# Modern Deep Convolutional Neural Networks with PyTorch

Modern Deep Convolutional Neural Networks with PyTorch

Modern Deep Convolutional Neural Networks with PyTorch 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

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