Convolutional Neural Networks

Image Processing

Advance Deep Learning Techniques

Regularization, Normalization

Transfer Learning

## Requirements

- Machine Learning
- Linear Regression and Classification
- Matrix Calculus, Probability
- Deep Learning basis: Multi perceptron, optimization
- Python, PyTorch

## Description

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

## 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