Vaughn  Sauer

Vaughn Sauer


ANN: Julia Artificial Neural Networks.


Implementation of backpropagation artificial neural networks in Julia.

Install (from within Julia interpreter):



using ANN

n_hidden_units = 20

ann = ArtificialNeuralNetwork(n_hidden_units)

n_obs = 150
n_feats = 80

X = rand(Float64, n_obs, n_feats)
y = rand(Int64, n_obs)

fit!(ann, X, y)

n_new_obs = 60
X_new = rand(Float64, n_new_obs, n_feats)

y_pred = predict(ann, X_new)


  • Allow users to build multilayer networks
  • Accept DataFrames as inputs. fit! and predict currently require Float64 matrices and vectors.

Author: EricChiang
Source code:
License: View license

#julia #machine-learning 

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ANN: Julia Artificial Neural Networks.

Shradha Singh


All About Artificial Neural Networks – What, Why, and How?

If you are interested in Artificial Intelligence, chances are that you must have heard about Artificial Neural Networks (ANN), and Deep Neural Networks (DNN). This article is about ANN.

The boom in the field of artificial intelligence may have come recently, but the idea is old. The term AI was coined way back in 1956. Its revival though in the 21st century can be traced to 2012 when ImageNet challenge. Before this, AI was known as neural networks or expert systems.

At the foundation of AI are the networks of artificial neurons, the same as the cells of a biological brain. Just like every neuron can be triggered by other neurons in a brain, AI works similarly through ANNs. Let’s know more about them.

Artificial Neural Networks – Borrowing from human anatomy
Popularly known as ANN, Artificial Neural Network is basically a computational system, which is inspired by the structure, learning ability, and processing power of a human brain.

ANNs are made of multiple nodes imitating the neurons of the human brain. These neurons are connected by links and also interact with each other. These nodes facilitate the input of data. The structure in ANN is impacted by the flow of information, which changes the neural networks based on the input and output.

A simple, basic-level ANN is a “shallow” neural network that has typically only three layers of neurons, namely:

Input Layer. It accepts the inputs in the model.
Hidden Layer.
Output Layer. It generates predictions.

#artificial neural networks #neural networks #ai #ml #artificial intelligence

Arne  Denesik

Arne Denesik


Introduction to Artificial Neural Networks for Beginners


ANNs (Artificial Neural Network) is at the very core of Deep Learning an advanced version of Machine Learning techniques. ANNs are versatile, adaptive, and scalable, making them appropriate to tackle large datasets and highly complex Machine Learning tasks such as image classification (e.g., Google Images), speech recognition (e.g., Apple’s Siri), video recommendation (e.g., YouTube), or analyzing sentiments among customers (e.g. Twitter Sentiment Analyzer).

ANN was first introduced in 1943 by the neurophysiologist Warren McCulloch and the mathematician Walter Pitts. However, ANN had its ups and downs. Post-1960 there was a drop in interest and excitement among researchers w.r.t neural networks with the advancement of Support Vector Machines and other powerful Machine Learning techniques that produced better accuracy and had a stronger theoretical foundation. Neural networks were complex and required tremendous computation power and time to train. However post 1990, the advancement in the field of computation (refer to Moore’s law) followed by the production of powerful GPU cards brought some interest back.

#data-science #neural-networks #machine-learning #artificial-neural-network #artificial-intelligence

Angela  Dickens

Angela Dickens


Introduction to Neural Networks

There has been hype about artificial intelligence, machine learning, and neural networks for quite a while now. I have been working on these things for over a year now so I would like to share some of my knowledge and give my point of view on Neural networks. This will not be a math-heavy introduction because I just want to build the idea here.

I will start from the neural network and then I will explain every component of a neural network. If you feel like something is not right or need any help with any of this, Feel free to contact me, I will be happy to help.

When to use the Neural Network?

Let’s assume we want to solve a problem where you are given some set of images and you have to build an automated system that can categories each of those images to its correct label.

The problem looks simple but how do we come with some logic using raw pixel values and target labels. We can try comparing pixels and edges but we won’t be able to come with some idea which can do this task effectively or say the accuracy of 90% or more.

When we have this kind of problem where we have high dimensional data like Images and we don’t know the relationship between Input(Images) and the Output(Labels), In this kind of scenario we should use Neural Networks.ư

What is the Neural network?

Artificial neural networks, usually simply called neural networks, are computing systems vaguely 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

#artificial-intelligence #gradient-descent #artificial-neural-network #deep-learning #neural-networks #deep learning

Tyshawn  Braun

Tyshawn Braun


Perceptron to Multi-layered Feedforward Neural Network

This article will help you understand the transition of AI from classical machine learning to deep learning, starting from the basics of machine learning with its major - supervised and unsupervised learning to different regularization and optimization techniques.

