DEV Community: Monish Kumar

Understanding Convolutional Neural Networks: A Deep Dive

Monish Kumar — Sun, 14 Jul 2024 15:33:34 +0000

Convolutional Neural Networks (CNNs) have become a fundamental building block in the field of deep learning, particularly for tasks involving image and video data. Originally inspired by the visual cortex of animals, CNNs have shown remarkable success in various applications such as image classification, object detection, and even natural language processing. This blog explores the architecture, working principles, and applications of CNNs.

Why Convolutional Neural Networks?

Traditional fully connected neural networks struggle with high-dimensional data like images due to the immense number of parameters required. CNNs address this issue by leveraging the spatial structure of data, drastically reducing the number of parameters and improving both computational efficiency and performance.

Architecture of CNNs

CNNs consist of several layers, each serving a specific purpose in the feature extraction and learning process. The primary layers in a CNN are:

Convolutional Layers
Pooling Layers
Fully Connected Layers

1. Convolutional Layers
The convolutional layer is the core building block of a CNN. It consists of a set of learnable filters (kernels) that slide over the input data to produce feature maps. Each filter detects specific features such as edges, textures, or shapes.

Feature Map=Input∗Kernel+Bias

Where ∗ denotes the convolution operation.

Activation Function: After the convolution operation, an activation function (e.g., ReLU) is applied to introduce non-linearity.

Activation Function: After the convolution operation, an activation function (e.g., ReLU) is applied to introduce non-linearity.

Output=ReLU(Feature Map)

2. Pooling Layers

Pooling layers are used to reduce the spatial dimensions of the feature maps, thereby reducing the computational load and the number of parameters. The most common pooling operation is max pooling, which takes the maximum value from a patch of the feature map.

Pooled Feature Map=max(Feature Map)

3. Fully Connected Layers

After several convolutional and pooling layers, the high-level features are flattened and fed into fully connected layers. These layers operate like traditional neural networks and are responsible for the final classification or regression tasks.

Output=Softmax(Weights⋅Flattened Features+Bias)

Working Principles of CNNs

The power of CNNs lies in their ability to learn hierarchical features through a series of convolutional and pooling operations. Here's a step-by-step overview of how CNNs work:

Feature Extraction: Initial convolutional layers capture low-level features like edges and textures. As the data passes through more layers, higher-level features such as shapes and objects are detected.
Dimensionality Reduction: Pooling layers reduce the spatial dimensions, making the network more computationally efficient and less prone to overfitting.
Classification/Regression: Fully connected layers at the end of the network use the extracted features to make predictions.

Applications of CNNs

1. Image Classification
CNNs excel at classifying images into predefined categories. Prominent datasets like ImageNet have been used to train CNNs to recognize thousands of object categories with high accuracy.

2. Object Detection
In object detection, CNNs not only classify objects within an image but also locate them. Techniques like Faster R-CNN and YOLO have demonstrated state-of-the-art performance in real-time object detection.

3. Image Segmentation
Image segmentation involves partitioning an image into meaningful segments. CNN-based architectures like U-Net and Mask R-CNN are widely used for tasks requiring precise segmentation, such as medical image analysis.

4. Facial Recognition
CNNs are the backbone of facial recognition systems, enabling accurate identification and verification of individuals based on facial features.

5. Natural Language Processing
While CNNs are primarily used for image-related tasks, they have also been successfully applied to text data for tasks like sentence classification and text generation.

Advantages of CNNs

Parameter Sharing: Convolutional layers share weights across spatial dimensions, reducing the number of parameters and the risk of overfitting.
Spatial Hierarchy: CNNs capture spatial hierarchies by learning increasingly abstract features at deeper layers.
Translation Invariance: Pooling layers introduce translation invariance, making CNNs robust to shifts and distortions in input data.

