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Introduction to UpSampling2d

Upsampling2d creates an upsampling layer, also known as image interpolation. To repeat the column and rows by size zero and one, the upsampling2d function is used. The layer of inputs defined in 2d is upsampling2d. The layer of upsampling2d is effective and simple, but it does not perform learning and does not fill in useful details about the operation of upsampling.

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Key Takeaways

The conv2d transpose layer is more complex as compared to the upsampling2d layer. Both layers is performing the operation of upsampling and also interpret the upsampling data.

This layer will combine the conv2d and upsampling layers into a single layer. We need to import libraries of UpSampling2d before using it.

What is UpSampling2d?

Each layer of upsampling2d is followed by a conv2d layer that will learn to interpret the input and train to translate the useful details. GAN is a neural network architecture that was used to train the model. The architecture will be made up of the discriminator and generator models, which will be implemented using a convolutional deep neural network. The discriminator is responsible for image classification. The generator is also responsible for generating new examples of the problem domain.

The generator will work by taking the random point by using latent space as input and output for completing the image. The convolutional neural network for the classification of the image is used to pool the layers for the input image down sampling. The convolutional layer is performing sown sampling by applying the filter as per the input image or feature maps. The activation which was resulting is a map of the output feature which contains the smaller border effects.

How to Use UpSampling2d?

To use upsampling2d we need to follow below steps as follows:

We need to import the different modules while using upsampling2d by using the import keyword.


from keras.layers import UpSampling2D from numpy import asarray import tensorflow as tf from keras.models import Sequential


2. Now we are defining the input data for using upsampling2d also we are printing the context of input data as follows.


ip_data = asarray ([[1, 2], [3, 4]]) print(ip_data)


3. Now we are reshaping the data in a single sample. We are taking the input data for sampling as follows.


ip_data = ip_data.reshape((1, 2, 2, 1))


4. Now we are defining the model of it by using the model’s name as sequential as follows.


mod = Sequential() mod.add(UpSampling2D (input_shape = (2, 2, 1))) mod.summary()


5. Now in this step we are making the prediction of input data by using upsampling2d as follows.


pred = mod.predict(ip_data)


6. Now in this step we are reshaping the output for removing the channel, also we are summarizing the output as follows.

pred = pred.reshape((4, 4)) print (pred)


tf.keras.UpSampling2D and Conv2DTranspose Layers

The tensor space model is pretrained by using initialization also we are configuring the same by using different ways. Below is the syntax of keras upsampling2d as follows:



We have defined multiple arguments with the function of upsampling2d when using it. Below example shows how we can define upsampling2d as follows:


from keras.layers import UpSampling2D from numpy import asarray import tensorflow as tf import numpy as np ip_sh = (2, 2, 1, 3) ip = np.arange ( (ip_sh)).reshape(ip_sh) print (ip) op = tf.keras.layers.UpSampling2D (size=(11, 22))(ip) print (op)


Con2DTranspose Layer

This is also known as the transpose convolution layer. The need for this layer generally arises from the need to shape and output of convolution for maintaining the pattern of connectivity that was compatible with the convolution. We must provide keyword arguments as input shapes by using the model’s first layer.

The conv2d transpose layer is shown in the example below:


from keras.layers import Conv2DTranspose from numpy import asarray import tensorflow as tf import numpy as np ip_sh = (2, 2, 1, 3) ip = np.arange ( print(ip) op = tf.keras.layers.Conv2DTranspose ( filters, kernel_size, strides = (2, 2), padding = "valid", output_padding = None, data_format = None, dilation_rate = (4, 4), activation = None, use_bias = True, bias_regularizer = None, bias_constraint = None, ) print (op)

Upsampling2d Arguments

Below are the arguments for upsampling as follows. It will accept the object of the field as follows.

Size – The type of size argument is float. Row and column are an upsampling factor. It contains the numbers array.

Shape – The type of shape argument is int. It contains the instruction as an output shape. This argument is used to create the input layer which was used for inserting the same before the layer.

Name – The type of name argument is a string. This is defined as the name of the layer.

Color – The type of color argument is a color format. This argument is defined as layer color.

Close button – The type of close button argument is dict. Close button will appear as a control dict.

