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Before we learn Back Propagation Neural Network (BPNN), let’s understand:

What is Artificial Neural Networks?

A neural network is a group of connected I/O units where each connection has a weight associated with its computer programs. It helps you to build predictive models from large databases. This model builds upon the human nervous system. It helps you to conduct image understanding, human learning, computer speech, etc.

What is Backpropagation?

Backpropagation is the essence of neural network training. It is the method of fine-tuning the weights of a neural network based on the error rate obtained in the previous epoch (i.e., iteration). Proper tuning of the weights allows you to reduce error rates and make the model reliable by increasing its generalization.

Backpropagation in neural network is a short form for “backward propagation of errors.” It is a standard method of training artificial neural networks. This method helps calculate the gradient of a loss function with respect to all the weights in the network.

In this tutorial, you will learn:

How Backpropagation Algorithm Works

The Back propagation algorithm in neural network computes the gradient of the loss function for a single weight by the chain rule. It efficiently computes one layer at a time, unlike a native direct computation. It computes the gradient, but it does not define how the gradient is used. It generalizes the computation in the delta rule.

Consider the following Back propagation neural network example diagram to understand:

How Backpropagation Algorithm Works

Inputs X, arrive through the preconnected path

Input is modeled using real weights W. The weights are usually randomly selected.

Calculate the output for every neuron from the input layer, to the hidden layers, to the output layer.

Calculate the error in the outputs

ErrorB= Actual Output – Desired Output

Travel back from the output layer to the hidden layer to adjust the weights such that the error is decreased.

Keep repeating the process until the desired output is achieved

Backpropagation is fast, simple and easy to program

It has no parameters to tune apart from the numbers of input

It is a flexible method as it does not require prior knowledge about the network

It is a standard method that generally works well

It does not need any special mention of the features of the function to be learned.

A feedforward neural network is an artificial neural network where the nodes never form a cycle. This kind of neural network has an input layer, hidden layers, and an output layer. It is the first and simplest type of artificial neural network.

Two Types of Backpropagation Networks are:

Static Back-propagation

Recurrent Backpropagation

It is one kind of backpropagation network which produces a mapping of a static input for static output. It is useful to solve static classification issues like optical character recognition.

Recurrent Back propagation in data mining is fed forward until a fixed value is achieved. After that, the error is computed and propagated backward.

The main difference between both of these methods is: that the mapping is rapid in static back-propagation while it is nonstatic in recurrent backpropagation.

In 1961, the basics concept of continuous backpropagation were derived in the context of control theory by J. Kelly, Henry Arthur, and E. Bryson.

In 1969, Bryson and Ho gave a multi-stage dynamic system optimization method.

In 1974, Werbos stated the possibility of applying this principle in an artificial neural network.

In 1982, Hopfield brought his idea of a neural network.

In 1986, by the effort of David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams, backpropagation gained recognition.

In 1993, Wan was the first person to win an international pattern recognition contest with the help of the backpropagation method.

Simplifies the network structure by elements weighted links that have the least effect on the trained network

You need to study a group of input and activation values to develop the relationship between the input and hidden unit layers.

It helps to assess the impact that a given input variable has on a network output. The knowledge gained from this analysis should be represented in rules.

Backpropagation is especially useful for deep neural networks working on error-prone projects, such as image or speech recognition.

Backpropagation in neural network can be explained with the help of “Shoe Lace” analogy

Not enough constraining and very loose

Too much constraint (overtraining)

Taking too much time (relatively slow process)

Higher likelihood of breaking

Discomfort (bias)

The actual performance of backpropagation on a specific problem is dependent on the input data.

Back propagation algorithm in data mining can be quite sensitive to noisy data

You need to use the matrix-based approach for backpropagation instead of mini-batch.

A neural network is a group of connected it I/O units where each connection has a weight associated with its computer programs.

Backpropagation is a short form for “backward propagation of errors.” It is a standard method of training artificial neural networks

Back propagation algorithm in machine learning is fast, simple and easy to program

A feedforward BPN network is an artificial neural network.

Two Types of Backpropagation Networks are 1)Static Back-propagation 2) Recurrent Backpropagation

In 1961, the basics concept of continuous backpropagation were derived in the context of control theory by J. Kelly, Henry Arthur, and E. Bryson.

Back propagation in data mining simplifies the network structure by removing weighted links that have a minimal effect on the trained network.

It is especially useful for deep neural networks working on error-prone projects, such as image or speech recognition.

The biggest drawback of the Backpropagation is that it can be sensitive for noisy data.

