$20 | Created by Denis Volkhonskiy | Last Updated 5/2019

Duration: 2 hours | Video: h264, 1280x720 | Audio: AAC, 44 KHz, 2 Ch | 687 MB

Genre: eLearning | Language: English + Sub | 29 Lectures

Image Recognition with Convolutional Neural Networks. Advanced techniques for Deep Learning and Representation learning

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