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Global Artificial Neural Network (ANN) Market Set to Reach $296M by 2024 | Company Profiles, Business Overviews, Product Offerings, Recent Developments, and Market Strategies

ANN solutions help organizations to perform such cognitive functions as problem-solving and machine learning.Manufacturing segment to grow at the highest CAGR during the forecast periodThe ANN market by industry vertical has been segmented into Banking, Financial Services, and Insurance (BFSI), telecommunication and IT, retail and eCommerce, healthcare and life sciences, manufacturing, government and defense, transportation and logistics, and others (media and entertainment, travel and hospitality, and education).

The manufacturing segment is projected to grow at the highest CAGR during the forecast period, owing to the growing demand to predict the mechanical proprieties of processed products based on given technological parameters.APAC to grow at the highest CAGR during the forecast periodAsia Pacific (APAC) is expected to grow at the highest CAGR during the forecast period.

Introduction 1.1 Objectives of the Study1.2 Market Definition1.3 Market Scope1.4 Years Considered for the Study1.5 Currency Considered1.6 Stakeholders2 Research Methodology 2.1 Research Data2.1.1 Secondary Data2.1.2 Primary Data2.1.2.1 Breakup of Primary Profiles2.1.2.2 Key Industry Insights2.2 Market Breakup and Data Triangulation2.3 Market Size Estimation2.3.1 Top-Down Approach2.3.2 Bottom-Up Approach2.4 Market Forecast2.5 Microquadrant Research Methodology2.5.1 Vendor Inclusion Criteria2.6 Assumptions for the Study2.7 Limitations of the Study3 Executive Summary 4 Premium Insights 4.1 Attractive Opportunities in the Artificial Neural Network Market4.2 Market Share By Region4.3 Market By Industry Vertical and Region4.4 Best Market to Invest, By Region, 20195 Market Overview and Industry Trends 5.1 Introduction5.2 Market Dynamics5.2.1 Drivers5.2.1.1 Enhanced Processing Power, Learning Ability, and Speed of Neural Networks to Drive the Growth of the Market5.2.1.2 Increasing Demand to Detect Complex Non-linear Relationships Between Variables and Recognize Patterns in Big Data5.2.1.3 Demand to Train Large Volumes of Data Sets With Low Supervision to Drive the Market5.2.2 Restraints5.2.2.1 Difficulty in Tracking the Outcomes of In-Process Stages5.2.3 Opportunities5.2.3.1 Increasing use of ANN in IoT and Data Analytics5.2.4 Challenges5.2.4.1 Extrapolation Issues in ANN Being a Drawback to the Market5.3 Regulatory Implications5.3.1 General Data Protection Regulation5.3.2 Health Insurance Portability and Accountability Act5.4 Expected Applications of ANN-Integrated Systems5.4.1 Data Mining and Archiving5.4.2 Analytical Software5.4.3 Optimization Software5.4.4 Visualization Software5.5 Ecosystem of Artificial Neural Network5.6 Types of Artificial Neural Network5.6.1 Recurrent Neural Network5.6.2 Convolutional Neural Network5.6.3 Feedforward Neural Network6 Artificial Neural Network Market By Component 6.1 Introduction6.2 Solution6.2.1 Open-Source Software Solution to Drive the Adoption of Artificial Neural Network in Research, Forecasting/Prediction, and Visualizations Areas6.3 Platform/API6.3.1 Availability of Flexible APIS to Drive the Adoption of Artificial Neural Network in Different Industries6.4 Services6.4.1 Managed Services6.4.1.1 Increasing Need for Monitoring and Maintaining Tool Operations and Reducing Overhead Costs to Drive the Growth of Managed Services in the Market6.4.2 Professional Services6.4.2.1 Consulting6. Technicalities Involved in Implementing Artificial Neural Network Tools and Services to Boost the Growth of Consulting Services6.4.2.2 Deployment and Integration6. Growing Need to Overcome System-Related Issues Effectively to Drive the Growth of Deployment and Integration Services6.4.2.3 Support and Maintenance6. Growing Deployment of Artificial Neural Network Software to Drive the Demand for Support and Maintenance Services7 Artificial Neural Network Market By Application 7.1 Introduction7.2 Image Recognition7.2.1 Rising Need for Efficient Techniques to Process and Categorize Objects in Variety of Fields to Drive the Growth of the Image Recognition Segment7.3 Signal Recognition7.3.1 Classification and Feature Extraction Algorithm to Fuel the Growth of the Signal Recognition Application in the Market7.4 Data Mining7.4.1 Demand for Predictive Analytics to Fuel the Growth of Data Mining Application in the Market7.5 Others8 Artificial Neural Network Market By Deployment Mode 8.1 Introduction8.2 Cloud8.2.1 Cost-Effectiveness and Scalability of Cloud Deployment Mode to Boost the Growth of This Segment8.3 On-Premises8.3.1 Data-Sensitive Organizations Prefer