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Top 10 Cheat Sheets For Data Analytics, Neural Network, And Machine…

For a deep understanding of any concept in 2023, clean sheets are essential

In the global tech market, cutting-edge technologies like artificial intelligence, neural net, machine learning, and data analytics are flourishing. ML professionals need cheatsheets to get a deeper understanding of the details. It is not easy to grasp these technologies in a short time. Advanced mechanisms make datasets and machinery concepts more complicated. ML cheatsheets, data analysis cheatsheets, and neuron cheatsheets are all necessary to be successful in this highly competitive market. Let’s look at the top ten cheatsheets for data analytics and neural networks in order to be successful in 2023.

It is important to be aware of the different layers in a neural network. The clean sheet for neural networks consists of three layers that can be used to help remember the smallest details of these networks. It includes an input layer and a hidden layer. Through the input layer, inputs are placed in the model. These inputs are processed by hidden layers, while the processed data can be accessed at the output layer.

It is important to have a clean sheet of neural network graphical representations. This includes topics like modeling physics systems, predicting protein interface, and non-structural data. This makes it easier to recall information quickly and effectively.

You will need to include multiple formulae that cover important concepts like linear vector spaces, linear independence, and Gram Schmidt Orthogonalization.

Top 10 Neural Network Jobs To Apply For In June 2023

Neural network concept has its roots in AI and is gaining popularity in the development of trading systems

A neural network is a collection of algorithms that are roughly fashioned after the human brain and are developed to recognize patterns. They can interpret data by employing machine perception, grouping, or categorizing raw input. They recognize patterns in vectors of real numbers, into which all real-world data, whether text, sound, time series, or images, is expected to be transformed. Using intelligent automation to accelerate a company’s growth could be the smartest option business leaders can make to stay ahead of the competition. A neural network can adapt to changing input; so the network generates the best possible result without needing to redesign the output criteria. The concept of the neural network, which has its roots in artificial intelligence, is swiftly gaining popularity in the development of trading systems. Let’s look at the top neural network jobs to apply for in June 2023.

Barclays

Pune, Maharashtra

Responsibilities

Interpret synergies between pieces of intelligence to identify potential changes to the cybercrime threat landscape

Identify and disseminate well-formulated recommendations to teams across the bank to mitigate the loss to the bank and its customers

Help to maintain strong relationships with industry partners and law enforcement colleagues to enable Barclays to tackle cybercrime at an industry level

Support the team while conducting deep-dive analysis on both successful and near-miss fraud cases to draw out cyber-enabled fraud trends and identify required changes to the banks’ fraud control systems

Specialist, Digital GP&L

Chennai, Tamil Nadu

Responsibilities:

Identify use cases – work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions

Processing, cleansing, and verifying the integrity of data used for analysis

Data mining using state-of-the-art methods

Selecting features, building and optimizing classifiers using machine learning techniques

Creating visualizations of complex data sets for easy understanding by business partners

Doing ad-hoc analysis and presenting results in a clear manner

Enhancing data collection procedures to include information that is relevant for building analytic systems

Creating automated anomaly detection systems and constant tracking of its performance

Analyze, Manipulate, and Validate data using SQL, R, Python, and other analytical tools

Develop, test, and pilot your solutions

Intel Corporation

Bengaluru, Karnataka

Responsibilities:

Work with the cross-functional teams; including product management, engineering, and sales to develop comprehensive communications plans around new framework-specific product features

Help define the content strategy by channel

email nurtures, community alliances, social and video platforms

Manage agency relationships to ensure successful delivery of content or marketing activity

Citi

Mumbai, Maharashtra

Responsibilities:

This role will support consumer risk scoring-related modeling, documentation, ad-hoc analytics, automation related activities for certain consumer portfolios including credit cards, loans, and others under the guidance of a senior manager. Ideal candidates will have 2-4 years of analytics experience covering data pulling/manipulation, predictive modeling, coding, and analysis.

Skills:

Strong knowledge of Python, and SAS.

