Modern Deep Convolutional Neural Networks with PyTorch – Free Udemy Courses
Image Recognition with Convolutional Neural Networks. Advanced techniques for Deep Learning and Representation learning
What you’ll learn
Modern Deep Convolutional Neural Networks with PyTorch – Free Udemy Courses
- Convolutional Neural Networks
- Image Processing
- Advanced Deep Learning Techniques
- Regularization, Normalization
- Transfer Learning
Requirements
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Machine Learning
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Linear Regression and Classification
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Matrix Calculus, Probability
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Deep Learning basis: Multi perceptron, optimization
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Python, PyTorch
Description
Dear friend, welcome to the course “Modern Deep Convolutional Neural Networks”! I tried to do my best to share my practical experience in Deep Learning and Computer vision with you.
The course consists of 4 blocks:
- Introduction section, where I remind you, what are Linear layers, SGD, and how to train Deep Networks.
- Convolution section, where we discuss convolutions, their parameters, advantages, and disadvantages.
- Regularization and normalization section, where I share with you useful tips and tricks in Deep Learning.
- Fine-tuning, transfer learning, modern datasets, and architectures
If you don’t understand something, feel free to ask for equations. I will answer you directly or will make a video explanation.
Prerequisites:
- Matrix calculus, Linear Algebra, Probability theory, and Statistics
- Basics of Machine Learning: Regularization, Linear Regression, and Classification,
- Basics of Deep Learning: Linear layers, SGD, Multi-layer perceptron
- Python, Basics of PyTorch
Who this course is for:
- Who knows a bit about neural networks
- Who wants to enrich their Deep Learning and Image Processing knowledge
- Who wants to study advanced techniques and practices
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