Init status – The type of init status argument is a string. The layer initial status of upsampling2d is open or closed.

Animetime – The type of anime time argument is int. It will define the speed of open and closed animation.

Dataformat – These arguments will determine the ordering of input data format in an input dimension.

Interpolation – It will define the mechanism of interpolation. The value of this argument is nearest.

Batch size – This argument is defined in the absence of batch input shape.

Dtype – The suppose layer is used as an input layer then this field is used as the data type.

Weights – This is defined as tensor which was defining the value of initial weights.

Batch input shape – It will define array numbers. This arguments is used in absence of batch size.

Examples of UpSampling2d

Given below are the examples mentioned:

Example #1

In the below example, we are using the function of upsampling2d as follows.


from keras.layers import UpSampling2D from numpy import asarray import tensorflow as tf from keras.models import Sequential ip = asarray([[11, 21], [31, 41]]) print(ip) ip = ip.reshape((1, 2, 2, 1)) conv = Sequential() conv.add(UpSampling2D (input_shape = (2, 2, 1))) conv.summary() pred_2d = conv.predict(ip) pred_2d = pred_2d.reshape((4, 4)) print(pred_2d)


Example #2

In below example we are defining the keras upsampling as follows.


from keras.layers import UpSampling2D from numpy import asarray import tensorflow as tf import numpy as np ip_spape = (2, 2, 1, 1) ip = np.arange ( (ip_spape)).reshape(ip_spape) print (ip) op = tf.keras.layers.UpSampling2D (size=(1, 2))(ip) print (op)



Generator will work by taking the random point by using latent space as input and output for completing the image. It is creating a layer of upsampling, which is also known as image interpolation. The upsampling2d function is used to repeat the column and rows by size zero and one.

Recommended Articles

This is a guide to UpSampling2d. Here we discussed the introduction, how to use UpSampling2d? arguments and examples. You may also have a look at the following articles to learn more –

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Is Dependency On Innovation Enough Or Decoding The Layers Of It Is Necessary?

With the emergence of technology, innovation is getting into the layers of businesses. But is the same happening the other way round? It is surprising to observe that companies these days are relying on innovation but fail in the successful development and implementation of it. Companies all across the globe are investing approximately $1 trillion on innovation per annum, but sadly at least 10 percent of it ($100 billion) is wasted. Most of the companies are trying to follow the path of big companies like Google, SpaceX and imitating their innovation trajectory. It has been observed over the years that imitation method hardly works in terms of innovation. It would beneficial for such enterprises to discover tools and structures which suits their agenda the best and satisfies their requirements, strategies, and approach. These companies can encompass some steps to get their innovation entity to act. The identification of the right kind of innovation is required to achieve a variety of goals. The range of goals can be from reducing undesirable costs and adding value to the product preventing it from possible disruption via competitors and developing futuristic models. The umbrella of innovation covers a large area including – fresh products, better backend processes, employing new-age technology to build efficient platforms and enhance customer experience. In this race, one needs to keep market status in mind too. The innovation should be market facing which involves new products, their reach to customers and potential disruption they could cause. Another aspect to bring into consideration is the convention. To cope with the disruptive market, it is not necessary to ignore conventional approach completely, sometimes traditional compliance-centric culture can also be beneficial. In order to prevent that, some companies tend to separately start their innovative unit to make it work simultaneously. Discovering the best source of innovation in quintessential. Considering the fact that the world is too small and you are no unique, you have to make an extra effort to produce unique. It is absolutely possible that the ideas on which you are working are already produced or in the process by some other organization around the world. Here you need to bring machination of developing sources into the picture. The company needs to understand where is that happening and how to generate a productive approach to have access to what is required by you. In such a situation, there can be two forms of sources – internal and external. •  Internal Sources: A lot of companies discover internal competitions which help they cultivate new ideas and concepts to drive innovation. The existing staff sometimes prove to be the needful resource for the company. Even in some cases, some employees can fetch funding for new companies in the market. •  External Sources: External sources can be created through various styles of partnerships such as the partnership with academia, acquiring new businesses or start-ups or collaborating with sponsors or innovators based on demographic approach. Companies can encourage other innovators and helping hands to work on the innovation while behaving as a backend support themselves. Additionally, they can also indulge in multi-company or industry-wide collaborations. Innovation is no magic that it will happen overnight, it also requires a process to produce outcomes like other business ideas. One needs to understand that big innovative ideas don’t get implemented themselves, they need to go through a process to general potential results. It is not necessary that the process of innovation operate as per the company’s conventional workflow, it might involve certain other procedures and tools to perform effectively.

