7 Deep Learning: tensor backpropagation, convolutional layers (MLVU2020) @riskone1
7 Deep Learning: tensor backpropagation, convolutional layers (MLVU2020) @riskone1
Uploaded February 2020 | Updated September 2026, 3 days ago
slides: mlvu.github.io/lectures/41.DeepLearning1.annotated.pdf
course materials: mlvu.github.io

We recap the basic algorithm of (scalar) backpropagation from last lecture and we discuss in detail what is required to extend this into a mature automatic differentiation system: how to deal with any computation graph, and how to apply backpropagation to models that operate in terms of tensor operations (like matrix multiplication).

The explanation broadly follows the structure of PyTorch, but most concepts apply to Tensorflow and other deep learning frameworks too.
7 Deep Learning: tensor backpropagation, convolutional layers (MLVU2020)MLVU 2.3 Gradient descentMLVU 13.1: Reinforcement learningMLVU 5.4: Logistic regression07 Linear Models 2: Support Vector Machines, the Kernel trick (MLVU2018)MLVU 1.1 What is machine learning?3 Methodology for evaluation: AUC, Bias and Variance (MLVU2020)4 Methodology 2: Data cleaning, Principal Component Analysis, Eigenfaces (MLVU2019)MLVU 4.1: Missing values and outliers11 Models for Sequential Data: Markov Models, Word2Vec, RNNs and LSTMs.5 Probability 1: Entropy, (Naive) Bayes, Cross-entropy loss (MLVU2019)MLVU 5.2: Learning with probability
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7 Deep Learning: tensor backpropagation, convolutional layers (MLVU2020)

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