Keras is a high-level deep learning framework that provides an easy-to-use interface for building, training, and deploying deep learning models. It is written in Python and can run on top of popular deep learning backends like TensorFlow, Theano, and Microsoft Cognitive Toolkit (CNTK). Keras is designed with user-friendliness, modularity, and extensibility in mind, making it a go-to tool for researchers, engineers, and developers exploring artificial intelligence and machine learning.

What is Keras?

Keras simplifies building deep learning models by abstracting the complexities of backend computations. It allows users to focus more on designing and iterating models rather than dealing with the low-level details of tensor operations. Some of its key characteristics include:

• User-Friendly: Offers a clean and intuitive API for fast prototyping.
• Extensibility: Supports custom layers, metrics, and loss functions.
• Cross-Platform Compatibility: Runs seamlessly on CPUs, GPUs, and TPUs.
• Wide Adoption: Used in academic research, industrial applications, and startups worldwide.

Top 10 Use Cases of Keras

1. Image Classification: Keras is widely used for training models that classify images into categories, such as identifying objects in pictures or detecting facial expressions.
2. Natural Language Processing (NLP): Used for tasks like text classification, sentiment analysis, and language translation.
3. Speech Recognition: Enables the development of speech-to-text systems and voice assistants.
4. Recommendation Systems: Powers personalized recommendations, such as those used by e-commerce and streaming platforms.
5. Healthcare Applications: Assists in medical imaging diagnostics, such as detecting anomalies in X-rays or MRIs.
6. Anomaly Detection: Detects fraudulent activities, such as credit card fraud, and irregular patterns in financial transactions.
7. Time Series Analysis: Used for forecasting trends, stock prices, and weather patterns.
8. Autonomous Driving: Facilitates perception and decision-making systems for self-driving cars.
9. Generative Models: Enables the creation of realistic images, videos, and music using GANs (Generative Adversarial Networks).
10. Robotics: Helps robots learn motor skills, object recognition, and navigation.

What are the Features of Keras?

1. Modularity: Components like layers, loss functions, and optimizers are fully modular and easily configurable.
2. Pretrained Models: Provides access to a library of pretrained models such as VGG, ResNet, and Inception for transfer learning.
3. Multiple Backend Support: Works with TensorFlow, Theano, or CNTK for flexibility in deployment.
4. Extensive Documentation: Offers detailed guides and examples for beginners and advanced users.
5. Customizability: Supports building custom neural network architectures and layers.
6. Built-in Support for GPUs: Accelerates model training by utilizing GPU hardware.
7. Integration with Other Libraries: Compatible with NumPy, Pandas, and Matplotlib for preprocessing and visualization.
8. Easy Debugging: Errors are reported with clear and helpful messages, simplifying the debugging process.

How Keras Works and Its Architecture

Keras provides a high-level abstraction for building neural networks. Its architecture consists of the following key components:

1. Models:
   • Sequential API: Allows building models layer-by-layer.
   • Functional API: Facilitates creating complex models with multiple inputs and outputs.
2. Layers: Layers are the building blocks of Keras models and can be stacked to create a neural network.
3. Backend Engine: Keras uses a backend engine (e.g., TensorFlow) to perform numerical computations.
4. Loss Functions: Specifies the objective that the model should optimize.
5. Optimizers: Algorithms like SGD, Adam, and RMSProp adjust model weights to minimize the loss function.
6. Metrics: Evaluate the model’s performance during training and testing.

How to Install Keras

To install Keras, follow these steps:

1.Install a Backend Framework: Keras requires a backend engine. You can install TensorFlow, JAX, or PyTorch. For example, to install TensorFlow, use:

pip install tensorflow

2.Install Keras: Once the backend is installed, install Keras using pip:

pip install keras

3. Verify Installation: To confirm successful installation, run the following in Python:

import keras
print(keras.__version__)

This should display the installed Keras version without errors.

Basic Tutorials of Keras: Getting Started

To get started with Keras, consider the following steps:

1.Import Necessary Libraries:

import keras
from keras.models import Sequential
from keras.layers import Dense

2.Load and Preprocess Data: Keras provides utilities to load datasets like MNIST. Preprocess the data by normalizing and reshaping as required.

