Schedule
Subject to revision. Lecture notes, assignments and solutions will all be linked here, as they are available.
Past video lectures by Alex Smola, the Textbook, and Jupyter Notebooks for Recitations

WeekLectureRecitation

1Deep Learning Applications and Linear Algebra[Lecture]
Python, Numpy, Pytorch, and Autograd [Recitation] [Colab Notebook 1  Python and Numpy] [Colab Notebook 2  Pytorch Tensors]

2Naive Bayes and Linear Models[Lecture]
Gradient Descent, Numerical Stability, and Regression [Recitation] [Colab Notebook 1  Gradient Descent] [Colab Notebook 2  Numerical Stability and Hardware Optimisation] [Colab Notebook 3  Regression]

3Maximum Likelihood Estimation, MAP Inference, and Softmax Regression[Lecture]
Learning from Data with Deep Neural Networks [Recitation] [Colab Notebook  Neural Networks with PyTorch]

4Multilayer Perceptrons, Model Complexity, Regularization, and Dropout[Lecture]
Inference and Overfitting [Recitation] [Colab Notebook  Inference and Overfitting]

5Convolutional Neural Networks[Lecture]
Convolutional Neural Networks and Architectures [Recitation] [Colab Notebook  CNNs]

6A History of CNN Architectures and Modern Applications[Lecture]


7Midterm  no class

8Sequence Modeling and Recurrent Neural Networks[Lecture]
Sequences and Language Models [Recitation] [Colab Notebook  Sequence Models]

9Advanced RNNs, Attention, and Transformers[Lecture]
Language Models, Neural Machine Translation, Attention, and Transformers [Recitation] [Colab Notebook  Neural Machine Translation]

Demo Day