Description
The Complete Neural Networks Bootcamp: Theory, Applications is a training course for 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 post-propagation algorithms (Backpropagation)
- 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 network 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: Yudmi
teacher: Fawaz Sammani
English language
Training level: introductory to advanced
Number of courses: 306
Training duration: 43 hours and 47 minutes
Course headings
Prerequisites of The Complete Neural Networks Bootcamp: Theory, Applications course
Some basic Python experience is preferable
Some High School Mathematics
Course images
Introduction video of The Complete Neural Networks Bootcamp: Theory, Applications course
Installation guide
After Extract, view 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
Password file(s): www.downloadly.ir
Size
12.61 GB
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