Theory, applications 2021-11 – Download

Description

The Complete Neural Networks Bootcamp: Theory, Applications is training in 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 Optimal Point in Neural Networks
gradient descent optimization algorithm
Stochastic gradient descent optimization algorithm
Momentum Optimization Algorithm
Adaptive gradient method (AdaGrad)
RMSProp algorithm
Adaptive impact estimation method (Adam)
Regularization techniques in neural networks
Familiarity with the phenomenon 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
Classification of handwritten cultivars with food neural network
Classification of individuals from database using feed 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
Non-linear 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 models widely used in the development of deep learning based projects.
AlexNet Architecture
VGGNet neural network
InceptionNet Architecture
Residual network
Object detection in deep learning
Transfer learning
Implement image recognition and image classification techniques
Autoencoders
Recurrent Neural Networks
Short-term and long-term memory (LSTM)
Word embedding models
And…

Course Specifications

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

course topics

The complete neural networks bootcamp: theory, application

The Complete Neural Networks Bootcamp: Theory, Prerequisites for Applications

Basic Python experience is preferred

Some mathematics in high school

Pictures

The complete neural networks bootcamp: theory, application

The complete bootcamp on neural networks: theory, introductory video to applications

installation guide

After the clip, watch with your favorite reader.

english subtitles

Quality: 720p

Changes:

Version 2021/11 increased in number by 26 lessons and duration by 2 hours and 32 minutes compared to 2021/7.