the On-Premises Deployment Mode for Artificial Neural Network Software9 Artificial Neural Network Market By Organization Size 9.1 Introduction9.2 Small and Medium-Sized Enterprises9.2.1 Need for Viable Cloud-Based Cost-Effective Solutions to Drive the Adoption of Artificial Neural Network in Small and Medium-Sized Enterprises9.3 Large Enterprises9.3.1 Increasing Adoption of Advanced Technologies to Drive the Adoption of Artificial Neural Network in Large Enterprises10 Artificial Neural Network Market By Industry Vertical 10.1 Introduction10.2 Banking, Financial Services, and Insurance10.2.1 Growing Focus on Financial Standards and Compliance With Regulations to Drive the Market10.3 Retail and Ecommerce10.3.1 Growing Demand to Identify Customer Behavior in Real-Time to Fuel the Growth of the Retail and Ecommerce Industry Vertical10.4 Telecommunications and It10.4.1 Increasing Demand to Provide Improved Services for a Growing Customer Base to Boost the Adoption of the Market10.5 Healthcare and Life Sciences10.5.1 Growing Demand to Achieve Better Patient Experience and Personalized Treatment in Real-Time to Fuel the Growth of the Healthcare and Life Sciences Industry Vertical10.6 Manufacturing10.6.1 Growing Need to Extend the Lifespan of Factory Equipment and Reduce the Risk of Production Delays to Fuel the Growth of the Artificial Neural Network Application in the Manufacturing Industry Vertical10.7 Transportation and Logistics10.7.1 Demand to Reduce Cost and Management of the Overall Supply Chain Flow to Drive the Growth of the Market10.8 Government and Defense10.8.1 Growing Demand for Enhanced Data Security and Advanced Intelligence to Drive the Market10.9 Others11 Artificial Neural Network Market By Region 11.1 Introduction11.2 North America11.2.1 United States11.2.1.1 Government's Focus on Innovation and Research to Fuel the Adoption of Artificial Neural Network Software in the United States11.2.2 Canada11.2.2.1 Increase in Investments and Research Activities to Drive Artificial Neural Network Software and Services Adoption in Canada11.3 Europe11.3.1 United Kingdom11.3.1.1 Government Focus on Innovation and Research to Fuel the Adoption of Artificial Neural Network Software in the United Kingdom11.3.2 Germany11.3.2.1 Growing Investments By Tech-Giants to Provide Opportunities for the Development of Artificial Neural Network Software11.3.3 France11.3.3.1 Focus on R&D and Heavy Inflow of Capital From Global Players and Investors to Drive the Artificial Neural Network Market Growth in France11.3.4 Italy11.3.4.1 Government Initiatives to Increase the Adoption of Artificial Neural Network in Manufacturing and Finance Sector to Help Market Growth11.3.5 Spain11.3.5.1 Government Smart City Initiatives Contributing to the Growth of the Market in Spain11.3.6 Rest of Europe11.4 Asia Pacific11.4.1 China11.4.1.1 Increasing Focus on Integrating Artificial Intelligence and Deep Learning Technologies to Drive the Adoption of Artificial Neural Network Software in China11.4.2 Japan11.4.2.1 Existing Market and Already Adopted Technology to Boost the Market in Japan11.4.3 South Korea11.4.3.1 Government Initiatives and Support Toward the Adoption of Artificial Intelligence to Drive the Market Growth11.4.4 India11.4.4.1 Advent of Neural Network Startups in India to Drive the Growth of the Market11.4.5 Australia and New Zealand11.4.5.1 Growth in Infrastructure Developments and the Adoption of Connected Devices to Drive the Market11.4.6 Rest of Asia Pacific11.5 Middle East and Africa11.5.1 Middle East11.5.1.1 Advanced Analytics Coupled With AI Adoption to Drive the Artificial Neural Network Market in the United Arab Emirates11.5.2 Africa11.5.2.1 Transformation in the Overall Infrastructure Industry to Boost the Adoption of Artificial Neural Network Tools in South Africa11.6 Latin America11.6.1 Brazil11.6.1.1 Investments By Multinational Companies to Drive the Market Growth in Brazil11.6.2 Mexico11.6.2.1 Government Initiatives and Increasing Demand for Artificial Neural Network Software and Services to Trigger the Market Growth in Mexico11.6.3 Rest of Latin America12 Competitive Landscape 12.1 Microquadrant Overview12.1.1 Visionaries12.1.2 Innovators12.1.3 Dynamic Differentiators12.1.4 Emerging Companies12.2 Competitive Benchmarking12.2.1 Business Strategy Excellence of Major Players in the Market12.2.2 Strength of Solution Offerings of Major Players in the Market12.3 Ranking of Players, 201913 Company Profiles 13.1 Introduction13.2 Google13.3 IBM13.4 Microsoft13.5 Oracle13.6 Intel13.7 Qualcomm13.8 Alyuda13.9 Ward Systems13.10 GMDH, LLC13.11 Starmind13.12 Neuralware13.13 Neurala13.14 Clarifai For more information about this report visit Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

