What You Always Wanted to Know About Deep Learning, but Were Afraid to Ask
Demystifying deep learning concepts like back-propagation, gradient descent, and neural networks. Practical guide to training a model for object recognition using TensorFlow and Keras.
Build Your Own Neural Network – With PHP
Discover how to build artificial neural networks using PHP, exploring key concepts like neurons, parameters, and machine learning algorithms in this hands-on introduction to AI.
Neural Networks - Walkthrough
Comprehensive walkthrough of neural networks with visual aids, demystifying concepts like 'tanh' and explaining how they work, suitable for beginners struggling with implementation.
Efficient Evaluation of Activation Functions over Encrypted Data
Explore techniques for approximating activation functions on encrypted data, with applications in secure machine learning for image classification and representation learning.
Classification Based on Missing Features in Deep Convolutional Neural Networks
Explore a novel approach to enhance neural network accuracy and robustness in adversarial situations, focusing on classification based on missing features in deep convolutional models.
Deep Learning with TensorFlow
Interactive TensorFlow session: build deep learning models from basics to advanced concepts. Hands-on approach to creating and using models in applications, suitable for beginners and experienced developers alike.
Writing a Neural Net from Scratch
Learn to build a neural network for image recognition from scratch in C#, covering key concepts like gradient descent and backpropagation without complex math or external libraries.
Writing a Neural Net from Scratch
Learn to code a neural network for image recognition in C# from scratch, covering gradient descent, activation functions, and backpropagation without complex math or external libraries.
Logistic Regression and the Perceptron Algorithm - A Friendly Introduction
Friendly introduction to logistic regression and perceptron algorithm, covering key concepts like data classification, gradient descent, and neural networks with minimal math and visual explanations.
A Friendly Introduction to Deep Learning and Neural Networks
Explore neural networks and deep learning concepts, from gradient descent to activation functions, in this friendly introduction to AI fundamentals.
MIT Introduction to Deep Learning
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for building effective models.
Introduction to Deep Learning
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques for building effective models.
EfficientML.ai - Basics of Neural Networks - Lecture 2
Explore neural network fundamentals, including architecture, training, and optimization techniques. Gain insights into efficient machine learning concepts and applications.
Basics of Neural Networks - EfficientML.ai Lecture 2
Explore neural network fundamentals, including architecture, training, and optimization techniques, in this comprehensive MIT lecture on efficient machine learning.
Fast Neural Kernel Embeddings for General Activations
Explore fast sketching methods for approximating multi-layered NNGP and NTK matrices with various activation functions, overcoming limitations of prior works and achieving significant speedups.
Introduction to Deep Neural Networks - Lecture 21
Explore deep neural networks, their architecture, and applications in AI. Learn key concepts and techniques for implementing advanced machine learning models.
Gaussian Pre-Activations in Neural Networks: Myth or Reality?
Explore Gaussian pre-activations in neural networks, challenging common assumptions and proposing new initialization methods for improved information propagation in deep, narrow architectures.
Mehler's Formula, Branching Processes, and Compositional Kernels of Deep Neural Networks
Explore deep neural networks through compositional kernels and branching processes. Gain insights into activation functions, network limits, and memorization capacity. Learn about eigenvalues and a new random features algorithm.
Intro to Deep Learning - ML Tech Talks
Comprehensive introduction to deep learning concepts, neural network architectures, and practical applications, featuring TensorFlow examples, code demos, and insights into representation learning and model interpretability.
A Connection between Probability, Physics and Neural Networks
Explore the intersection of probability, physics, and neural networks, focusing on constructing physics-consistent models using Gaussian processes and the central limit theorem.
Understanding ChatGPT: From Machine Learning to Language Models
Explore machine learning, neural networks, and GPT technology. Gain insights into the evolution and inner workings of ChatGPT, from basic concepts to advanced applications.