Conclusion

Convolutional Neural Networks have revolutionized the field of deep learning, particularly for tasks involving visual data. Their ability to automatically learn hierarchical features from raw data makes them indispensable for a wide range of applications. By understanding the architecture and working principles of CNNs, researchers and practitioners can harness their power to tackle complex problems in computer vision and beyond.

Attention Mechanisms in Deep Learning: Unlocking New Capabilities

Monish Kumar — Sat, 13 Jul 2024 18:13:33 +0000

Attention mechanisms have become a cornerstone of modern deep learning architectures, particularly in natural language processing (NLP) and computer vision. Introduced as a solution to the limitations of traditional sequence models, attention mechanisms allow models to dynamically focus on different parts of input data, leading to significant improvements in performance and flexibility. This blog explores the fundamentals, types, and applications of attention mechanisms in deep learning.

Why Attention Mechanisms?

Traditional models like RNNs and CNNs have limitations in handling long-range dependencies and varying importance of input elements. Attention mechanisms address these issues by enabling models to weigh the relevance of different parts of the input data dynamically. This ability to focus selectively allows for more nuanced and effective processing of complex data.

Fundamentals of Attention Mechanisms

At its core, an attention mechanism computes a weighted sum of input elements, where the weights represent the importance of each element. The attention process can be summarized in three steps:

Scoring: Calculate a score that represents the relevance of each input element with respect to the current task.
Weighting: Apply a softmax function to convert the scores into probabilities, ensuring they sum to one.
Summation: Compute a weighted sum of the input elements based on the probabilities.

The general formula for the attention mechanism is:

Types of Attention Mechanisms

1.Additive (Bahdanau) Attention

Introduced by Bahdanau et al., additive attention uses a feed-forward network to compute the alignment scores. It is particularly useful in sequence-to-sequence models for machine translation.

2.Dot-Product (Luong) Attention

Proposed by Luong et al., dot-product attention computes the alignment scores using the dot product between the query and key vectors. It is computationally more efficient than additive attention.

3.Scaled Dot-Product Attention

Scaled dot-product attention, used in transformers, scales the dot products by the square root of the key dimension to prevent the gradients from vanishing or exploding.

4.Self-Attention

Self-attention, or intra-attention, allows each element of a sequence to attend to all other elements. It is the backbone of the transformer architecture and is crucial for capturing dependencies in sequences.

5.Multi-Head Attention

Multi-head attention involves using multiple sets of queries, keys, and values, allowing the model to attend to information from different representation subspaces. It enhances the ability to focus on various parts of the input.

Applications of Attention Mechanisms

1.Natural Language Processing

Machine Translation: Attention mechanisms allow translation models to focus on relevant words in the source sentence.
Text Summarization: They help models identify key sentences and phrases to generate coherent summaries.
Question Answering: Attention helps models find the answer span in a context paragraph.

2.Computer Vision

Image Captioning: Attention mechanisms can highlight important regions of an image when generating descriptive captions.
Object Detection: They help models focus on relevant parts of the image to detect and classify objects.

3.Speech Recognition

Attention mechanisms enhance the ability of models to focus on relevant parts of an audio signal, improving transcription accuracy.

4.Healthcare

In medical imaging, attention mechanisms can help models focus on critical areas, such as tumors or lesions, improving diagnostic accuracy.

Conclusion

Attention mechanisms have revolutionized deep learning by providing models with the ability to dynamically focus on relevant parts of the input data. This capability has led to significant advancements in various fields, including NLP, computer vision, and speech recognition. By understanding and leveraging attention mechanisms, researchers and practitioners can build more powerful and efficient models, pushing the boundaries of what is possible with deep learning.

Exploring Transformer Networks: Revolutionizing Deep Learning

Monish Kumar — Fri, 12 Jul 2024 11:56:04 +0000

Transformer networks have dramatically transformed the landscape of deep learning, particularly in the realm of natural language processing (NLP). Introduced in the paper "Attention is All You Need" by Vaswani et al. in 2017, transformers have since become the foundation of state-of-the-art models like BERT, GPT, and T5. Unlike traditional sequence models, transformers rely solely on attention mechanisms, which enable them to capture complex dependencies in data more effectively.

