Neural Networks: Exercises Solutions

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Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Digital Logic Circuits: CMOS and Verilog Design

Discusses digital logic circuits, CMOS technology, and Verilog for circuit design.

Nonlinear Supervised Learning

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

Neural Networks: Training and Activation

Explores neural networks, activation functions, backpropagation, and PyTorch implementation.

Deep Learning: Convolutional Neural Networks

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

Deep Learning Fundamentals

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Feed-forward Networks

Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.

Digital Logic Circuits: CMOS and Verilog Modeling

Discusses digital logic circuits, focusing on CMOS technology and Verilog modeling techniques.

Neural Networks: Regression and Classification

Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.

Deep Learning: Data Representations and Neural Networks

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