$20 | Created by Denis Volkhonskiy | Last Updated 5/2019
Duration: 2 hours | Video: h264, 1280×720 | 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
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