Udemy – The Complete Neural Networks Bootcamp: Theory, Applications 2021-11

Description

The Complete Neural Networks Bootcamp: Theory, Applications is a training course in neural network and deep learning systems based on the Python programming language and the PyTorch library, published by Udemy Academy. This training course 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 achieve 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
• Installing and configuring the PyTorch framework
• Feed Forward Neural Network
• Classification of handwritten cultivars with feed neural network
• Classification of database individuals using feed neural network
• Practicing and training artificial neural network on a set of different datasets
• Illustration and graphic representation of the process of learning and practicing neural networks
• Nonlinear 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 deep learning-based projects
• AlexNet Architecture
• VGGNet Neural Network
• Inception Net architecture
• Residual Network
• Object Detection in deep learning
• Transfer Learning
• Implement image recognition and image classification techniques
• Autoencoders
• Recurrent Neural Networks
• Short-term long-term memory (LSTM)
• Word Embedding models
• And …

Course specifications

Publisher: Udemy
Instructors: Fawaz Sammani
Language: English
Level: Introductory to Advanced
Number of Lessons: 306
Duration: 43h 47m

Course topics

The Complete Neural Networks Bootcamp: Theory, Applications Prerequisites

Some Basic Python Expreience is preferable

Some High School Mathematics

Pictures

The Complete Neural Networks Bootcamp: Theory, Applications Introduction Video

Installation guide

After Extract, watch with your favorite Player.

English subtitle

Quality: 720p

Changes:

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

Download Links

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 (s): www.abc.com

Size

12.61 GB