Introduction to Artificial Neural Network (ANN) With Python
Introduction to Neural Network
The artificial neural network is a system that was first introduced by psychologist Donald Hebb in the 1940s. The neural network was modelled after the human brain and it can be trained to perform a series of tasks, such as recognizing an object from a picture. In 1954, Rosenblatt proposed a computational model of a neural net for pattern recognition.
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In the last few years, there has been an increase in research on artificial neural networks due to their ability to process large amounts of data quickly and efficiently.
One example is that Google’s AI program AlphaGo Zero used an Artificial Neural Network with Deep Learning to teach itself how to play the game “Go” without any external data or training from humans.
Design of NN
Neural networks are designed to process information in a way that mimics how the human brain processes information. They are composed of a large number of interconnected nodes, or individual computers. These nodes work closely together and share data with one another.
A neural network is a set of algorithms and systems that are modelled after our brain but use numbers instead of neurotransmitters. These networks are made up of many layers, each one stacked on top of the other like sheets lining a mattress.
Neural networks can be used for all sorts of things including image processing, prediction, and classification of patterns and features in data sets.
Neural Network Computation
A neural network is a computational model based on the architecture of the human brain. A neural network consists of neurons that are connected to each other through weighted links.
The weights on these links are adjusted in order to optimize the performance of the system for its particular task, such as identifying handwritten digits, recognizing spoken words or distinguishing between two different types of images.
Since neural networks can be used to model arbitrary functions, they are often referred to as universal approximators because they can effectively map any function without being explicitly programmed for that purpose.
What is ANN?
An Artificial Neural Network (ANN) is an interconnected group of nodes, akin to the vast network of neurons in the human brain. The connections between the nodes are weighted, which means that the strength of the connection varies according to its importance.
The ANN can be programmed with many input or output parameters. This programming is called a neural network architecture, and it is used to determine how strongly each connection affects any other node in the network
In order to apply an artificial neural network to a classification problem, we must first decide on a type of network architecture. The most common type of architecture is a feed-forward neural network. Feed-forward networks consist of an input layer, one or more hidden layers and an output layer. Data is fed from the input layer into the hidden layers and finally through the output layer to produce outputs that can be classified as belonging to one of the various classes.
The latest research updates in Artificial Neural Networks include the following: — Google DeepMind’s AlphaGo Zero is now able to teach itself without any human intervention — Facebook has released its own AI chatbot for Messenger — IBM has announced the release of their new AI chip called ‘Watson D3’
What is DeepLearning?
Deep Learning is a form of machine learning in which the data processed by the network has multiple layers and connections between nodes at different layers.
Neural Network: A neural network is a network consisting of many loosely related units, each of which can communicate with other units at the same or higher level in the hierarchy. It is composed of an input layer, one or more hidden layers, and an output layer.
Deep Learning: Deep learning is a subset of machine learning that focuses on neural networks that are particularly well suited to analyze complex data such as text, sound and images. Deep Learning (DL) implementations typically involve training neural networks on huge sets of data (i.e., millions or billions) and then presenting it with new data in order to predict its output accurately enough to
Neural Network in Deep Learning
Neural Network is the backbone of Deep Learning. Neural Networks are a set of algorithms that take inspiration from the human brain and how it processes data.
Neural Networks typically require many more training examples to learn than other machine learning techniques like Regression or Decision Trees. Moreover, neural networks are not deterministic in nature and they have fewer guarantees on the accuracy of the predictions they make.
Neural networks have been used in many different applications such as image recognition, speech recognition, robot control, natural language processing and so on. While deep learning has been a key player in artificial intelligence for a long time now, it is only recently that neural networks have come into prominence with the advent of powerful parallel-processing hardware.
Python Role in Neural networks
Python is a general-purpose programming language that is used for data analysis, statistics, and machine learning. Python is considered to be the best programming language for artificial neural networks due to its high scalability and efficiency.
Python Libraries
There are many interesting Python libraries used in Artificial Neural Networks. Let’s take a look at some of them below.
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PyTorch is one of the most popular Python libraries for developing deep learning models. It is very fast and flexible, making it easier to train bigger neural networks than other frameworks.
TensorFlow is another popular Python library used in Artificial Neural Network development. Its goal is to be a general-purpose machine learning library, which can run on both CPUs and GPUs, in order to provide the best performance possible in every situation.
Theano is an open-source library that lets you define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently at runtime with automatic differentiation (backpropagation). Theano is a Python library that provides a fast and efficient way to calculate mathematical expressions on multi-core CPUs and GPUs
Keras is a high-level neural networks API, written in Python and capable of running on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation.
Scikit-learn provides tools for machine learning in the Python programming language. Its algorithms are implemented in the form of estimators, which are easy to use building blocks that can be combined to fit different needs.
Many More Libraries are present and it continuously supported and developed by the Python Community
CodeGeeko.com — Python Teaching
CodeGeeko is a company that teaches Artificial Intelligence to kids in Python programming. They have designed this program for kids aged between 8–14 years so they can have the necessary skill set to be able to code. This program has been designed in collaboration with professional programmers so it focuses on teaching children the basic skill set required for future careers in coding. The way CodeGeeko works is by teaching kids about Artificial Intelligence and how it’s used in the real world, and then teaching them how to code using Python programming languages. (www.codegeeko.com)
