Theory, Applications 2021-11 – Full Version

Description

The Complete Neural Networks: Theory, Applications Bootcamp is a training course on neural networks and deep learning systems based on the Python programming language and the PyTorch library, published by Udemy Academy. This training covers all theoretical and practical topics and has a completely practical and project-oriented approach.

What you will learn in The Complete Neural Networks Bootcamp: theory, applications

Theory and practice of artificial neural networks
Development of backpropagation algorithms
Activator functions in neural networks
Loss functions and their application in deep learning and neural networks
Various optimization techniques to reach the optimum point in neural networks
Gradient descent optimization algorithm
Stochastic gradient descent optimization algorithm
Moment Optimization Algorithm
Adaptive Gradient Method (AdaGrad)
RMSProp algorithm
Adaptive Impact Assessment Method (Adam)
Regularization techniques in neural networks
Knowledge of overfitting and techniques to prevent it
Random elimination technique to reduce overfitting in neural networks
Normalization techniques
Batch normalization
Layer normalization
PyTorch Deep Learning Framework
Installation and configuration of the PyTorch framework
Feed Forward Neural Network
Handwritten Cultivar Classification with Feeding Neural Network
Classification of Database Individuals Using Flow Neural Network
Practice and train an artificial neural network on a set of different datasets
Illustration and graphical representation of the process of learning and practicing neural networks
Nonlinear data separation
Design and development of neural networks without special libraries or frameworks and using only the Python programming language and the numpy library
Convolutional networks
Architectures and development patterns widely used in the development of projects based on deep learning
AlexNet Architecture
VGGNet Neural Network
Architecture of InceptionNet
Residual network
Deep Learning Object Detection
Transfer learning
Implement image recognition and classification techniques
Auto-encoders
Recurrent Neural Networks
Long Term Short Term Memory (LSTM)
Word embedding patterns
And …

Course specifications

Editor: Udemy
Instructors: Fawaz Sammani
French language
Level: Introductory to Advanced
Number of lessons: 306
Duration: 43h 47m

Course topics

The Complete Bootcamp on Neural Networks: Theory, Application

The complete bootcamp on neural networks: theory, applications, prerequisites

Some basic Python experience is preferred

Some high school math

Pictures

The Complete Bootcamp on Neural Networks: Theory, Application

The Complete Neural Networks Bootcamp: Theory, Application Intro Video

Installation guide

After ripping, watch with your favorite player.

english subtitle

Quality: 720p

Changes:

The 2021/11 version increased the number of lessons by 26 and the duration by 2 hours and 32 minutes compared to 2021/7.