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:

1. Introduction section, where I remind you, what is Linear layers, SGD, and how to train Deep Networks.

2. Convolution section, where we discuss convolutions, it's parameters, advantages and disadvantages.

3. Regularization and normalization section, where I share with you useful tips and tricks in Deep Learning.

4. 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