27 Useful Keyboard Shortcuts For Layers & Layer Masks In Photoshop

While keyboard shortcuts in Photoshop may seem like a skill only used by experienced editors, anyone can (and should) learn them. The sooner you learn keyboard shortcuts, the sooner you will begin editing in Photoshop like a pro. These useful layer and layer mask shortcuts will save you time and keep your layers panel organized.

Not only do I have keyboard shortcuts for you, but you can also create custom layer mask shortcuts that suit you. Once you learn these shortcuts, you won’t go back to searching through menus to find the action you need.

– Layer Mask Shortcuts In Photoshop

Besides improving productivity, layer mask shortcuts make it easier for you to keep track of the changes you make. Here are 11 layer mask shortcuts to help improve your workflow. 

1. Creating A Custom Layer Mask Shortcut

There are many built-in layer mask shortcuts you can use in Photoshop. However, you can also make your own layer mask shortcuts too.

When the dialog box opens, make sure the shortcut type is set to ‘Application Menus’.

Then, scroll through the list of application menus to find the ‘Layer’ option.

You will notice there is a list of tasks you can create a shortcut for in the layer mask menu. In my case, I’ll do a shortcut for the ‘Link/Unlink’ layer mask task.

3. Inverting A Layer Mask – Control + I (On Windows)/Command + I (On Mac)

This command gives you a new perspective of a layer mask, making it easier to spot any flaws in a layer mask selection and correct them.

To invert the layer mask press Control+I (On Windows)/Command+I (On Mac). This will let you toggle between the ‘visible’ and ‘invisible’ elements of the layer mask.

Once inverted, you can then press Control+I (On Windows)/Command+I (On Mac) again to change the layer mask back to the original state.

This command is especially useful when you want to apply an effect to an object without affecting the other parts of the image.

To deselect the object once you are done, press Control+D (On Windows)/Command+D (On Mac).

You can copy a layer mask to use on another layer when you need the same mask, rather than creating a new layer mask for each layer.

6. Filling A Layer Mask With Foreground Color – Alt + Backspace (On Windows)/Option + Delete (On Mac)

You can find the foreground and background color selection boxes in the toolbar. By default, the foreground color is white, while the background color is black. 

When you fill a layer mask with the default foreground color by pressing Alt+Backspace (On Windows)/Option+Delete (On Mac), it turns completely white, making all content linked to the layer mask visible.

You can then use the brush tool with black as the foreground color to hide portions of an image.

7. Filling A Layer Mask With Background Color – Control + Backspace (On Windows)/Command + Delete (On Mac)

If you fill your layer mask with the default background color, which is black, all content of the layer linked to the layer mask will be hidden. 

Painting your whole layer mask black by pressing Control+Backspace (On Windows)/Command+Delete (On Mac) is useful when you want to use the white brush to bring back portions of an image only.

8. Enabling Quick Masking – ‘’ (On Windows and Mac)

When you enable the layer mask overlay, the selected object turns red, known as a quick mask.

The opacity of the layer mask overlay is set to 50% by default. You may find it easier to refine your selection by using this command. To activate it, hit the backward slash bar on your keyboard.

9. Targeting A Layer – Control + 2 (On Windows)/Command + 2 (On Mac)

Press Control+2 (Windows)/Command+2 (Mac) to edit an image linked to a layer mask rather than the mask.

10. Targeting A Layer mask – Control + (On Windows)/Command + (On Mac)

By using this shortcut, you can make changes to the layer mask, rather than the image the mask is linked to. Pressing Control+ (On Windows)/Command+ (On Mac) allows you to quickly target the layer mask of the selected layer.

