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Modern Deep Convolutional Neural Networks with PyTorch

Offered By: Udemy

Tags

Convolutional Neural Networks (CNN) Courses Linear Algebra Courses PyTorch Courses Image Recognition Courses Transfer Learning Courses Image Processing Courses Normalization Courses Regularization Courses

Course Description

Overview

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

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


Taught by

Denis Volkhonskiy

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