Lectures
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Multi-Modality and Generative Models – Surveying SOTA Multi-Modal Generative Models (DALL-E 2, Imagen and more), alongside several recent works for expediting generative-model inference (Deep Learning Course 236781).
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Tutorials (English)
CNNs – Convolutional Layers, Convolutional Neural Networks, Residual Connection
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Optimization – Key concepts in numerical optimization of neural networks with Pytorch.VIDEO
Object Detection – Fundamental Performance Metrics and Deep-Learning Solutions for Object Detection.VIDEO
Sequence Models – Neural models for sequential data – RNNs, LSTMs, GRUs.VIDEO
Attention – Key concepts in attention for sequence processing.VIDEO
Transformers – Transformer Models, Positional Encoding, Multiheaded Attention.VIDEO
GANs – Intro to generative modeling, Generative Adversarial Neural networks.VIDEO
Tutorials (Hebrew)
ML Fundamentals – Definition and key concepts of learning problems, Perceptron model, logistic regression.VIDEO
MLPs – Intro to Multilayer-Perceptron models.
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CNNs – Convolutional Layers, Convolutional Neural Networks, Residual Connection
AutoDiff – Pytorch’s mechansim for automatic differentiation.VIDEO
Object Detection – Fundamental Performance Metrics and Deep-Learning Solutions for Object Detection.
Sequence Models – Neural models for sequential data – RNNs, LSTMs, GRUs.
Attention – Key concepts in attention for sequence processing.
Transformers – Transformer Models, Positional Encoding, Multiheaded Attention.
GANs – Intro to generative modeling, Generative Adversarial Neural networks.
VAEs – Generative modeling with Variational Autoencoders.VIDEO
DDPMs – Intro to generative modeling with diffusion models, DDPM models.VIDEO
Multi Modality – Basic notions in multi-modal deep learning, CLIP.VIDEO