11. Delete A Layer Mask – Delete (On Windows and Mac)

If you want to delete a layer mask without dragging it to the trash bin icon in the layers panel, select the layer mask(s) and press Delete on your keyboard. To select multiple layer masks at a time, hold in Control/Command while selecting each layer mask.

– Layer Shortcuts In Photoshop 

Layer keyboard shortcuts speed up simple tasks such as creating a new layer. They also help in non-routine tasks, such as changing blending modes.

12. Creating A New Layer – Control + Shift + N (On Windows)/Command + Shift + N (On Mac)

This shortcut allows you to create an empty layer by pressing Control+Shift+N (On Windows)/Command+Shift+N (On Mac). After activating the shortcut a window will open, allowing you to name the layer which will appear in your layers panel.

While selecting layers, you may not want to select them in sequence but rather select one layer on the top of the panel and the other at the bottom.

To select alternate layers, hold Control (On Windows)/Command (On Mac) while selecting the layers. 

15. Duplicating A layer – Control + J (On Windows)/Command + J (On Mac)

You can duplicate layers to compare the original version of a layer with the edited one or to blend layers. Duplicate a layer by pressing Control + J (On Windows)/Command + J (On Mac). The copied layer will be named after the original layer with “copy” added to the name.

16. Changing Layers Opacity – Press 1-9 (On Windows and Mac)

Rather than using the opacity slider in the layers panel, you can set the opacity of a layer using the numbers. This will only work when a non-brush tool is selected.

To do this, press a number between 1-9 on your keyboard. The layer opacity is set to the percentage of the number you select, for example, it sets it to 10% when you press 1. If you want to go back to 100%, hit 0.

To set an opacity with more specific percentages, press one number after the other. For example, pressing 6 and 7 will set the opacity to 67%.

17. Toggling Blending Modes – Shift and (+/-) (On Mac & Windows)

Blending modes can create a new look for your layers and enhance the effects you apply to them. To toggle through blending modes, select the desired layer, and then press the plus (+) or the minus sign (-) while holding ‘Shift’. 

18. Visualizing A Specific Layer Only – Alt + Eye Icon (On Windows)/Option+Eye Icon (On Windows) 19. Moving A Layer Up – Control + [ (On Windows)/Command + [ (On Mac)

Putting a layer on top of another layer is sometimes necessary, such as when a layer has an effect that you can apply to all layers. To move a layer above another layer, press Control/Command + [.

20. Moving A Layer Down – Control + ] (On Windows)/Command + ] (On Mac)

Press control/command + ] on your keyboard to move a layer down in the layers panel.

21. Grouping Layers – Control + G (On Windows)/Command + G (On Mac)

Grouping layers helps keep your workspace organized in the layers panel. To group layers, select the layers you want in the group and press Control + G (On Windows) or Command + G (On Mac). 

22. Ungrouping layers – Control + Shift + G (On Windows)/Command + Shift + G (On Mac) 23. Merging Layers – Command + E (On Mac)/Control + E (On Windows) 24. Selecting All Layers In The Layers Panel – Control + Alt + A (On Windows)/Command + Option + A (On Mac)

By pressing Control + Alt + A (On Windows)/Command + Option + A (On Mac) you can select all the layers in the layers panel, including layer groups. When using this shortcut the background layer will not be selected.

25. Creating Clipping Mask – Alt + Control + G (On Windows)/Option + Command + G (On Mac)

Clipping masks allow you to apply an effect to a layer without affecting others. Press Alt+Control+G (On Windows)/Option+Command+G (On Mac) to create a clipping mask. You can also remove a clipping mask with the same shortcut.

The pointing down arrow to the left of a layer thumbnail indicates that the layer is a clipping mask layer. This will only affect the layer directly below it.

26. Delete Layer – Delete Key (On Windows and Mac)

Delete any layers by pressing ‘delete’ on your keyboard. This shortcut also works for groups of layers.

Make sure the correct layer is selected before pressing delete on your keyboard. You can also delete multiple layers at once by selecting the relevant layers before pressing delete.

27. Finding layers – Alt+Shift+Control+F (On Windows)/Option+Shift+Command+F (On Mac)

This shortcut activates the search bar in the layers panel, you can then type in a specific layer name to locate it in the layers panel. This is useful if you don’t know where a layer is when there are multiple layers in the layer panel.

