Udemy – The Complete Neural Networks Bootcamp: Theory, Applications 2021
The Complete Neural Networks

Udemy – The Complete Neural Networks Bootcamp: Theory, Applications 2021

Description

The Complete Neural Networks Bootcamp: Theory, Applications is a training course on the development of neural networks and systems based on deep learning with the Python programming language and the PyTorch library, published by Udemy Academy. This training course includes 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 course:

  • The theory and working method of artificial neural networks
  • Development of backpropagation algorithms
  • Activator functions in neural networks
  • Loss functions and their application in deep learning and neural networks
  • Different 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 momentum estimation method (Adam)
  • Regularization techniques in neural networks
  • Getting to know the phenomenon of overfitting and its prevention techniques
  • Random elimination technique to reduce overfitting in neural networks
  • normalization techniques
  • batch normalization
  • Layer Normalization
  • PyTorch deep learning framework
  • Installing and configuring the PyTorch framework
  • Feed Forward Neural Network
  • Classification of handwritten figures with feedforward neural network
  • Classification of diabetics using feed-forward neural network
  • Training and training artificial neural networks on a set of different datasets
  • Illustration and graphic representation of learning and training process of neural networks
  • Non-linear data separation
  • Design and development of neural networks without special libraries and frameworks and only using Python programming language and NumPy library.
  • Convolutional Networks
  • Architectures and development patterns widely used in the development of projects based on deep learning
  • AlexNet architecture
  • VGGNet neural network
  • Inception Net architecture
  • Deep Residual Network
  • Object detection in deep learning
  • transfer learning
  • Implementation of image recognition and image classification techniques
  • Autoencoders
  • Recurrent Neural Networks
  • Long Short-Term Memory (LSTM)
  • Word Embedding models
  • And …

Course details

Publisher: Udemy
Instructor: Fawaz Sammani
Language: English
Education level: Introductory to advanced
Number of lessons: 306
Duration of training: 43 hours and 47 minutes

The Complete Neural Networks Course headings

The Complete Neural Networks

 

Prerequisites of The Complete Neural Networks Bootcamp: Theory, Applications course

Some basic Python experience is preferable

Some High School Mathematics

The Complete Neural Networks Course images

The Complete Neural Networks

Installation guide

After Extract, view it with your favorite Player.

English subtitle

Quality: 720p

Changes:

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

download link

Download part 1 – 3 GB
Download part 2-3 GB
Download part 3 – 3 GB
Download part 4 – 3 GB
Download part 5 – 615 MB
file password link
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