Image Recognition with Convolutional Neural Networks. Advanced techniques for Deep Learning and Representation learning
What you will learn
Convolutional Neural Networks
Image Processing
Advance Deep Learning Techniques
Regularization, Normalization
Transfer Learning
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
English
language
Content
Introduction
Introduction
Computer Vision Problems
Linear Layer and Classification Pipeline
Loss functions and Softmax
Stochastic Gradient Descend
PRACTICE #1: Data loading
PRACTICE #2: Linear Classifier in PyTorch (part 1)
PRACTICE #3: Linear Classifier in PyTorch (part 2)
PRACTICE #4: Multi-layer perceptron
Convolutional Neural Networks
What is image
Motivation to Convolutions
Convolution operation
Parameters of the convolution
Non-linear function
Max Pooling and Average Pooling
Building deep convolutional network
PRACTICE #5: Convolutional Neural Network
Regularization and Normalization
Overfitting. L2 regularization
DropOut regularization. DropConnect regularization
DropBlock regularization
Early Stopping regularization
Batch Normalization
Improving the quality
Data Augmentation
Existing datasets
Modern Architectures
Transfer Learning
Boat Recognition Project
Data Loading
Data Augmentation
Transfer Learning: ResNet-18