Deep Learning PythonOpenCVCNNRNNLST

Deep Learning with Python/ Keras
Deep Learning PythonOpenCVCNNRNNLST
File Size :
6.53 GB
Total length :
15h 1m



Shrirang Korde


Last update




Deep Learning PythonOpenCVCNNRNNLST

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 PythonOpenCVCNNRNNLST


Python language


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


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

Course Information:

Udemy | English | 15h 1m | 6.53 GB
Created by: Shrirang Korde

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