The goal of this article is to introduce the major concepts of machine learning-what is machine learning? How does a machine learn by itself to do a particular task? How does it choose the essential features of the data that strongly contribute to the prediction of future events? How can we understand whether a machine has succeeded or failed in that?

In this article, we will discuss the foundations of artificial neural networks starting from perceptron to multi-layered feedforward neural networks. This article discusses the various stages of how to apply different transformation techniques for data preparation, how to train a neural network, and then validate and deploy a neural network for solving real-world problems.

In this article, we’re going to cover the following main topics:

· Understanding Machine Learning and Artificial Neural Network

· Feedforward Neural Network & Backpropagation Algorithm

· Evaluating and Tuning the Artificial Neural Network

· Classical Machine Learning vs Deep Learning

Understanding Machine Learning and Artificial Neural Network

This section starts with a brief overview of what is machine learning, its major types- supervised and unsupervised learning. Then we will understand the very evolution of artificial neural networks starting with how a biological neuron works. We’ll also discuss the design of artificial neurons with an understanding of deep neural networks with activation functions.

What is Machine Learning?

The term, Machine learning, has become a buzzword nowadays which refers to the ability of a machine to learn from the data without the help of the set of rules that are defined explicitly as like in the traditional rule-based algorithms. So definitely, if it learns from the data without any need for an explicit declaration of rules then it has to do with the experience from learning.

Our way of learning always follows a curve of failures although it’s perfectly descendent, lastly it will converge to the extent of our hard work

In the last decade of technology, machine learning techniques have become the common tools to automate the tasks that would have required huge efforts with the traditional rule-based algorithms.

In the** traditional rule-based algorithms**, the set of rules used to be defined to work on with a specific variety of data and could not be generalized to a large extent of data because of its specificity of working on only particular data. For example, if YouTube, a video sharing site decides to perform a copyright check on videos that are being uploaded on its server with a human operator, it will need a lot many people to execute this task of copyright check. But if YouTube chooses to do this with the help of some video processing algorithm then the task of copyright check would be easier but not robust as video processing algorithm possibly would work only on a set of videos that don’t have any kind of transformations like flip, rotate, crop, blur, etc. And it’s quite difficult to write a separate algorithm for individual transformation so the solution to this problem can be machine learning. In this case, a learning model is built by getting trained on data and identifying implicit features that uniquely signifies the data with which new data can be validated automatically.

Today, we are living in the era of machine-learning-based technologies; email services learn how to classify the emails into spam and ham; search engines learn what to recommend to the user based on their search history; banking systems are now able to sanction loans based on the creditworthiness of a customer. Prediction of heart disease based on clinical data, identifying voice commands, and forecasting annual rainfall are other significant tasks that machine learning facilitates.

One common problem with all of these applications is that a programmer cannot explicitly define the set of instructions for the task that needs to be performed due to the underlying complexity of the data; this was machine learning helps. It has made itself useful across industries like retail, banking, healthcare to the automobile industry for its ability to predict future events with significant accuracy.

_In machine learning algorithms, the __input is the experience in form of data _and output is knowledge or wisdom gained with inductive inference which in turn helps to predict future events, so rather, machine learning is an art of experiential learning.

Let us start with a real-life experience of preparing a food dish with some cooking recipe, how do we prepare the food, let’s go through the process of making the delicious food, at first we collect all the ingredients that are needed for food preparation, as a naïve person in the cooking, we follow a cooking recipe which involves set of steps that need to be performed. Let us take an example of a famous dish of western India, poha, which needs many ingredients like beaten rice flakes, mustard, curry leaves, groundnuts, oil, salt, and others. Assume now, with all ingredients, we start making the poha as per the directions in the cooking recipe.

#artificial-intelligence #machine-learning #artificial-neural-network #neural-networks #deep-learning

Tia  Gottlieb

Tia Gottlieb


Neural Network on Beer Dataset

Artificial neural networks (ANNs), usually simply called neural networks (NNs), are computing systems vaguely 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 that receives a signal then processes it 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.

Image for post

Neural networks learn (or are trained) by processing examples, each of which contains a known “input” and “result,” forming probability-weighted associations between the two, which are stored within the data structure of the net itself. The training of a neural network from a given example is usually conducted by determining the difference between the processed output of the network (often a prediction) and a target output. This is the error. The network then adjusts it’s weighted associations according to a learning rule and using this error value. Successive adjustments will cause the neural network to produce output which is increasingly similar to the target output. After a sufficient number of these adjustments the training can be terminated based upon certain criteria. This is known as [[supervised learning]].

#r #ann #beer #neural-networks #nn #neural networks