Why Transformers?

Before transformers, RNNs and their variants (LSTMs, GRUs) were the go-to models for sequence tasks. However, these models struggled with long-range dependencies and parallelization. Transformers address these issues by dispensing with recurrence and instead using self-attention mechanisms to process sequences in parallel.

Architecture of Transformers

The transformer model comprises an encoder and a decoder, both made up of multiple layers of identical sub-units. Each layer contains two main components: multi-head self-attention mechanisms and position-wise fully connected feed-forward networks.

Multi-Head Self-Attention Self-attention allows the model to weigh the importance of different words in a sequence when encoding a word. Multi-head attention enables the model to focus on different parts of the sequence simultaneously.

Position-Wise Feed-Forward Networks After the attention mechanism, the data passes through a feed-forward network applied independently to each position.

Positional Encoding

Since transformers lack the sequential nature of RNNs, positional encodings are added to the input embeddings to provide information about the position of each token in the sequence.

Encoder-Decoder Structure

Encoder: The encoder consists of a stack of identical layers. Each layer has two sub-layers: multi-head self-attention and a feed-forward network. Residual connections and layer normalization are applied around each sub-layer.
Decoder: The decoder also consists of a stack of identical layers, with an additional sub-layer for multi-head attention over the encoder's output. This allows the decoder to focus on relevant parts of the input sequence when generating the output.

Advantages of Transformers

Parallelization: Unlike RNNs, transformers process all tokens in a sequence simultaneously, leading to faster training and inference.
Long-Range Dependencies: Self-attention mechanisms can capture dependencies between distant tokens more effectively than RNNs.
Scalability: Transformers scale efficiently with increasing data and model sizes, making them suitable for large datasets and complex tasks.

Applications of Transformers

Transformers have revolutionized numerous applications, including:

Natural Language Processing: Language translation, text generation, summarization, and sentiment analysis.
Vision: Vision transformers (ViTs) apply transformer models to image recognition tasks.
Audio: Audio transformers are used for tasks like speech recognition and music generation.
Reinforcement Learning: Transformers are being explored for their potential in reinforcement learning scenarios.

Conclusion

Transformers have revolutionized deep learning by addressing the limitations of traditional sequence models. Their ability to handle long-range dependencies, process data in parallel, and scale effectively has made them the backbone of many state-of-the-art models. By understanding the core principles of transformers, researchers and practitioners can harness their power to tackle a wide range of complex tasks across various domains.

Understanding GRU Networks

Monish Kumar — Wed, 10 Jul 2024 16:41:09 +0000

Gated Recurrent Units (GRUs) are a type of recurrent neural network (RNN) architecture that have become increasingly popular for sequence modeling tasks. Introduced by Kyunghyun Cho et al. in 2014, GRUs aim to solve some of the vanishing gradient problems associated with traditional RNNs while maintaining a simpler structure compared to Long Short-Term Memory (LSTM) networks.

Why GRUs?

GRUs address the limitations of standard RNNs, such as difficulty in learning long-term dependencies due to vanishing gradients. They offer a simpler alternative to LSTMs, with fewer parameters and gates, which often leads to faster training and potentially better performance on certain tasks.

Architecture of GRUs

GRUs consist of two main gates: the reset gate and the update gate. These gates control the flow of information and allow the network to retain or forget information as needed.

Reset Gate The reset gate determines how much of the past information to forget. It decides whether to ignore the previous hidden state when calculating the current hidden state.

Update Gate The update gate determines how much of the past information to pass along to the future. It decides how much of the new hidden state will come from the previous hidden state and how much from the current input.

Current Memory Content The current memory content combines the new input and the previous hidden state, scaled by the reset gate.

Final Hidden State The final hidden state is a combination of the previous hidden state and the current memory content, controlled by the update gate.

