Artificial neural networks | Classification algorithms | Mathematical psychology | Computational statistics
Artificial neural networks (ANNs), usually simply called neural networks (NNs) or neural nets, are computing systems inspired by the biological neural networks that constitute animal brains. An ANN is based on a collection of connected units or nodes called artificial neurons, which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit a signal to other neurons. An artificial neuron receives signals then processes them and can signal neurons connected to it. The "signal" at a connection is a real number, and the output of each neuron is computed by some non-linear function of the sum of its inputs. The connections are called edges. Neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Neurons may have a threshold such that a signal is sent only if the aggregate signal crosses that threshold. Typically, neurons are aggregated into layers. Different layers may perform different transformations on their inputs. Signals travel from the first layer (the input layer), to the last layer (the output layer), possibly after traversing the layers multiple times. (Wikipedia).
Deep Learning with Neural Networks and TensorFlow Introduction
Welcome to a new section in our Machine Learning Tutorial series: Deep Learning with Neural Networks and TensorFlow. The artificial neural network is a biologically-inspired methodology to conduct machine learning, intended to mimic your brain (a biological neural network). The Artificial
From playlist Machine Learning with Python
In this video, I present some applications of artificial neural networks and describe how such networks are typically structured. My hope is to create another video (soon) in which I describe how neural networks are actually trained from data.
From playlist Machine Learning
But what is an Artificial Neuron? | Artificial Neurons decoded
#ArtificialNeuron #perceptron #deeplearning In this video, I have explained the concept of an artificial neuron from a mathematical point of view. This video will help the beginners to understand the building blocks of the modern day deep neural nets. You will get to know what are the dif
From playlist Learn Machine Learning Concepts
This lecture gives an overview of neural networks, which play an important role in machine learning today. Book website: http://databookuw.com/ Steve Brunton's website: eigensteve.com
From playlist Intro to Data Science
Neural Networks 1 Neural Units
From playlist Week 5: Neural Networks
17 Machine Learning: Artificial Neural Networks
Let's demystify artificial neural networks with an accessible lecture on artificial neural networks, including the architecture, parameters, hyperparameters, and training with back-propagation and steepest descent. A demonstration workflow is available at https://git.io/fjlao. Try out a
From playlist Machine Learning
This lecture discusses artificial intelligence (AI) in the context of data science and machine learning. Book website: http://databookuw.com/ Steve Brunton's website: eigensteve.com
From playlist Intro to Data Science
What is Neural Network in Machine Learning | Neural Network Explained | Neural Network | Simplilearn
This video by Simplilearn is based on Neural Networks in Machine Learning. This Neural Network in Machine Learning Tutorial will cover the fundamentals of Neural Networks along with theoretical and practical demonstrations for a better learning experience ๐ฅEnroll for Free Machine Learning
From playlist Machine Learning Algorithms [2022 Updated]
What is a Neural Network? | How Deep Neural Networks Work | Neural Network Tutorial | Simplilearn
๐ฅArtificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=WhatisaNeuralNetwork-VB1ZLvgHlYs&utm_medium=Descriptionff&utm_source=youtube ๐ฅProfessional Certificate Program In AI And Machine Learning: https:
From playlist Deep Learning Tutorial Videos ๐ฅ[2022 Updated] | Simplilearn
Neural Network In Artificial Intelligence | Neural Network Explained | Neural Network | Simplilearn
Neural Network in artificial intelligence by simplilearn is a tutorial based on the fundamentals of neural network programming. In this video, we are going to discuss the role of *Neural Network in Artificial Intelligence* in different fields.This video will give idea on how neural network
From playlist Deep Learning Tutorial Videos ๐ฅ[2022 Updated] | Simplilearn
Data Science - Part VIII - Artifical Neural Network
For downloadable versions of these lectures, please go to the following link: http://www.slideshare.net/DerekKane/presentations https://github.com/DerekKane/YouTube-Tutorials This lecture provides an overview of biological based learning in the brain and how to simulate this approach thr
From playlist Data Science
What is Deep Learning? | Introduction to Deep Learning | Deep Learning Tutorial | Simplilearn
๐ฅArtificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=DeepLearning-FbxTVRfQFuI&utm_medium=Descriptionff&utm_source=youtube ๐ฅProfessional Certificate Program In AI And Machine Learning: https://www.si
From playlist Deep Learning Tutorial Videos ๐ฅ[2022 Updated] | Simplilearn
Machine Learning vs Deep Learning vs Artificial Intelligence | ML vs DL vs AI | Simplilearn
๐ฅArtificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=AI-9dFhZFUkzuQ&utm_medium=Descriptionff&utm_source=youtube ๐ฅProfessional Certificate Program In AI And Machine Learning: https://www.simplilearn.
From playlist Deep Learning Tutorial Videos ๐ฅ[2022 Updated] | Simplilearn
How Do Neural Networks Grow Smarter? - with Robin Hiesinger
Neurobiologists and computer scientists are trying to discover how neural networks become a brain. Will nature give us the answer, or is it all up to an artificial intelligence to work it out? Watch the Q&A: https://youtu.be/DoTSICEUm90 Get Robin's Book: https://geni.us/5wIuX0W Join Peter
From playlist Livestreams
What is Deep Learning | Deep Learning Explained | Deep Learning Tutorial For Beginners | Simplilearn
๐ฅArtificial Intelligence Engineer Program (Discount Coupon: YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=WhatIsNov18DeepLearning&utm_medium=Descriptionff&utm_source=youtube ๐ฅProfessional Certificate Program In AI And Machine Learning: https://www.sim
From playlist Deep Learning Tutorial Videos ๐ฅ[2022 Updated] | Simplilearn
NVIDIA Deep Learning Course Class #1 โ Introduction to Deep Learning
Register for the full course at https://developer.nvidia.com/deep-learning-courses This first in a series of webinars Introduction to Deep Learning covers basics of Deep Learning, why it excels when running on GPUs, and the three major frameworks available for taking advantage of Deep Lear
From playlist Deep Neural Networks
Introduction to "Intelligence and Learning"
This video begins a new series accompanying my ITP spring 2017 course "Intelligence and Learning". The syllabus to this class can be found here: https://github.com/shiffman/NOC-S17-2-Intelligence-Learning Support this channel on Patreon: https://patreon.com/codingtrain To buy Coding Train
From playlist Intelligence and Learning
Artificial Intelligence Full Course in 10 Hours [2023] | Artificial Intelligence Tutorial | Edureka
๐ฅ ๐๐๐๐ก๐ข๐ง๐ ๐๐๐๐ซ๐ง๐ข๐ง๐ ๐๐ง๐ ๐ข๐ง๐๐๐ซ ๐๐๐ฌ๐ญ๐๐ซ๐ฌ ๐๐ซ๐จ๐ ๐ซ๐๐ฆ : https://www.edureka.co/masters-program/machine-learning-engineer-training (Use Code "๐๐๐๐๐๐๐๐๐") This Edureka video on "Artificial Intelligence Full Course" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence
From playlist Artificial Intelligence Tutorial For Beginners | Edureka
Neural Network Architectures & Deep Learning
This video describes the variety of neural network architectures available to solve various problems in science ad engineering. Examples include convolutional neural networks (CNNs), recurrent neural networks (RNNs), and autoencoders. Book website: http://databookuw.com/ Steve Brunton
From playlist Data Science