Sustainable Manufacturing: Design & Decision-Making

Related lectures (31)

Page 2 of 4

Covers the basics of neural networks, focusing on the development from two layers neural networks to deep neural networks.

Delves into neural network approximation, supervised learning, challenges in high-dimensional learning, and deep learning experimental revolution.

Covers data representations, Bag of Words, histograms, data pre-processing, and neural networks.

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Explores neural networks optimization, including backpropagation, batch normalization, weight initialization, and hyperparameter search strategies.

Covers Convolutional Neural Networks, standard architectures, training techniques, and adversarial examples in deep learning.

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Covers the basics of neural networks, activation functions, training, image processing, CNNs, regularization, and dimensionality reduction methods.

Introduces Convolutional Neural Networks, explaining their architecture, training process, and applications in semantic segmentation tasks.

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.