via Artificial Neurons.
This article offers a brief glimpse of the history and basic concepts of machine learning. We will take a look at the first algorithmically described neural network and the gradient descent algorithm in context of adaptive linear neurons, which will not only introduce the principles of machine learning but also serve as the basis for modern multilayer neural networks in future articles.
via Introduction to Artificial Neural Networks Part 2 – Learning.
Welcome to part 2 of the introduction to my artificial neural networks series, if you haven’t yet read part 1 you should probably go back and read that first!
In part 1 we were introduced to what artificial neural networks are and we learnt the basics on how they can be used to solve problems. In this tutorial we will begin to find out how artificial neural networks can learn, why learning is so useful and what the different types of learning are. We will specifically be looking at training single-layer perceptrons with the perceptron learning rule.
Before we begin, we should probably first define what we mean by the word learning in the context of this tutorial. It is still unclear whether machines will ever be able to learn in the sense that they will have some kind of metacognition about what they are learning like humans. However, they can learn how to perform tasks better with experience. So here, we define learning simply as being able to perform better at a given task, or a range of tasks with experience.
via Introduction to Artificial Neural Networks – Part 1.
This is the first part of a three part introductory tutorial on artificial neural networks. In this first tutorial we will discover what neural networks are, why they’re useful for solving certain types of tasks and finally how they work.
Computers are great at solving algorithmic and math problems, but often the world can’t easily be defined with a mathematical algorithm. Facial recognition and language processing are a couple of examples of problems that can’t easily be quantified into an algorithm, however these tasks are trivial to humans. The key to Artificial Neural Networks is that their design enables them to process information in a similar way to our own biological brains, by drawing inspiration from how our own nervous system functions. This makes them useful tools for solving problems like facial recognition, which our biological brains can do easily.
More and more people are looking to new types of tools and solutions for deeper insights into data than traditional statistics. There is great need to provide better services based on diagnosis, recommendation, exploration, estimation, optimization, etc. It is imperative to introduce more “intelligence” into
the systems in order to create value and enhance competitiveness.
The new age of “wearables” (smart watches, “glasses”, bio-sensors and new sensors in mobiles) requires intelligent processing to become truly meaningful. The availability of large amounts of data makes it possible to quickly train the models for prediction, classification, recognizing patterns in texts, etc.
But are current models sufficient? Intelligent systems at the forefront are often combinations of different types of AI, extraction features that span over multiple data sources with highly variable quality. Another important aspect is the ability to utilize feedback, such as from example users, which allow solutions to be automatically optimized and adapted to changing conditions. There will be many exciting practical examples from AI projects
from major U.S. companies such as eBay, Vodafone (one of the largest telecom operators) and Safeway (U.S. equivalent to ICA).
Click to Read: In this quick post I just wanted to share some Python code which can be used to benchmark, test, and develop Machine Learning algorithms with any size of data.
Click to Read: As we have described previously on this blog, at Netflix we are constantly innovating by looking for better ways to find the best movies and TV shows for our members. When a new algorithmic technique such as Deep Learning shows promising results in other domains (e.g. Image Recognition, Neuro-imaging, Language Models, and Speech Recognition), it should not come as a surprise that we would try to figure out how to apply such techniques to improve our product. In this post, we will focus on what we have learned while building infrastructure for experimenting with these approaches at Netflix. We hope that this will be useful for others working on similar algorithms, especially if they are also leveraging the Amazon Web Services (AWS) infrastructure. However, we will not detail how we are using variants of Artificial Neural Networks for personalization, since it is an active area of research.
The post is for fundamental understanding on how to get started with Neural networks and build applications using NodeJS. I have not discussed any self developed example for now. I would share the same very soon.
Briefly I would introduce ANN i.e Artificial Neural Networks : The introduction of the concepts talk fundamental based using logic gates in design but nowadays we just simulate the logic in our web applications using libraries to get the same results.