Neural Networks - Lecture 25
Dive into neural networks' structural foundations and discover their role as a hypothesis class, exploring key concepts and practical applications in machine learning.
Neural Networks Foundations - Feedforward Neural Networks
Master the fundamentals of feedforward neural networks, exploring their architecture, functionality, and implementation principles for building effective machine learning models.
Machine Learning - Lecture 28
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level lecture from University of Utah.
Machine Learning - Lecture 25
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level lecture from University of Utah.
Machine Learning - Lecture 24
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level lecture from University of Utah.
Machine Learning - Lecture 23
Dive into advanced data science concepts through comprehensive coverage of machine learning principles, algorithms, and practical applications in this graduate-level academic lecture.
What Is a Neural Network? - Machine Learning for JavaScript Hackers with TensorFlow.js
Explore neural networks, perceptrons, and sigmoid neurons using TensorFlow.js. Learn about activation functions, network architecture, and weight initialization for machine learning in JavaScript.
How to Create a Neural Network and Train It to Identify Doodles
Comprehensive guide to building and training neural networks from scratch, covering key concepts and practical implementation for image recognition tasks.
Create Artificial Neural Network Using Weight Initialization Tricks
Learn weight initialization techniques for creating artificial neural networks, enhancing model performance and convergence in deep learning applications.
CNN Explainer
Interactive visualization tool demystifies Convolutional Neural Networks (CNNs) for beginners. Explore network structures, mathematical operations, and train your own model using hands-on demonstrations and explanations.
Build a Neural Network for Classification from Scratch with PyTorch
Learn to build a neural network for classification using PyTorch, covering data preparation, network structure, and implementation. Gain hands-on experience in deep learning fundamentals.
Machine Learning Explained - Teaching a Robot to Walk Tutorial
Comprehensive tutorial on machine learning concepts, from handwriting recognition to reinforcement learning, with practical demonstrations using neural networks and robot simulations.
Approximation with Deep Networks - Remi Gribonval, Inria
Explore deep network approximation, focusing on expressivity, universal properties, sparse networks, and the impact of network architecture and activation functions on performance.
PyTorch Activation and Loss Functions
Explore activation and loss functions in PyTorch, including kink vs. smooth activations, margin-based losses, and designing effective loss functions for Energy-Based Models (EBMs).
Deep Neural Networks
Explore deep neural networks, activation functions, and optimization techniques. Learn about gradient vanishing and solutions like rectified linear units.
Introduction to Neural Networks in Python - Tensorflow-Keras
Learn neural network basics, architecture, hyperparameters, and implementation in Python using TensorFlow/Keras. Includes hands-on coding examples for various classification tasks, from simple to complex datasets.
ODSC London Meet-up - Deep Learning Frameworks
Explore deep learning frameworks, algorithms, and state-of-the-art developments with industry expert Peter Morgan in this comprehensive overview of neural network technologies.
Machine Learning and Neural Networks: Activation Functions - Day 11
Explore activation functions in neural networks, their role in machine learning, and how they impact model performance and decision-making processes.
Basic Aspects of Convolutional Neural Networks - Tutorial
Explore fundamental concepts of convolutional neural networks, their architecture, and applications in deep learning. Gain insights into this powerful technique for image and pattern recognition.
How the Brain of an AI Works - Understanding Neural Networks
Explore the inner workings of AI neural networks, from basic mathematical equations to complex layered structures, and understand their potential impact on society and employment.
Machine Learning for Quantum Matter - Lecture 1
Explore machine learning applications in quantum matter, covering classical algorithms, high-dimensional data, supervised learning, neural networks, and practical examples in physics.