Convolutional Neural Networks and Image Processing II

• basic knowledge of programing in Python
• high school level of mathematics
• Basics of machine learning on the level of our course Introduction to machine Learning
• Knowledge on the level of our basic Convolutional Networks and Image Processing

• Architectures of neural networks for image processing (convolution, deconvolution, pooling, residual)
• Big neural networks for image processing (VGG 16 and ResNet)
• Image Segmentation (U-net, Object detection)
• Practical example of image segmentation
• Generative Adversarial Networks
• Practical example of image generation
• Superresolution (Upsampling, practical example of using GANs for superresolution)
• Practical project on housing price prediction using the combination of tabular and image data

Previous courses

Python I - introduction to programming [PYTHON_INTRO]

Introduction to Machine Learning [MLC_INTRO]

Convolutional Neural Networks and Image Processing [MLC_CNIP]