Lectures related to Neural Networks: Exercises Solutions

Logic Systems: TTL Gates and Analog Aspects

Explores TTL gates, analog aspects, and various logic gates in digital circuits.

Logic Systems: Karnaugh Maps and TTL Gates

Explores Karnaugh maps, TTL gates, analog aspects of digital logic, and prime implicants.

Covers logical circuits, gates, and their combinations to perform basic operations on bits, including adding binary numbers.

Karnaugh Maps: Grouping Techniques

Explores Karnaugh maps, grouping rules, and transistor logic in logic systems.

Logic Gates and IC Technology

Explores the implementation of logic gates in semiconductor material, focusing on TTL and CMOS technologies, ICs, hazards, clocks, D flip-flops, and switch debouncing.

Introduction to Logisim Evolution: Basics of Digital Circuit Design

Introduces Logisim Evolution, focusing on basic components for digital circuit design.

Complex Gates: Exercise-2 Solution

Covers the design of complex logic gates in VLSI.

Quantum Computing: Molecular Transistors and Logic Gates

Delves into molecular transistors, logic gates, and their design using molecular technology.

Neural Networks: Two Layers Neural Network

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

Static Logic Fundamentals

Covers the fundamentals of static logic, including noise immunity, noise margins, and various logic gate designs.

Deep Learning Fundamentals

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.