Build an Artificial Neural Network(ANN) from scratch: Part-1

In my previous article Introduction to Artificial Neural Networks(ANN), we learned about various concepts related to ANN so I would recommend going through it before moving forward because here I’ll be focusing on the implementation part only.

In this part-1, we will build a fairly easy ANN with just having 1 input layer and 1 output layer and no hidden layer.

However, if you really want to understand the in-depth working of a neural network, I suggest you learn how to code it from scratch using Python or any other programming language.

Before we start coding, let’s first let’s see how our neural network will execute in theory: Theory of ANN An artificial neural network is a supervised learning algorithm which means that we provide it the input data containing the independent variables and the output data that contains the dependent variable.

In the beginning, the ANN makes some random predictions, these predictions are compared with the correct output and the error(the difference between the predicted values and the actual values) is calculated.

If you look at the neural network in the above figure, you will see that we have three features in the dataset: X1, X2, and X3, therefore we have three nodes in the first layer, also known as the input layer.

The following are the steps that execute during the feedforward phase of ANN: Step 1: Calculate the dot product between inputs and weights The nodes in the input layer are connected with the output layer via three weight parameters.

Mathematically, the summation of dot product: X.W=x1.w1 + x2.w2 + x3.w3 + b Step 2: Pass the summation of dot products (X.W) through an activation function The dot product XW can produce any set of values.

Step 2: Minimize the cost Our ultimate goal is to fine-tune the weights of our neural network in such a way that the cost is minimized the minimum.

In order to minimize the cost, we need to find the weight and bias values for which the cost function returns the smallest value possible.

The above equation tells us to find the partial derivative of the cost function with respect to each weight and bias and subtract the result from the existing weights to get new weights.

To find if the cost increases or decreases, given the weight value, we can find the derivative of the function at that particular weight value.

On the other hand, if the cost is decreasing with an increase in weight, a negative value will be returned, which will be added to the existing weight value since negative into negative is positive.

We need to repeat the execution of gradient descent for all the weights and biases until the cost is minimized and for which the cost function returns a value close to zero.

we store the values from the input input_set to the inputs variable so that the value of input_set remain as it is in each iteration and whatever changes are done that must be done to inputs variable.

