Lecture 20 – Learning the feature vector 𝒇(𝒙)

Cosine (extract from undergrad DLSP25)

Chapter 2, video 4–6

06 – Optimisation and gradient ascent

05 – Multi-class perceptron, binary and multi-class logistic regression

04 – Binary classifier evaluation, binary Perceptron

Chapter 1, video 1–3

03 – Naïve Bayes parameters estimation and Laplace smoothing

02 – Discrete probability recap, Naïve Bayes classification

00 – Course introduction

01 – Course first part recap, Naïve Bayes intro

14 – From latent-variable EBM (K-means, sparse coding) to target prop to autoencoders, step-by-step

07 – Classification, an energy perspective – PyTorch 5-step training code

06 – Classification, an energy perspective – Backprop and contrastive learning

05 – Classification, an energy perspective – Notation and introduction

03 – Inference with neural nets

00 – Intro to NYU Deep Learning Fall 2022 playlist

10P – Non-contrastive joint embedding methods (JEMs) for self-supervised learning (SSL)

09P – Contrastive joint embedding methods (JEMs) for self-supervised learning (SSL)

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

13L – Optimisation for Deep Learning

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

08L – Self-supervised learning and variational inference

09L – Differentiable associative memories, attention, and transformers

14 – Prediction and Planning Under Uncertainty

06L – Latent variable EBMs for structured prediction

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

13 – The Truck Backer-Upper

04L – ConvNet in practice

03L – Parameter sharing: recurrent and convolutional nets

02L – Modules and architectures

01L – Gradient descent and the backpropagation algorithm

12 – Planning and control

12L – Low resource machine translation

11 – Graph Convolutional Networks (GCNs)

10L – Self-supervised learning in computer vision

11L – Speech recognition and Graph Transformer Networks

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

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

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

07 – Unsupervised learning: autoencoding the targets

01 – History and resources

Behind the scenes

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

05.2 – But what are these EBMs used for?

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

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

04.1 – Natural signals properties and the convolution

02 – Neural nets: rotation and squashing

03 – Tools, classification with neural nets, PyTorch implementation

Supervised and self-supervised transfer learning (with PyTorch Lightning)

Week 15 – Practicum part B: Training latent variable energy based models (EBMs)

Week 15 – Practicum part A: Inference for latent variable energy based models (EBMs)

Matrix multiplication, signals, and convolutions

Week 14 – Practicum: Overfitting and regularization, and Bayesian neural nets

Week 14 – Lecture: Structured prediction with energy based models

Week 13 – Lecture: Graph Convolutional Networks (GCNs)

Week 13 – Practicum: Graph Convolutional Neural Networks (GCN)

Week 12 – Lecture: Deep Learning for Natural Language Processing (NLP)

Week 12 – Practicum: Attention and the Transformer