3. Build the Model:

model = Sequential()
model.add(Dense(128, activation='relu', input_shape=(input_dim,)))
model.add(Dense(10, activation='softmax'))

4. Compile the Model: Specify the optimizer, loss function, and metrics:

model.compile(optimizer='adam', loss='sparse

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Hi dear, welcome to the fascinating way of Jira’s concept of learning. Today I will tell you about the creation of projects in Jira. This is the most common and important feature because if you are using Jira then it is very important for you to know how to create the project in Jira so that you can start your journey with Jira. You will see what is the agenda in this article? So, in this article, we will learn how to create a project, what the classic project is and how to work with that, and how to create boards so, we will start from the first one. You will see first how to create projects so, let’s go to the cloud instance. There will be the cloud instance that you have created. In this section, you will see there is the default page of a Jira. If you want to change your default page in Jira then you can do this I will explain the setting later in this course in the article

How to create a project?

We will, we are going to create the projects so, you will go into the project section and you will see, you will haven’t create any project yet so, it will show you currently have no projects so for creating the project you will click on the create project button and you will see, it gives you options to enter your project name and to choose the template right now it’s showing the scrum template but if you want to change your project template then you can simply click on the change template and you will get a load of options available. There, you can choose according to your requirements the Kanban, scrum, task tracking, and bug tracking templates and many more are available there but you will use the scrum template so. You will go with that scrum one and select that scrum one. You will mention the project name and project name is project a. Once you will click on the advanced option you will see the key of a project. The key of a project should be very descriptive and easy to use. What is the logic behind the project key? It takes the first letter of our project name and if you want to name and if you want to change the project key then you would recommend changing it at the time of the creation of the projects because it is very difficult to change the project T later once you will create the project and adding issues or your requirements in the project so I would recommend you to change your project T then please change at the time of the creations so you will go and click on create button it will land you in that page where you will see the backlog options in the backlog you can create your requirements in the form of a story in the form of the task and you can create the epics.

I will explain to you what are the effects stories and tasks you can see the other issues types are available story tasks bugs and there are the options of creating the epic so, what are these issues types? So, you will see, what are options are available in that project you will see when you will create the project in Jira then it creates automatically a bowl. You can say this is a PA board if you want to create your own board then you will click on the plus icon and you can create the board it could be a scrum or it could be a Kanban board it’s up to you but for now, you will cancel it and the second option is a backlog as I told you, you can collect your requirements in the backlog and the third one is the active sprint then you don’t have any experience available there.      

What is a classic project?

So, it will look like as there are the three columns are available to do in progress and then I will explain to you how you can change these columns and how can you add more columns in this active sprint board and there are report options are available. As you will see, there are a lot of reports are available burndown charts, burnup charts sprint reports velocity, and the reports which are related to the issue analysis forecast and management and the others. You will see, there are no available sprints for this board that’s why this is the reason it will not show any report there so once you will start your sprint then how can you start your sprint? You will be able to see the report there so, you will back to the other one. You will see relays this is the best option. With the help of these release options you can create the versions and how you will create diversion you can manage your releases there next option is issues and filters all issues that you will create in that project will be listed down there so, you can choose all issues and it will show you all the issues in a list. I will show you how but this time you don’t have any issues so, you cannot see it and you will see there the other options are available. You will see open issues your reported issues recently viewed issues or recently created resort or updated issues as well so, I shall show you once you will create issues in the project and the next project is pages. You can show any pages which are related to your project there and the next one is the components. You can create the components and you can categorize your issues as for the components that are the best feature in the Jira and you can add the other items may be.

If you will click on that then you can add the repose repository link pages Ning or any shortcut which are frequently used so you will go into the last one and which is a project setting in that option that contains a lot of features. So first, we will start from the details you will see that you can change the project name and the project key there but you will be able to see that option because you will be admin and have access to change that project key but you will haven’t admin access then you can’t change it you can mention any URL but you can’t change the project type. If you want to change the project type then you cannot do it there for this you will have to create a new project and move your all the issues on that particular project type and there you can select an image and you may be put your project low logo and any other related images. You can write their description and mention the project lead there and you can set the default assignee as well the next option is people. In this option, you can add the people which are going to work into your project. So simply, you can add the people by clicking that arrow options you can choose the role and type the name then it will show automatically has done. I will explain to you later how you will create the roles and how can you add more team members? So that you can add the people to your project. The third one is a summary where you will be able to see the complete summary of your project. 

