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
Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.
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