Advantages of GRUs

Simpler Architecture: With fewer gates and parameters than LSTMs, GRUs are easier to implement and train.
Efficiency: GRUs often converge faster and require less computational resources.
Performance: In many cases, GRUs perform on par with or even better than LSTMs on specific tasks.

When to Use GRUs

GRUs are well-suited for tasks involving sequential data, such as:

Natural Language Processing: Text generation, language translation, and sentiment analysis.
Time Series Prediction: Stock market predictions, weather forecasting, and anomaly detection.
Speech Recognition: Recognizing and transcribing spoken words.

Conclusion

Gated Recurrent Units offer an efficient and powerful alternative to traditional RNNs and LSTMs. Their simpler architecture, combined with their ability to handle long-term dependencies, makes them an attractive choice for many sequence modeling tasks. By understanding the inner workings of GRUs, you can better leverage their strengths to build robust and effective neural networks for a variety of applications.

Understanding LSTM Networks: A Guide to Time Series and Sequence Prediction

Monish Kumar — Thu, 04 Jul 2024 03:23:36 +0000

In the realm of artificial intelligence and deep learning, Long Short-Term Memory (LSTM) networks have emerged as a powerful tool for handling time series and sequential data. This blog aims to demystify LSTM networks, explaining their architecture, functioning, and applications.

What is an LSTM Network?

LSTM is a type of recurrent neural network (RNN) designed to overcome the limitations of traditional RNNs, particularly the issue of long-term dependencies. Standard RNNs struggle to remember information from earlier time steps when the gap between relevant information and the point where it's needed becomes too large. LSTMs address this problem with a sophisticated memory cell structure.

The Architecture of LSTM Networks

LSTM networks are composed of units called LSTM cells, each containing three main components: gates, cell state, and hidden state.

Cell State: This is the memory of the network, carrying information across different time steps.
Gates: LSTMs have three gates (input, forget, and output gates) that regulate the flow of information.

Forget Gate

The forget gate decides what information should be discarded from the cell state. It takes the previous hidden state and the current input, passes them through a sigmoid function, and outputs a number between 0 and 1. A value of 0 means "completely forget" and 1 means "completely keep".

Input Gate

The input gate determines what new information should be added to the cell state. It has two parts: a sigmoid layer (to decide which values to update) and a tanh layer (to create new candidate values).

Output Gate

The output gate decides what the next hidden state should be. This hidden state is also used for predictions.

How LSTM Works

At each time step, LSTM processes the input data through the gates and updates the cell state and hidden state accordingly. Here's a step-by-step overview:

Forget Step: The forget gate evaluates which information from the previous cell state should be carried forward.
Input Step: The input gate assesses and updates the new information.
Update Step: The cell state is updated by combining the information from the forget and input steps.
Output Step: The output gate decides the new hidden state, which is used for making predictions and passed to the next time step.

Applications of LSTM Networks

LSTMs are particularly well-suited for:

Time Series Prediction: Forecasting stock prices, weather, and other temporal data.
Natural Language Processing (NLP): Language modeling, text generation, machine translation, and sentiment analysis.
Speech Recognition: Transcribing spoken words into text.
Anomaly Detection: Identifying unusual patterns in data, such as fraud detection.

Conclusion

LSTM networks have revolutionized the way we handle sequential data, providing a robust solution to the challenges posed by long-term dependencies in traditional RNNs. With their unique architecture and gate mechanisms, LSTMs can retain crucial information over extended periods, making them indispensable for a wide range of applications in time series analysis, NLP, and beyond.

Whether you're working on predicting stock prices, generating human-like text, or detecting anomalies, LSTM networks offer a powerful toolset to achieve remarkable results.

