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Perceptrons


Deep Neural Network from Scratch in Python

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In this video we build on last week Multilayer perceptrons to allow for more flexibility in the architecture! However, we need to be careful about the layer of abstraction we put in place in order to facilitate the work of the user who want to simply fit and predict. Here we make use of the following three concept: Network, Layer and Neuron. These three components will be composed together to make a fully connected feedforward neural network neural network. For those who don't know a fully connected feedforward neural network is defined as follows (From Wikipedia): "A feedforward neural network is an artificial neural network wherein connections between the nodes do not form a cycle. As such, it is different from its descendant: recurrent neural networks. The feedforward neural network was the first and simplest type of artificial neural network devised. In this network, the information moves in only one direction, forward, from the input nodes, through the hidden nodes (if any) and to the output nodes. There are no cycles or loops in the network."


Build Multilayer Perceptron Models with Keras

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In this 45-minute long project-based course, you will build and train a multilayer perceptronl (MLP) model using Keras, with Tensorflow as its backend. In this 45-minute long project-based course, you will build and train a multilayer perceptronl (MLP) model using Keras, with Tensorflow as its backend. We will be working with the Reuters dataset, a set of short newswires and their topics, published by Reuters in 1986. It's a very simple, widely used toy dataset for text classification. There are 46 different topics, some of which are more represented than others. But each topic has at least 10 examples in the training set.


The mathmatics of the perceptron

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I have been learning about machine learning for some time now and I believe that, to really understand something, one must understand the basics. In this blog post I will go through the theory and at the end I'll share the code for a perceptron, the most basic neural network there is that dates back to 1958 created by Frank Rosenblatt. To train and test it, I'll use a fragment of the mnist dataset. This dataset contains images of 28 x 28 pixels of hand-written digits from 0 to 9. We'll try to classify two, four and six digits and see that it becomes increasingly more difficult to make predictions. The concept of the perceptron is that, with only a simple layer of calculations, our code can understand rules and paths without explicitlly writing them.


A Quick Introduction to Neural Networks

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An Artificial Neural Network (ANN) is a computational model that is inspired by the way biological neural networks in the human brain process information. Artificial Neural Networks have generated a lot of excitement in Machine Learning research and industry, thanks to many breakthrough results in speech recognition, computer vision and text processing. In this post, we will try to develop an understanding of a particular type of Artificial Neural Network called the Multi-Layer Perceptron. ANNs are at the core of Deep Learning. They are versatile, powerful, and scalable, making them ideal to tackle large and highly complex Machine Learning tasks, such as classifying billions of images (e.g., Google Images), powering speech recognition services (e.g., Apple's Siri), recommending the best videos to watch to hundreds of millions of users every day (e.g., Youtube), or learning to beat the world champion at the game of Go by examining millions of past games and then playing against itself (DeepMind's AlphaGo).


AI Contributing to Better Accuracy and Precision in Weather Forecasting - AI Trends

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Traditional models of weather forecasting are based on statistical measures based on data collected from deep space satellites, such as NOAA's Deep Space Climate Observatory, weather balloons, radar systems, and sometimes from IoT-based sensors. Today, AI is finding a role in weather forecasting with machine learning being employed to process more complex data in less time, with the hope of improving accuracy. For example, the Numerical Weather Prediction (NWP) site from NOAA offers a range of data sets for use by researchers, from temperature and precipitation data to wave heights, according to a recent account in Analytics Insight. The site offers vast data sets relayed from weather satellites, relay stations, and radiosondes to help deliver short-term weather forecasts or long-term climate predictions. Besides machine learning, other AI techniques for weather predictions include Artificial Neural Networks, Ensemble Neural Networks, Backpropagation Networks, Radial Basis Function Networks, General Regression Neural Networks, Genetic Algorithms, Multilayer Perceptrons and fuzzy clustering.


Neural Networks in Python

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In this tutorial, we will implement a multi-layered perceptron (a type of a feed-forward neural network) in Python using three different libraries. We'll start off with the most basic example possible, going to more complex and flexible frameworks with the aim of increasing our understanding of how to implement neural networks in Python. Quoting from the scikit-learn documentation [1], "A Multi-layer Perceptron (MLP) is a supervised learning algorithm that learns a function f: Rᵐ Rᵒ by training on a dataset, where m is the number of dimensions for input and o is the number of dimensions for output. Given a set of features X x¹,x²,…,xᵐ, and a target y, it can learn a non-linear function approximator for either classification or regression. It is different from logistic regression, in that between the input and the output layer, there can be one or more non-linear layers, called hidden layers".


Introduction to Machine Learning

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Introduction to Machine Learning This class will teach you the end-to-end process of investigating data through a machine learning lens. This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more). Duke University has about 13,000 undergraduate and graduate students and a world-class faculty helping to expand the frontiers of knowledge.


11 Essential Neural Network Architectures, Visualized & Explained

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The perceptron is the most basic of all neural networks, being a fundamental building block of more complex neural networks. It simply connects an input cell and an output cell. The feed-forward network is a collection of perceptrons, in which there are three fundamental types of layers -- input layers, hidden layers, and output layers. During each connection, the signal from the previous layer is multiplied by a weight, added to a bias, and passed through an activation function. Feed-forward networks use backpropagation to iteratively update the parameters until it achieves a desirable performance.


AI Academy #3: Learn Artificial Neural Networks from A-Z

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Do you like to learn how to forecast economic time series like stock price or indexes with high accuracy? Do you like to know how to predict weather data like temperature and wind speed with a few lines of codes? If you say Yes so read more ... Artificial neural networks (ANNs) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with any task-specific rules. In this Course you learn multilayer perceptron (MLP) neural network by using Scikit learn & Keras libraries and Python.You learn how to classify datasets by MLP Classifier to find the correct classes for them.


Machine learning tool trains on old code to spot bugs in new code

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Altran has released a new tool that uses artificial intelligence (AI) to help software engineers spot bugs during the coding process instead of at the end. Available on GitHub, Code Defect AI uses machine learning (ML) to analyze existing code, spot potential problems in new code, and suggest tests to diagnose and fix the errors. Walid Negm, group chief innovation officer at Altran, said that this new tool will help developers release quality code quickly. "The software release cycle needs algorithms that can help make strategic judgments, especially as code gets more complex," he said in a press release. Code Defect AI uses several ML techniques including random decision forests, support vector machines, multilayer perceptron (MLP) and logistic regression.