Modern Deep Convolutional Neural Networks with 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



Requirements



    Machine Learning

    Linear Regression and Classification

    Matrix Calculus, Probability

    Deep Learning basis: Multi perceptron, optimization

    Python, PyTorch



Last updated 5/2019