We know that our cost function is: We need to differentiate this function with respect to each weight and this can be done easily using chain rule of differentiation.

So our final derivative of the cost function with respect to any weight is: Now the slope can be simplified as: we have the z_del variable, which contains the product of dcost and dpred.

Instead of looping through each record and multiplying the input with the corresponding z_del, we take the transpose of the input feature matrix and multiply it with the z_del.

Once the loop starts, you will see that the total error starts decreasing and by the end of the training the error is left with a very small value.

In this article, we learned how to create a very simple artificial neural network with one input layer and one output layer from scratch using numpy python library.

Introduction to Artificial Neural Networks(ANN)

Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost.

In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound.

Models are trained by using a large set of labeled data and neural network architectures that contain many layers.

It is composed of large number of highly interconnected processing elements(neurons) working in unison to solve a specific problem.” Topics to cover: Biological Neurons (also called nerve cells) or simply neurons are the fundamental units of the brain and nervous system, the cells responsible for receiving sensory input from the external world via dendrites, process it and gives the output through Axons.

Cell body (Soma): The body of the neuron cell contains the nucleus and carries out biochemical transformation necessary to the life of neurons.

The soma processes these incoming signals over time and converts that processed value into an output, which is sent out to other neurons through the axon and the synapses.

In the above figure, for one single observation, x0, x1, x2, x3...x(n) represents various inputs(independent variables) to the network.

In the simplest case, these products are summed, fed to a transfer function (activation function) to generate a result, and this result is sent as output.

xn.wn = ∑ xi.wi Now activation function is applied 𝜙(∑ xi.wi) The Activation function is important for an ANN to learn and make sense of something really complicated.

If the input value is above or below a certain threshold, the neuron is activated and sends exactly the same signal to the next layer.

The problem with this function is for creating a binary classifier ( 1 or 0), but if you want multiple such neurons to be connected to bring in more classes, Class1, Class2, Class3, etc.

Sigmoid Activation Function — (Logistic function)A Sigmoid function is a mathematical function having a characteristic “S”-shaped curve or sigmoid curve which ranges between 0 and 1, therefore it is used for models where we need to predict the probability as an output.

The drawback of the Sigmoid activation function is that it can cause the neural network to get stuck at training time if strong negative input is provided.

The main advantage of this function is that strong negative inputs will be mapped to negative output and only zero-valued inputs are mapped to near-zero outputs.,So less likely to get stuck during training.

Rectified Linear Units — (ReLu)ReLu is the most used activation function in CNN and ANN which ranges from zero to infinity.[0,∞) It gives an output ‘x’ if x is positive and 0 otherwise.

This is simple enough but there is a way to amplify the power of the Neural Network and increase its accuracy by the addition of a hidden layer that sits between the input and output layers.

This way the neurons work and interact in a very flexible way allowing it to look for specific things and therefore make a comprehensive search for whatever it is trained for.

Learning in a neural network is closely related to how we learn in our regular lives and activities — we perform an action and are either accepted or corrected by a trainer or coach to understand how to get better at a certain task.

Based on the difference between the actual value and the predicted value, an error value also called Cost Function is computed and sent back through the system.

It is a first-order iterative optimization algorithm and its responsibility is to find the minimum cost value(loss) in the process of training the model with different weights or updating weights.

In Gradient Descent, instead of going through every weight one at a time, and ticking every wrong weight off as you go, we instead look at the angle of the function line.

If slope → Negative, that means yo go down the curve.If slope → Positive, Do nothing This way a vast number of incorrect weights are eliminated.

One thing to be noted is that, as SGD is generally noisier than typical Gradient Descent, it usually took a higher number of iterations to reach the minima, because of its randomness in its descent.

Step-6 → Repeat step-1 to 5 and update the weights after each observation(Reinforcement Learning) Step-7 → When the whole training set passed through the ANN, that makes and epoch.

They may be used for a variety of different concepts and ideas, and learn through a specific mechanism of backpropagation and error correction during the testing phase.

By properly minimizing the error, these multi-layered systems may be able to one day learn and conceptualize ideas alone, without human correction.

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