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01 – History and resources

01L – Gradient descent and the backpropagation algorithm

02 – Neural nets: rotation and squashing

02L – Modules and architectures

03 – Tools, classification with neural nets, PyTorch implementation

03L – Parameter sharing: recurrent and convolutional nets

04L – ConvNet in practice

04.1 – Natural signals properties and the convolution

04.2 – Recurrent neural networks, vanilla and gated (LSTM)

05L – Joint embedding method and latent variable energy based models (LV-EBMs)

05.1 – Latent Variable Energy Based Models (LV-EBMs), inference

05.2 – But what are these EBMs used for?

06L – Latent variable EBMs for structured prediction

06 – Latent Variable Energy Based Models (LV-EBMs), training

07L – PCA, AE, K-means, Gaussian mixture model, sparse coding, and intuitive VAE

07 – Unsupervised learning: autoencoding the targets

08L – Self-supervised learning and variational inference

08 – From LV-EBM to target prop to (vanilla, denoising, contractive, variational) autoencoder

09L – Differentiable associative memories, attention, and transformers

09 – AE, DAE, and VAE with PyTorch; generative adversarial networks (GAN) and code

10L – Self-supervised learning in computer vision

10 – Self / cross, hard / soft attention and the Transformer

11L – Speech recognition and Graph Transformer Networks

11 – Graph Convolutional Networks (GCNs)

12L – Low resource machine translation

12 – Planning and control

13L – Optimisation for Deep Learning

13 – The Truck Backer-Upper

14L – Lagrangian backpropagation, final project winners, and Q&A session

14 – Prediction and Planning Under Uncertainty

AI2S Xmas Seminar - Dr. Alfredo Canziani (NYU) - Energy-Based Self-Supervised Learning

Description:

Dive into the world of deep learning with this comprehensive NYU course. Explore the history and foundations of neural networks, including gradient descent and backpropagation. Master essential concepts like convolutional and recurrent neural networks, and gain hands-on experience with PyTorch implementations. Delve into advanced topics such as energy-based models, self-supervised learning, and variational inference. Discover the applications of deep learning in computer vision, speech recognition, and natural language processing. Learn about graph convolutional networks, transformers, and attention mechanisms. Tackle optimization techniques for deep learning and explore planning and control under uncertainty. Conclude with insights into Lagrangian backpropagation and participate in a Q&A session. This course offers a thorough understanding of deep learning principles and their practical applications in various domains.

NYU Deep Learning

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#Computer Science #Deep Learning #Machine Learning #Unsupervised Learning #Artificial Intelligence #Neural Networks #PyTorch #Self-supervised Learning #Autoencoders

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