Now that you know these 27 keyboard shortcuts in Photoshop for layers and layer masks, let’s get into some more essential shortcuts. To learn the essential keyboard shortcuts for every aspect of Photoshop, check out my guide to the 93 most important keyboard shortcuts in Photoshop!

Happy Editing!

Apache Kafka Use Cases And Installation Guide

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


Today, we expect web applications to respond to user queries quickly, if not immediately. As applications cover more aspects of our daily lives, it is difficult to provide users with a quick response.


Caching is used to solve a wide variety of these problems, but applications require real-time data in many situations. In addition, we have data to be aggregated, enriched, or otherwise transformed for further consumption or further processing. In these cases, Kafka is helpful.

What is Apache Kafka?

It is an open-source platform that ingests and processes streaming data in real time. Streaming data is generated simultaneously by thousands of data sources every second. Apache Kafka uses a Subscribe and Publish model for reading and writing streams of records. Unlike other messaging systems, Kafka has built-in sharding, replication, higher throughput, and is more fault-tolerant, making it an ideal solution for processing large volumes of messages. More about Kafka integration

What is Apache Kafka Used For?

Kafka Use Cases are numerous and found in various industries such as financial services, manufacturing, retail, gaming, transportation and logistics, telecommunications, pharmaceuticals, life sciences, healthcare, automotive, insurance, and more. Kafka is used wherever large-scale streaming data is processed and used for reporting. Kafka use cases include event streaming, data integration and processing, business application development, and microservices. Kafka can be used in the cloud, multi-cloud and hybrid deployments. 6 Reasons to Automate Your Data Pipeline

Kafka Use Case 1: Tracking web activity

Kafka Use Case 2: Operational Metrics

Kafka can report operational metrics when used in operational data feeds. It also collects data from distributed applications to enable alerts and reports for operational metrics by creating centralized data sources of operations data.

Kafka Use Case 3: Aggregating Logs

Kafka collects logs from different services and makes them available in a standard format to multiple consumers. Kafka supports low-latency processing and multiple data sources, which is great for distributed data consumption.

Use Case 4: Stream Processing

How Does Apache Kafka Work?

It is known as an event streaming platform because you can:

• Publish, i.e., write event streams, and Subscribe, i.e., read event streams, including continuous import and export of data from other applications.

• Reliably store event streams for as long as you want.

• Processing of events stream when they occur or after.

Kafka is highly scalable, elastic, secure, and has a data-distributed publish-subscribe messaging system. The distributed system has servers and clients that work through the TCP network protocol. This TCP (Transmission Control Protocol) helps in transferring data packets from source to destination between processes, applications, and servers. A protocol establishes a connection before communication between two computing systems on a network occurs. Kafka can be deployed on virtual machines, bare hardware, on-premise containers, and in cloud environments.

Kafka’s Architecture – The 1000 Foot View

• Brokers (nodes or servers ) handle client requests for production, consumption, and metadata and enable data replication in clusters. Their several Brokers that can be more than one in a cluster.

• Zookeeper maintains cluster state, topic configuration, leader election, ACLs, broker lists, etc.

• Producer is an application that creates and delivers records to the broker.

• A consumer is a system that consumes records from a broker.

Kafka producers and consumers

Kafka Producers are essentially client applications that publish or write events to Kafka, while Kafka Consumers are systems that receive, read, and process those events. Kafka Producers and Kafka Consumers are completely separate, and they have no dependency. It is one important reason why Apache Kafka is so highly scalable. The ability to process events exactly once is one of Kafka’s guarantees.

Kafka’s themes

Kafka Records

Entries are event information that is stored in a topic as a record. Applications can connect and transfer the record to the topic. The data is durable and can be stored in the topic long until the specified retention period expires. Records can consist of different types of information – information about a web event such as a purchase transaction, social media feedback, or some data from a sensor-driven device. It can be an event that signals another event. These topic records can be processed and reprocessed by applications that connect to the Kafka system. Records can be described as byte arrays that store objects in any format. An individual record will have two mandatory attributes – key and value and two optional attributes – timestamp and header.

