What you'll learn:
- Convolutional Neural Networks
- Image Processing
- Advance Deep Learning Techniques
- Regularization, Normalization
- Transfer Learning
Dear friend, welcome to the course "Modern Deep Convolutional Neural Networks"! I tried to do my best in order 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 is Linear layers, SGD, and how to train Deep Networks.
Convolution section, where we discuss convolutions, it's 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 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