Preferred knowledge of SQL, R, big data tools such as PySpark, Hadoop, Hive, etc

Knowledge of scoring model development, credit risk, lending

Experience in data manipulation, processing, and predictive modeling such as regression, decision trees, gradient boosting machines, random forests, neural networks, etc

Huquo Consulting Pvt. Ltd

Gurgaon/Gurugram

Responsibilities:

Utilizing Natural Language Processing tools and libraries to extract valuable business data from documents

Applying Machine Learning /Deep Learning techniques to analyze the extracted data

Adjustment of the models and algorithms to improve the accuracy of the result

Sharing technical expertise with the team brings new practices and techniques

Integration of the developed application with producers and consumers of data

Break down large or complex data science projects into meaningful subprojects

Ensure a common understanding and agreement on data science project scope and goals and any subsequent changes

Micron Technology

Hyderabad, Telangana

Responsibilities:

Develop control structures within a simple environment to ensure the accuracy and quality of data through all upstream and downstream data channels

Provide thought leadership and participate in projects that involve any of the upstream or downstream data flows and processes

Ensure controls are in place over applications to ensure data integrity by performing data integrity gap analysis

Coordinate the resolution of data integrity gaps by working with the business owners and IT

Work with business partners to gather and understand functional requirements, develop complex queries and provide reports

PwC India

Kolkata, WB

Requirements:

Understanding of functional process and business objectives. Ability to engage with clients to understand current and future business goals and translate business problems into analytical frameworks

Excellent programming skills in Python. Strong working knowledge of Python’s numerical data analysis and AI frameworks such as NumPy, Pandas, Scikit-learn, Jupyter, etc

Knowledge of predictive/prescriptive analytics including Machine Learning algorithms (Supervised and Unsupervised), Deep Learning algorithms, and Artificial Neural Networks

Experience with Natural Language Processing and Text Analytics for information extraction, parsing, and topic modeling

OptumLabs

Responsibilities:

Produce innovative solutions driven by exploratory data analysis from unstructured, diverse datasets

Analyze large amounts of data and identify anomalies, recognize patterns, and provide business-level insights

Work with multiple sources of data, data structures, and pipelines and effectively form a combined dataset powerful enough for modeling

TMF Group

Responsibilities

Understanding Business processes and the existing ecosystem

Understand Business KPIs, metrics, and their calculations

Analyze, understand, and model data being used in the process and (re)define process flows, reporting, and data visualization that improves efficiency in how we deliver outputs and insights to Business users

Perform Descriptive, Diagnostic, and Predictive analytics.

Identify key insights and relations from data in the context of the Business scenario

Share analysis with Business users

Work collaboratively with Business users to deliver insights in an agile manner;

Design high-quality reports and dashboards selecting the right types of visuals and presentations considering the best user experience

Seven Consultancy (HR Solution)

Responsibilities:

Consulting with managers to determine and refine machine learning objectives.

Designing machine learning systems and self-running artificial intelligence (AI) software to automate predictive models.

Transforming data science prototypes and applying appropriate ML algorithms and tools.

Ensuring that algorithms generate accurate user recommendations.

Turning unstructured data into useful information by auto-tagging images and text-to-speech conversions.

Solving complex problems with multi-layered data sets, as well as optimizing existing machine learning libraries and frameworks.

Build And Automate Machine Learning

Intel keeps on eating up new businesses to work out its machine learning and AI tasks.

In the most recent move, TechCrunch has discovered that the chip goliath has obtained chúng tôi an Israeli organization that has manufactured and works a platform for information scientists to assemble and run machine learning models, which can be utilized to prepare and follow various models and run examinations on them, construct proposals and the sky is the limit from there.

Intel affirmed the securing to us with a short note. “We can affirm that we have procured Cnvrg,” a representative said.

Also read: Top 10 Trending Technologies You should know about it for Future Days

Intel isn’t uncovering any monetary terms of the arrangement, nor who from the startup will join Intel.

Cnvrg, helped to establish by Yochay Ettun (CEO) and Leah Forkosh Kolben, had raised $8 million from financial specialists that incorporate Hanaco Venture Capital and Jerusalem Venture Partners, and PitchBook gauges that it was esteemed at around $17 million in its last round.

It was just seven days back that Intel made another procurement to help its AI business, additionally in the region of machine getting the hang of demonstrating: it got SigOpt, which had built up a streamlining platform to run machine picking up displaying and reenactments.

Also read: Top 10 Programming Languages for Kids to learn

Cnvrg.io’s platform works across on-reason, cloud and half and half conditions and it comes in paid and complementary plans (we covered the dispatch of the free help, marked Core, a year ago).

It contends with any semblance of Databricks, Sagemaker and Dataiku, just as more modest activities like chúng tôi that are based on open-source structures.

Cnvrg’s reason is that it gives an easy to use platform to information scientists so they can focus on formulating calculations and estimating how they work, not fabricating or keeping up the platform they run on.

While Intel isn’t saying much regarding the arrangement, it appears to be that a portion of a similar rationale behind a week ago’s SigOpt procurement applies here also: Intel has been pulling together its business around cutting edge chips to more readily contend with any semblance of Nvidia and more modest players like GraphCore.