Apache Zookeeper and Kafka

Apache Zookeeper is software that monitors and maintains order in the Kafka system and acts as a centralized, distributed coordination service for the Kafka Cluster. It manages configuration and naming data and is responsible for synchronization across all distributed systems. Apache Zookeeper monitors Kafka cluster node states, Kafka messages, partitions, and topics, among other things. Apache Zookeeper allows multiple clients to read and write simultaneously, issue updates, and act as a shared registry in the system. Apache ZooKeeper is an integral part of distributed application development. It is used by HBase, Apache Hadoop, and other platforms for functions such as node coordination, leader election, configuration management, etc.

Use cases of Apache Zookeeper

Apache Zookeeper coordinates the Kafka Cluster. it needs Zookeeper to be installed before it can be used in production. This is necessary even if the system consists of a single broker, topic, and partition. Zookeeper has five use cases – administrator selection, cluster membership, topic configuration, access control lists (ACLs), and quota tracking. Here are some Apache Zookeeper use cases:

Apache Zookeeper chooses Kafka Controller.

A Kafka Controller is a broker or server that maintains a leader/follower relationship between partitions. Each Kafka cluster has only one driver. In the event of a node shutdown, the controller’s job is to ensure that other replicas take over as partition leaders to replace the partition leaders on the node being shut down.

Apache Zookeeper manages the topic configuration.

Zookeeper software keeps records of all topic configurations, including the list of topics, the number of topic partitions for each topic, overriding topic configurations, preferred leader nodes, and replica locations, among others.

The zookeeper Software maintains access control lists or ACLs

The Zookeeper software also maintains ACLs (Access Control Lists) for all topics. Details such as read/write permissions for each topic, list of consumer groups, group members, and the last offset each consumer group got from the partition are all available.

Installing Kafka – Several Steps Installing Apache Kafka on Windows OS

Prerequisites: Java must be installed before starting to install Kafka.

Installation – required files

To install Kafka, you will need to download the following files:

Install Apache ZooKeeper

A. Download and extract Apache ZooKeeper from the above link.

b. Go to the ZooKeeper configuration directory, and change the dataDir Path from “dataDir=/tmp/zookeeper” to “:zookeeper-3.6.3data” in the zoo_sample.cfg file. Please note that the name of the Zookeeper folder may vary depending on the downloaded version.

C. Set system environment variables, add new “ZOOKEEPER_HOME = C:zookeeper-3.6.3”.

d. Edit the system variable named Path and add;%ZOOKEEPER_HOME%bin;

E. Run – “zkserver” from cmd. Now ZooKeeper is up and running on the default port 2181, which can be changed in the zoo_sample.cfg file.

Install Kafka

A. Download and extract Apache Kafka from the above link.

b. In the Kafka configuration directory. replace the chúng tôi path from “log.dirs=/tmp/kafka-logs” to “C:kafka-3.0.0kafka-logs” in Please note that the name of the Kafka folder may vary depending on the downloaded version.

C. In case ZooKeeper is running on a different computer, edit these __ Here, we will define the private IP of the server

listeners = PLAINTEXT:// Here, we need to define the public IP address of the server}

d. Add the below properties to

E. Kafka runs as default on port 9092, and it connects to ZooKeeper’s default port which is 2181.

Running Kafka server.

A. From Kafka installation directory C:kafka-3.0.0binwindows, open cmd and run the below command.

b. The Kafka Server is up and running, and it’s time to create new Kafka Topics to store messages.

Create Kafka Topics

A. Create a new topic as my_new_topic.

b. From C:kafka-3.0.0binwindows, open cmd and run the command below:

Commands For Installing Kafka

Kafka Connectors

How can Kafka be connected to external systems? Kafka Connect is a framework that connects databases, search indexes, file systems, and key-value stores to Kafka using ready-to-use components called Kafka Connectors. Kafka connectors deliver data from external systems to Kafka topics and from Kafka topics to external systems.

Kafka resource connectors

The Kafka Source Connector aggregates data from source systems such as databases, streams, or message brokers. The source connector can also collect metrics from application servers into Kafka topics for near-real-time stream processing.

