- Lecture and seminar materials for each week are in ./week* folders, see README.md for materials and instructions
- Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue
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week01 Introduction to Digital Signal Processing
- Lecture: Signals, Fourier transform, Spectrograms, MelScale
- Seminar: Intro in PyTorch, AudioMNIST Classification
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week02 Automatic Speech Recognition
- Lecture: Metrics, Attention, LAS, CTC, BeamSearch
- Seminar: QuartzNet and CTC based ASR
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week03 Text to Speech
- Lecture: Tacotron2, WaveNet, Parallel WaveGAN
- Seminar: WaveNet based Vocoder
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week04 Language Modeling
- Lecture: Word Embeddings, Language Modeling
- Seminar: POS tagging based on bi-LSTM, Text Classification based on CNN+Attention, and Neural LMs
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week05 Machine Translation
- Lecture: Encoder-Decoder framework, Attention, Transformer
- Seminar: Transformer in details
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week06 Transfer Learning
- Lecture: ELMo, BERT
- Seminar: Intro to HuggingFace
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week07 Facial Recognition
- Lecture: Triplet loss, Angular Softmax, ArcFace
- Seminar: Metric Learning with CIFAR100
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week08 Segmentation
- Lecture: Upsampling, U-Net, HRNet, Metrics
- Seminar: Cell Segmentation
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week09 Object Detection
- Lecture: R-CNN, Fast R-CNN, Faster R-CNN, YOLO
- Seminar: People Detection
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week10 How to deploy your neural network?
- Lecture: Quantization, Pruning, Distilation
- Seminar: Flask and torchserve for model deployment
- ASR Implementation of a small ASR model based on QuartzNet
- MT Implementation of a small MT model based on Transformer
- CV Implementation of a small segmentation model
Course materials and teaching (mainly) performed by