Deep learning
This course covers a comprehensive curriculum on deep learning and machine learning, including basic data representations, algorithms, coding in Python with scikit-learn, parallel programming for GPUs and multi-core CPUs, neural networks, image recognition, convolutional neural networks, adversarial attacks, and more. Students will also engage in assignments, a mid-term, and a final project involving deep learning implementation in Keras on a connected dataset. The course provides a hands-on approach to mastering deep learning concepts and techniques.
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Presentation Transcript
Deep learning Usman Roshan
Course material Machine learning background Basic data representations Basic algorithms Coding in Python scikit-learn Parallel programming: CUDA and OpenCL for GPUs OpenMP for multi-core CPUs Neural networks Basic multi-layer perceptrons
Course material Machine learning for image recognition Classifying images with conventional methods Convolutions for image analysis Effect of fixed convolutions on images Deep learning for image recognition Convolutional neural networks (CNN) Optimization of CNNs
Course material Adversarial attacks Black box and white box attacks on image classification systems Deep learning for text Representation of text data: bag of words and word2vec CNNs for text
Grading One GPU assignment, one OpenMP assignment, One Keras assignment One mid-term Students in groups of two will do a project and submit it towards the end of the semester. Project summaries and results will be presented in 10-15 min slots. Project will involve deep learning implementation in Keras on a dataset connected to a paper.
