Pre-Trained network (with Practical)
Section 9: Introduction to Functional API
Lecture 50 Intro to Functional API (with Practical)
Lecture 51 Multi Input Multi Output model (with Practical)
Beginner Python developers, Data Science students, Students who have some exposure to Machine Learning


Homepage

https://www.udemy.com/course/deep-learning-smt/