What you'll learn

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