Description
The Complete Neural Networks Bootcamp: Theory, Applications is a training course on the development of neural networks and deep learning systems based on the Python programming language and the PyTorch library, published by Yodemi 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 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
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- Classification of handwritten cultivars with feed neural network
- Classification of diabetic individuals using feed neural network
- Practicing and training an 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: Udemi
Instructor: Fawaz Sammani
Language: English
Education Level: Basic to Advanced
Number of Courses: 280
Training Duration: 41 hours and 15 minutes
Course topics on 2021/11
Prerequisites for The Complete Neural Networks Bootcamp: Theory, Applications
Some Basic Python Experience is preferable
Some High School Mathematics
Course pictures
Installation guide
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English subtitle
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Download Part 2 – 3 GB
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Download section 5 – 3 GB
Download Section 6 – 1.3 GB
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