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Senior Director Wang Feng to Outline Smart “Analysis” Manufacturing Strategy for China at Annual Semicon China Tradeshow

SHENZHEN, China–(BUSINESS WIRE)–#AI–BISTel, the world leader in engineering automation systems (EES) and AI applications for semiconductor and flat panel display smart manufacturing announced today that it will provide a preview to its smart manufacturing strategy for China at the annual Semicon China exposition in Shanghai, March 17-19, 2021. Called Big Data-Driven Smart Analytical Solutions Designed for China’s Semiconductor Manufacturing, Senior Director, Wang Feng, will unveil a wave of smart analysis solutions designed for China’s fast growing semiconductor manufacturing market.

The featured presentation from Feng also showcases updates to BISTel’s latest FDC system, the industry’s leading real-time fault detection solution; Trace analytics will feature an updated eDatalyzer® suite for root cause analysis based on machine learning that quickly solves yield issues in semiconductor production. Besides these two key developments, BISTel will demonstrate the benefits of a cloud-based solutions for semiconductors, including, a next-generation FDC systems called dynamic fault detection (DFD) that removes costly infrastructure and deployment costs, offering China’s semiconductor manufacturers access to the most advanced FDC technology at fraction of the cost. In back-end test, assembly and packaging manufacturing, BISTel new Optimus Edge solutions will feature the first Edge devices for semiconductor packaging and assembly wire bonding applications that cut infrastructure costs and improve machine performance.

“We are pleased to announce semiconductor smart analysis solutions designed specifically to meet the needs of China’s growing semiconductor market,” said W.K. Choi, CEO, BISTel. Whether on-premises or in the cloud, BISTel China is offering chipmakers a comprehensive range of process control automation solutions that improves yields, reduces costs and provides Chinese growing semi-industry access to the latest technologies to improve semiconductor manufacturing,” added Choi.

BISTel is a leading provider of equipment engineering systems (EES) and real-time, A.I. applications for smart manufacturing. BISTel’s intelligent manufacturing solutions collect and manage data, monitor the health of equipment, optimize process flows, analyze large data, quickly identify root cause failures to mitigate risk, predict issues before they occur and extend the life of equipment through industry leading predictive analytics. BISTel helps customers reduce downtime, improve yield, increase equipment utilization and achieve significant production and engineering efficiencies across the factory. Founded in 2000, BISTel has more than 395 employees worldwide. BISTel has extensive domain expertise in global manufacturing, including semiconductor, flat panel, and PCB/SMT manufacturing as well as automotive, energy and pharmaceutical manufacturing. 

The post BISTel Features Big Data-Driven Smart Analytical Solutions Designed for China’s Semiconductor Manufacturing Sector at Semicon China 2021 appeared first on Artificial Intelligence.

Recently, Microsoft held its annual global IT conference, Microsoft Ignite 2021, virtually. The event announced a number of AI-based features and updates across the cloud platform Azure and other business applications. 

Like every year, the session started with a keynote speech from CEO Satya Nadela. His talk focused on the critical role of the public cloud.

During the keynote speech, Nadella touched upon five key attributes that will drive the next generation of innovation in cloud: 

• Ubiquitous and decentralised computing
• Empowered creators & communities at every place
• Expanded economic opportunity for every member of the global workforce
• Sovereign data and ambient intelligence
• Trust by design

Let’s look at all the machine learning updates and features announced at the recent Ignite 2021 event. 

Alex Kipman, Mixed Reality Technical Fellow at Microsoft, unveiled Microsoft Mesh — a new mixed-reality platform powered by Azure. Microsoft Mesh enables presence and shared experiences from anywhere on any device through mixed reality applications. With the Microsoft Mesh SDK, you can build collaborative, cross-platform mixed reality applications faster using AI-powered tools for avatars, holoportation, spatial rendering, and more. Mesh contains tools to enable holographic rendering, immersive experiences, spatial maps, and multi-user sync. 