Understanding Recurring Neural Networks (RNNs)

Monish Kumar — Fri, 28 Jun 2024 02:21:48 +0000

In the dynamic field of artificial intelligence and machine learning, Recurring Neural Networks (RNNs) hold a unique place. Unlike traditional neural networks, RNNs are designed to recognize patterns in sequences of data, such as time series, speech, text, financial data, and even DNA sequences. This ability to consider temporal information and context makes RNNs particularly powerful for a variety of applications.

What Are Recurring Neural Networks?

Recurring Neural Networks (RNNs) are a class of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. This creates an internal state of the network which allows it to exhibit dynamic temporal behavior. Unlike feedforward neural networks, RNNs can use their internal state (memory) to process sequences of inputs.

How RNNs Work

At the heart of an RNN is the concept of 'recurrent' connections which enable the network to maintain a memory of previous inputs. This is achieved through a looping mechanism where the output from a previous step is fed as input to the current step. Mathematically, this is represented as:

Types of RNNs

Vanilla RNNs: These are the simplest form of RNNs, where each neuron has a direct loop connection. However, vanilla RNNs often suffer from issues like vanishing and exploding gradients, which can make training difficult.
Long Short-Term Memory (LSTM) Networks: LSTMs are a special kind of RNN capable of learning long-term dependencies. They are explicitly designed to avoid the long-term dependency problem. LSTM networks are made up of units called memory cells, which maintain their state over time and are regulated by three gates: input gate, output gate, and forget gate.
Gated Recurrent Units (GRUs): GRUs are a variation on LSTMs but with a simplified structure. They combine the input and forget gates into a single update gate, and they also merge the cell state and hidden state, resulting in fewer parameters to train.

Applications of RNNs

Natural Language Processing (NLP): RNNs are widely used in NLP tasks such as language modeling, text generation, machine translation, and sentiment analysis.
Speech Recognition: They can process audio data and are essential in speech-to-text applications.
Time Series Prediction: RNNs are effective in forecasting time series data such as stock prices, weather predictions, and sales forecasting.
Video Analysis: They are used to model temporal sequences in video data, useful in applications like video captioning and action recognition.

Challenges and Solutions

Vanishing and Exploding Gradients: This problem is prevalent in vanilla RNNs when dealing with long sequences. LSTM and GRU architectures mitigate this issue with their gated mechanisms.
Training Time and Computational Complexity: RNNs, especially LSTMs and GRUs, can be computationally intensive. Optimizations like parallelization and the use of GPUs can help.
Overfitting: RNNs can overfit on small datasets. Regularization techniques such as dropout can help reduce overfitting.

Conclusion

Recurring Neural Networks are a powerful tool for sequence learning, providing the ability to model temporal dependencies in data. With advancements like LSTMs and GRUs, many of the traditional challenges of RNNs have been mitigated, opening up a vast array of applications in fields like NLP, speech recognition, and time series prediction. As research continues, the capabilities and efficiency of RNNs are likely to improve further, solidifying their role in the future of machine learning.

By understanding the workings and applications of RNNs, we can better leverage their strengths and address their weaknesses, paving the way for more advanced and efficient neural network models.

Understanding Recursive Neural Networks

Monish Kumar — Thu, 27 Jun 2024 09:09:34 +0000

Recursive Neural Networks (RecNNs) are a fascinating and powerful class of neural networks designed to model hierarchical structures in data. Unlike traditional neural networks, which process data in a linear sequence, RecNNs can process data structures such as trees, making them particularly well-suited for tasks like natural language processing (NLP) and computer vision.

What are Recursive Neural Networks?

Recursive Neural Networks are a type of neural network that applies the same set of weights recursively over a structured input to produce a structured output. This recursive application allows the network to handle variable-length and hierarchical data efficiently.

How Do Recursive Neural Networks Work?

The key idea behind RecNNs is to break down complex structures into simpler components. For example, in NLP, a sentence can be broken down into phrases, which can be further broken down into words. RecNNs process these components hierarchically, starting from the leaves (basic units like words) and combining them recursively to form higher-level representations (phrases, sentences, etc.).