Kafka sink connectors

The Kafka Sink Connector exports data from Kafka topics to other systems. These can be popular databases like Oracle, SQL Server, SAP or indexes like Elasticsearch, batch systems like Hadoop, cloud platforms like Snowflake, Amazon S3, Redshift, Azure Synapse, and ADLS Gen2, etc.


Kafka has numerous and found in various industries such as financial services, manufacturing, retail, gaming, transportation and logistics, telecommunications, pharmaceuticals, life sciences, healthcare, automotive, insurance, and more. Kafka is used wherever large-scale streaming data is processed and used for reporting.

It is an open-source platform that ingests and processes streaming data in real time. Streaming data is generated simultaneously by thousands of data sources every second.

The Kafka Source Connector aggregates data from source systems such as databases, streams, or message brokers. The source connector can also collect metrics from application servers into Kafka topics for near-real-time stream processing.

Zookeeper software keeps records of all topic configurations, including the list of topics, the number of topic partitions for each topic, overriding topic configurations, preferred leader nodes, and replica locations, among others.

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Openai Whisper: Pricing, Features And Use Cases

A general purpose multilingual speech recognition system that lets users transcribe or translate audio files.

About Open AI Whisper

Whisper AI is an Open AI product that automatically recognizes speech and transcribes it. The tool is trained with a robust dataset of 680,000 hours of multilingual and multitask data from the web. It is trained using natural language and deep learning to interpret speeches in multiple languages. You can use Open AI Whisper to transcribe existing audio files, but it cannot record audio.

Whisper AI transcribes English and non-English audio with a high-level of accuracy. The tool also translates audio files into other languages. Whisper AI is trained with a large and diverse dataset and doesn’t focus specifically on a single language. It offers a zero-shot performance that makes 50% fewer errors compared to existing automatic speech recognition models.

Open AI Whisper Features

OpenAI Whisper is a powerful speech recognition tool. It offers several features to automate speech recognition and transcription. Some of its useful features include the following:

Whisper AI can translate and understand 100 languages.

It can identify the language of an audio file.

It offers API for developers to integrate Whisper AI features into other software.

Whisper AI offers offline access to users.

It can recognize speech in various accents despite background noise.

Open AI Whisper Use Case – Real-World Applications

Open AI Whisper can be used in every industry seeking speech recognition or translation services. Some real-life applications of this AI tool are as follows:

Translators can use Whisper AI to translate speech into other languages.

Transcribers can use Whisper AI to convert audio files into text.

Developers can use the API to create other powerful apps with Whisper AI functionality.

Open AI Whisper Pricing

Open AI Whisper is a free, open source model. You can access it using your Open AI credentials without paying a single penny. But the tool charges for API usage. Its API starts at $0.006 per 1000 tokens. It offers flexible pricing options, allowing users to pay as they use the credits.


Does Open AI own Whisper AI?

Whisper AI is a product of Open AI. The tool was launched in 2023 for automatic speech recognition. However, it is still under development, so you may encounter frequent new updates while using the tool.

Which languages does Whisper AI support?

Whisper AI supports more than 100 languages. You can use it in English, and non-English languages like Telugu, Korean, Chinese, Russian, Romanian, Hungarian, Tamil, French, Portuguese, Italian, Japanese, German, Greek, etc.

Do I need to create a Whisper AI account?

To access Whisper AI, you need to use your Open AI account. If you don’t have an Open AI account, create one using the sign up button. After signing in, you can start using Whisper AI to recognize speeches.

Does Whisper AI record audio?

No, Whisper AI doesn’t record audio files. It only transcribes or translates existing audio files. You cannot record calls or other speech using Whisper AI for language identification or speech recognition purposes.

Which file formats are supported on Whisper AI?

Whisper AI supports audio files in m4a, mp3, webm, mp4, mpga, wav, and mpeg. The maximum file size supported is 250 MB.

Whisper AI can be used for speech recognition in multiple languages. The tool has a robust dataset trained with thousands of hours of speech. You can use it to transcribe audio files, identify languages, or translate speech.

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Trending Ideas And Use Cases For Gpt

What Are Open AI and GPT-3?

Before we will find out how to use Open AI, let’s draw a clear line between OpenAI and GPT-3 since these two terms are often mixed up.