So it bodes well to likewise give/put resources into AI apparatuses for clients, explicitly administrations to help with the process stacks that they will be running on those chips.

It’s prominent that in our article about the Core complementary plan a year ago, Frederic noticed that those utilizing the platform in the cloud can do as such with Nvidia-improved holders that sudden spike in demand for a Kubernetes bunch.

Also read: Best 12 Vocabulary Building Apps for Adults 2023?

Intel’s attention on the up and coming age of figuring expects to balance decreases in its inheritance activities. In the last quarter, Intel announced a 3% decrease in its incomes, driven by a drop in its server farm business.

It said that it’s anticipating the AI silicon market to be greater than $25 billion by 2024, with AI silicon in the server farm to be more prominent than $10 billion in that period.

Data Interoperability & Machine Learning In 2023 & Beyond

Modern business systems are integrated, and if an AI-powered solution is added to a business’s digital mix, it needs to have the ability to work together with all the other software and tools. AI and machine learning interoperability gives that level of integration to AI-powered digital solutions. However, to achieve this interoperability, AI/ML models need to have the ability to exchange data and interact with each other. That is where data interoperability comes into play.

If you wish to achieve data interoperability in your business to leverage interoperable machine learning, this article is for you. In this article, we explore data interoperability, why it’s important for interoperable AI solutions, and what are its different types to help business leaders achieve interoperability across their digital network.

Data interoperability is the ability of two or more systems, applications, data stores, or microservices to exchange data and interact with each other. Interoperability enables data transfer between different types of data sources, ensuring that data is accessible across a variety of formats and platforms. This allows organizations to leverage data from disparate sources for things such as analytics and data visualization, data integration, and data sharing.

There are two main types of data interoperability:

Data-level interoperability: Data-level or syntactic interoperability enables data to be shared across applications and platforms.

Semantic-level interoperability: This type of interoperability allows the data to be interpreted correctly by different machine-learning systems.

Data interoperability is important for organizations because it enables data to be accessible across different formats and platforms. This helps organizations make data-driven decisions, reducing costs, increasing operational efficiency, and improving customer experience.

Organizations nowadays work with tens of thousands of data points, and it can be beneficial for them to use them in a synergistic way. Data interoperability enables AI/ML systems to communicate with other systems to produce more accurate and extensive results.

For instance, an interoperable automated invoicing system will have the option to share the invoices with other systems (procurement, inventory management, ERP, PIM systems, etc., in different formats that are compatible with those systems.

Data interoperability in the healthcare industry allows data from multiple sources (e.g., patient records, medical devices, imaging data, etc.) to be collected and shared for better diagnosis and treatment of patients.

Data interoperability in healthcare:

Data interoperability in the manufacturing industry enables data from machines, robots, and other equipment to be collected and shared for better production processes and quality control.

Data interoperability in financial services helps organizations manage risk by collecting data from a variety of sources, such as customer data, market data, transaction data, etc. This enables institutions to make smarter decisions about investments and pricing strategies.

Data interoperability in the education sector enables data from multiple sources (e.g., student data, teacher data, course data, etc.) to be collected and shared for better decision-making about curriculum planning and personalized learning.

Here is a video of how data interoperability works in an educational setting:

Data-level interoperability can be achieved through data integration platforms such as data warehouses and data lakes. These platforms allow data to be collected from different sources, stored in a single unified repository, organized, and accessed in various formats. This is necessary because it enables systems to adopt common data formats and structural protocols. The data can then be accessed and applied to various analytics tools and used for machine learning tasks.

This step helps the different systems understand each other’s data. Semantic-level interoperability can be achieved by adding information about the data (metadata) and connecting each data element to a standardized, common vocabulary. This process also involves annotating and labeling the data.

Additionally, data standards can also be used to ensure that data from multiple sources can be accessed and interpreted correctly by different AI systems. This helps organizations create uniform datasets that are consistent across various data sources.

Now, the data vocabulary needs to be established and linked to an ontology. In order to link a vocabulary to an ontology, two approaches can be used:

Data mapping: Which involves connecting data elements from different formats into one unified data structure.

Data federation: Which is a technique that allows data stored in multiple data sources to be shared and accessed as if it were stored in a single data source.

Through these standards, businesses can share relevant data without relying on another information system.

