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Lesson 05 – A softer perceptron, part I probabilities

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Lesson 04 – Bias, perceptron’s properties, and multi-class classification

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Lesson 03 – Wiener’s cybernetics, Hebbian plasticity, and Rosenblatt’s perceptron

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Lesson 02 – Programming a neural network

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Lesson 01 – Course intro and McCulloch & Pitts binary neuron

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Lecture 20 – Learning the feature vector 𝒇(𝒙)

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Cosine (extract from undergrad DLSP25)

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Chapter 2, video 4–6

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06 – Optimisation and gradient ascent

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

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04 – Binary classifier evaluation, binary Perceptron

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Chapter 1, video 1–3

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03 – Naïve Bayes parameters estimation and Laplace smoothing

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02 – Discrete probability recap, Naïve Bayes classification

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00 – Course introduction

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01 – Course first part recap, Naïve Bayes intro

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

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

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

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05 – Classification, an energy perspective – Notation and introduction

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03 – Inference with neural nets

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00 – Intro to NYU Deep Learning Fall 2022 playlist

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

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

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

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13L – Optimisation for Deep Learning

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

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08L – Self-supervised learning and variational inference

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09L – Differentiable associative memories, attention, and transformers

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14 – Prediction and Planning Under Uncertainty

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