Microsoft has announced the public preview of Azure Percept. Percept is a platform of hardware and services that intends to simplify the ways in which customers can use Azure AI technologies on the edge, including taking advantage of Azure cloud offerings such as device management, AI model development and analytics. 

Azure Percept will assist customers in building and managing edge AI solutions. The platform intends to simplify the process of developing, training and deploying edge AI solutions. Roanne Sones, Corporate Vice President at Microsoft’s edge and platform group, said the goal of this new solution is to give customers a single, end-to-end system, from the hardware to the AI capabilities, that “just works” without a lot of technical know-how.

Azure Purview

To enable customers to discover and govern data, Microsoft introduced its unified governance service called Azure Purview. Users can now use Azure Purview to scan Azure Synapse workspaces across serverless and dedicated SQL pools.

Azure Arc is a set of technologies that extends Azure management and services to any infrastructure. The developers at Microsoft created Azure Arc to enable the flexibility and agility customers are seeking to innovate anywhere while providing governance and security. Azure Arc enables customers to run Azure services in any Kubernetes environment, whether it’s on-premises, multi-cloud or at the edge. At Ignite 2021, the team announced the expansion of  Azure Arc enabled services to include Azure Machine Learning. Now, users can enable the Azure Machine Learning agent to run on any OSS Kubernetes cluster that Azure Arc supports.

Azure Synapse Pathway

Azure Synapse Pathway helps customers simplify their migration experience to Azure Synapse. With this tool, users can now scan their source systems and automatically translate their existing scripts into TSQL. Azure Synapse Pathway will support customers migrating from Teradata, Snowflake, Netezza, AWS Redshift, SQL Server, and Google BigQuery. 

Semantic Search

Semantic Search on Azure Cognitive Search is said to offer the best combination of search relevance, developer experience, and cloud service capabilities available on the market. At the heart of Azure Cognitive Search is its full text, keyword-based search engine built on the BM25 algorithm. With Semantic Search, users can avail a ranking algorithm that will use deep neural networks to rank the articles based on how “meaningful” they are relative to the query. 

Form Recognizer is an Azure Cognitive Service and an AI-powered document extraction service that understands any document. The service applies advanced machine learning techniques to accurately extract the text, key or value pairs as well as tables from documents. The document extraction service has several features, such as simple extraction, customised results, flexible deployment, among others.

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Microsoft will launch a raft of new features for its Azure Stream Analytics platform in preview next week, though you’ll have to wait at least a few weeks for general availability.

Microsoft pitches Azure Stream Analytics as a platform for quickly developing and deploying complex serverless analytics on data streams.

Top of the list of new features is online scaling, which means users no longer have to stop and restart a Stream Analytics job to change the number of Streaming Units allocated to a job. Streaming units represent the amount of compute resources allocated to a job.

As Microsoft puts it, “This builds on the customer promise of long-running mission-critical pipelines that Stream Analytics offers today.”

Also new is the ability to implement custom deserializers in C#, “which can then be used to de-serialize events received by Azure Stream Analytics.” At the same time, developers can now create Stream Analytics modules that write or reuse custom C# functions and invoke them right in the query through User Defined Functions. Both of these changes will give developers more to play with when dealing with IoT or other edge applications.

Other new features include the ability to debug query steps in Visual Studio, and to carry out local testing on live data in Visual Studio code, and the option of of managed identity authentication via PowerBI.

The public preview of all these features will open on November 4, with general availability coming some weeks later.

Looking a little further ahead, Microsoft has pencilled in a public preview of the ability to analyze ingress data from Event Hubs or IoT Hub on Azure Stack, and egress the results to a blob storage or SQL database  in January 2020. 

And if you like playing secret squirrel, you might like to know the company is soliciting signups for a Private Preview for real time scoring courtesy of Azure Machine Learning. The service will be based on custom pre-trained models managed by Azure Machine Learning, and hosted in either Azure Kubernetes Service or Azure Container Instances. Models can be built with a range of Python libraries, including Scikit-learn, PyTorch, TensorFlow, and trained on platforms, including Azure Databricks, Azure Machine Learning Compute, and HD Insight.

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