Machine Learning for Engineers
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Lecture 8.4 - Vision Transformers and Multimodal Models
Lecture 10 - Agentic AI
Lecture 9 - Fine-tuning Transformer Models
Lecture 8.1 - Tokenization and embeddings
Lecture 8.2 - Seq2seq models and self-attention
Lecture 8.4 - Vision Transformers (from scratch)
Lecture 8.3 - Transformer models
Lecture 7.7 - Transfer Learning
Lecture 7.6 - Visualizing what models look at
Lecture 7.5 - Visualizing what models learn
Lecture 7.4 - Famous Convolutional Networks
Lecture 7.3 - Building Convolutional Networks
Lecture 7.2 - Convolutional Neural Networks
Lecture 7.1 - Convolutions
Welcome
Extra Lecture - Meta-Learning
Extra Lecture - Gaussian Processes in practice
Extra Lecture - Gaussian Processes
Extra Lecture - Naive Bayes and Bayesian Networks
Extra Lecture - Bayesian Learning
Lecture 8.2 - Word embeddings (old version)
Lecture 8.1 - Neural Networks for text (old version)
Lecture 6.6 - Model selection and regularization
Lecture 6.4 - Neural network optimizers
Lecture 6.5 - Neural networks in practice
Lecture 6.3 - Activation functions and weight initialization
Lecture 6.2 - Training neural nets
Lecture 6.1 - Introduction to neural networks
Lecture 5.8 - Handling imbalanced data
Lecture 5.7 - Missing value imputation