OpenAI is a research company that is engaged in studying the capabilities of artificial intelligence and looking for ways to use it for the benefit of humanity. The project was created by Elon Musk and other innovative entrepreneurs. The core mission of the organization is to develop such practices and approaches to artificial intelligence that will help coping with modern challenges (like data hacking, for example), and at the same time, prevent other companies and research institutes from using AI for malicious purposes.

GPT-3 (Generative Pre-trained Transformer), in turn, is a quite powerful machine learning algorithm that is shared via API, and enables developers to use it in their smart models programming. To date, GPT-3 is one of the smartest ML algorithms. It is capable of solving such tasks that just a few years ago, could be solved exclusively by a human. In some cases, the skills of the model go beyond the ones of the human, making it quite a demanded tool in AI powered project development.

GPT-3 Possible Use Cases

What can you do with GPT-3? Below are some use cases that you may take for your project inspiration and user experience boost.

Text writing and storytelling. The times when artificial intelligence learns to create engaging interesting texts and stories is not that far off – it is already here. For example, The Guardian has already published a text generated by GPT-3. And here is a Better Writer project  – a Grammarly like tool that is able to suggest content generation ideas as well.

Translation. This is one of the most powerful features of the GPT-3 model. While machine translation isn’t an innovation, most of the translations apps and websites still require human check to edit generated texts. But this is not the case with GPT-3 since this tool learns from the previous experience, catches the context, and generates the most accurate translation an ML algorithm is capable of.

What Kind of Apps This Technology Is Suitable For?

Considering such an impressive list of use cases, GPT-3 startups will be able to develop quite a lot of creative and useful ideas to use GPT-3 in their projects. To date, this technology seems to be the most promising for the solutions from the following industries:

Related: Top 7 services in demand for internet users.

GPT-3 Possible Pitfalls

Despite innovative potential and the product ideas compilation for OpenAI’s GPT-3 that can be continued, there are still some concerns about its effective and seamless usage.

GPT-3 can make mistakes. GPT-3 can sometimes have logic problems and make mistakes. And this is either the result of a programming loophole, or artificial intelligence has already learned one clearly human quality – the system does not want to admit that it does not know something or is mistaken. For example, GPT-3 repeatedly gave wrong answers to the trick questions from American history, claiming that certain individuals were presidents during a certain period.

GPT-3 can generate offensive content. In addition to some of its logic flaws, the algorithm can also generate offensive content, especially on controversial topics like politics or religion. Therefore, if you plan to use it in these or related directions, it is better to control the AI work with the help of the human mind.

GPT-3 is hardly predictable. The main concern regarding the capabilities of artificial intelligence also did not bypass GPT-3. Already, the system has gained enough power to make the consequences of its use difficult to predict.

How to Use GPT-3 in Your Product?

However, in the hands of a responsible entrepreneur, the technology can deliver ultimate benefit and value. Here is how to use Open AI models for your startup development.

Develop your startup idea and validate it – If you aren’t going to use GPT-3 for your idea generation, then you should come up with it on your own and validate it with the help of market overview, target audience and competitors research, the proof of concepts, user acceptance testing, and focus group surveys.

Decide on the task GPT-3 will solve for your project – The next step is to decide what task will GPT-3 perform in your project, and most importantly, how it will bring some novelty to the user experience. Try to employ your creativity, have one more look at the technology’s features and opportunities, and being guided by your research, come up with something new and valuable.

GPT-3 Projects

Request Open AI API – Since the technology is quite demanded, you will join a waitlist after leaving your request. So, use this time for some more research, validation, and creativity boost. Also, find out Open AI pricing models to choose the one that suits your project needs.

Integrate it into your MVP and test it – This is quite a standard step in LEAN-supported software development. Before investing in a full-fledged solution, test your MVP with your future users, use their positive response as proof for fundraising and keep the gained insights in mind at the next stage.

Extend your features – At this stage, you will be able to turn your MVP into a full-fledged AI-powered software. However, keep in mind the possible pitfalls we’ve mentioned and don’t let them spoil your users’ experience.


Using an Open AI solution for your tech project can be quite a promising development strategy. However, make sure to use it wisely, staying compliant with the core company’s mission – create AI solutions that benefit humanity and streamline its progress.

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