If you wish to learn more about how to collect data for interoperable systems, feel free to download our comprehensive data collection whitepaper:

If you need help finding a vendor or have any questions, feel free to contact us:

Shehmir Javaid

Shehmir Javaid is an industry analyst at AIMultiple. He has a background in logistics and supply chain management research and loves learning about innovative technology and sustainability. He completed his MSc in logistics and operations management from Cardiff University UK and Bachelor’s in international business administration From Cardiff Metropolitan University UK.

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Understand Machine Learning And Its End

This article was published as a part of the Data Science Blogathon.

Machine Learning: Machine Learning (ML) is a highly iterative process and ML models are learned from past experiences and also to analyze the historical data. On top, ML models are able to identify the patterns in order to make predictions about the future of the given dataset.

Since 5V’s are dominating the current digital world (Volume, Variety, Variation Visibility, and Value), so most of the industries are developing various models for analyzing their presence and opportunities in the market, based on this outcome they are delivering the best products, services to their customers on vast scales.

Machine learning (ML) is widely applicable in many industries and its processes implementation and improvements. Currently, ML has been used in multiple fields and industries with no boundaries. The figure below represents the area where ML is playing a vital role.

Just have a look at the Venn Diagram, we could understand where the ML in the AI space and how it is related to other AI components.

As we know the Jargons flying around us, let’s quickly look at what exactly each component talks about.

Machine Learning Process, is the first step in ML process to take the data from multiple sources and followed by a fine-tuned process of data, this data would be the feed for ML algorithms based on the problem statement, like predictive, classification and other models which are available in the space of ML world. Let us discuss each process one by one here.

Machine Learning – Stages: We can split ML process stages into 5 as below mentioned in the flow diagram.

Collection of Data

Data Wrangling

Model Building

Model Evaluation

Model Deployment

Identifying the Business Problems, before we go to the above stages. So, we must be clear about the objective of the purpose of ML implementation. To find the solution for the given/identified problem. we must collect the data and follow up the below stages appropriately.

Data collection from different sources could be internal and/or external to satisfy the business requirements/problems. Data could be in any format. CSV, XML.JSON, etc., here Big Data is playing a vital role to make sure the right data is in the expected format and structure.

Data Wrangling and Data Processing: The main objective of this stage and focus are as below.

Data Processing (EDA):

Understanding the given dataset and helping clean up the given dataset.

It gives you a better understanding of the features and the relationships between them

Extracting essential variables and leaving behind/removing non-essential variables.

Handling Missing values or human error.

Identifying outliers.

The EDA process would be maximizing insights of a dataset.

Handling missing values in the variables

Convert categorical into numerical since most algorithms need numerical features.

Need to correct not Gaussian(normal). linear models assume the variables have Gaussian distribution.

Finding Outliers are present in the data, so we either truncate the data above a threshold or transform the data using log transformation.

Scale the features. This is required to give equal importance to all the features, and not more to the one whose value is larger.

Feature engineering is an expensive and time-consuming process.

Feature engineering can be a manual process, it can be automated

Training and Testing:

Test data is used to see how well the machine can predict new answers based on its training.

used to train the model.

Training

Training data is the data set on which you train the model.

Train data from which the model has learned the experiences.

Training sets are used to fit and tune your models.

Testing

are “unseen” data to evaluate your models.

Test data: After the training the model, test data is used to test its efficiency and performance of the model

The purpose of the random state in train test split: Random state ensures that the splits that you generate are reproducible. The random state that you provide is used as a seed to the random number generator. This ensures that the random numbers are generated in the same order.

Data Split into Training/Testing Set

We used to split a dataset into training data and test data in the machine learning space.

The split range is usually 20%-80% between testing and training stages from the given data set.

A major amount of data would be spent on to train your model

The rest of the amount can be spent to evaluate your test model.

But you cannot mix/reuse the same data for both Train and Test purposes

If you evaluate your model on the same data you used to train it, your model could be very overfitted. Then there is a question of whether models can predict new data.

Therefore, you should have separate training and test subsets of your dataset.

MODEL EVALUATION: Each model has its own model evaluation mythology, some of the best evaluations are here.

Evaluating the Regression Model.

Sum of Squared Error (SSE)

Mean Squared Error (MSE)

Root Mean Squared Error (RMSE)

Mean Absolute Error (MAE)

Coefficient of Determination (R2)

Adjusted R2

Evaluating Classification Model.

Confusion Matrix.

Accuracy Score.

Deployment of an ML-model simply means the integration of the finalized model into a production environment and getting results to make business decisions.

So, Hope you are able to understand the Machine Learning end-to-end process flow and I believe it would be useful for you, Thanks for your time.

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