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Introduction to Deep Learning (DML) Summer 2026
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    zsamboki/bsm-introduction-to-deep-learning-summer-2026
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    zsamboki/bsm-introduction-to-deep-learning-summer-2026
    • Home
      • Installation Instructions
      • Syllabus
      • 6/15 Basics of Supervised Learning
      • 6/16 Linear Regression
      • 6/17 Multinomial Logistic Regression I
        Gradient Descent
      • 6/18 Multinomial Logistic Regression II
        Negative Log Likelihood
      • 6/22 Stochastic Gradient Descent
      • 6/23 Repeated Experiments
        Confidence Intervals and Ensembles
      • 6/24 Early Stopping and Grid Search
      • 6/25 Introduction to Reinforcement Learning I
      • 6/29 Introduction to Reinforcement Learning II
      • 6/30 Q-Learning
      • 7/1 Nonlinear Function Approximation with Multilayer Perceptrons
      • 7/2 Multilayer Perceptrons and Universal Approximation Theorems
      • 7/6 Initialization
      • 7/7 Hyperparameter Optimization
      • 7/8 Optimizers
      • 7/9 Policy Gradient Theorem
      • 7/13 Deep Q-Networks
      • 7/14 Introduction to Geometric Deep Learning
      • 7/15 Convolutional Neural Networks I
        Convolutions are the Shift-Equivariant Maps
      • 7/16 Convolutional Neural Networks II
        Locality and Pooling Maps
      • 7/20 Dropout, Normalization, and Residual Layers
      • 7/21 Tokenization and Embeddings
      • 7/22 Dot-Product Attention
      • 7/23 Transformer Blocks
      • 7/27 BERT Models for Language Modelling and Supervised Tasks
      • 7/28 Generative Pretrained Transformer
      • 7/29 Scaling Transformers and Instruction Tuning
    • Lab Notebooks
      • Homework 1
        Due 6/23, 10:00am
      • Homework 2
        Due 6/25, 10:00am
      • Homework 3
        Due 6/30, 10:00am
      • Homework 4
        Due 7/6, 10:00am
      • Homework 5
        Due 7/8, 10:00am
      • Homework 6
        Due 7/14, 10:00am
      • Homework 7
        Due 7/16, 10:00am
      • Homework 8
        Due 7/23, 10:00am
      • Homework 9
        Due 7/28, 10:00am

    Lab Notebooks

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    7/29 Scaling Transformers and Instruction Tuning
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    Homework 1
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