Basic Architecture

Input Layer: The leaves of the tree represent the input data, such as words in a sentence.
Hidden Layers: These layers combine the representations of child nodes to form parent node representations. This process continues recursively until the root node is reached.
Output Layer: The root node’s representation can be used for various tasks such as classification or regression.

Applications of Recursive Neural Networks

Natural Language Processing: RecNNs are used for tasks like sentiment analysis, machine translation, and syntactic parsing. They can capture the hierarchical structure of language, making them effective for understanding context and relationships between words.
Image Processing: In computer vision, RecNNs can model the hierarchical structure of objects within an image. For instance, parts of an object can be combined to form a complete object representation.
Hierarchical Data Analysis: Any data with a natural hierarchical structure, such as social networks, web pages, or biological data, can benefit from RecNNs.

Advantages of Recursive Neural Networks

Hierarchical Representation: RecNNs naturally handle hierarchical data, providing a rich representation of the input structure.
Parameter Sharing: Since the same set of weights is used recursively, RecNNs are parameter efficient.
Flexibility: They can model various types of data structures, making them versatile for different applications.

Challenges and Limitations

Complexity: Training RecNNs can be computationally intensive and complex due to the recursive nature of the computations.
Data Requirements: They often require a large amount of annotated hierarchical data for effective training.
Gradient Vanishing/Exploding: Like other deep networks, RecNNs can suffer from gradient vanishing or exploding problems during training.

Conclusion

Recursive Neural Networks offer a powerful way to model hierarchical data structures. They have proven to be particularly effective in fields like natural language processing and computer vision, where understanding the structure of the input data is crucial. Despite their complexity and training challenges, the benefits they offer make them a valuable tool in the arsenal of machine learning techniques.

As the field of artificial intelligence continues to evolve, Recursive Neural Networks are likely to play an increasingly important role in developing sophisticated models capable of understanding and processing complex, structured data.

Exploring Recursive Nets

Monish Kumar — Fri, 14 Jun 2024 16:26:24 +0000

This is a submission for DEV Computer Science Challenge v24.06.12: One Byte Explainer.

Explainer

Recursive neural networks (RvNNs) are a class of artificial neural networks where the structure is applied recursively, particularly well-suited for hierarchical or structured data. They operate on tree-like structures, making them apt for tasks involving nested or sequential data.

In RvNNs, each node in a tree represents a computational unit, and its children nodes feed into it. This recursive application allows the network to capture the compositional nature of data, making it especially effective for tasks such as natural language processing, where sentences and their components exhibit a hierarchical structure.

The recursive process typically involves combining information from child nodes using a learned function to produce a parent node's representation. This function often takes the form of a neural network itself, such as a feedforward network. The process continues until reaching the root node, whose representation can be used for tasks like classification or regression.

Training RvNNs involves backpropagation through structure (BTS), a variant of backpropagation tailored to tree structures. BTS calculates gradients in a way that respects the recursive nature of the network, allowing the model to learn from the entire structure rather than just linear sequences of data.

Recursive nets are closely related to recurrent neural networks (RNNs), but while RNNs deal with sequences in a linear manner, RvNNs handle hierarchical structures, providing a more flexible framework for complex, nested data. Notable applications include sentiment analysis, syntactic parsing, and image segmentation, where understanding the part-whole relationships is crucial.

By leveraging their recursive nature, RvNNs offer a powerful tool for modeling and understanding data with inherent hierarchical properties, capturing both local and global structures effectively.

Deploying Apps on Render

Monish Kumar — Sat, 09 Sep 2023 07:47:06 +0000

Deploying is one of the essential stages in development of any softwares or apps. Deploying an app makes it accessible to users, allowing them to use and benefit from the software. Without deployment, the application remains confined to the development and testing environments.Apps are created to provide value to users or organisations. Deployment ensures that the value envisioned during development is delivered to the intended audience.

As we are now aware of the importance of deployment. Let's get into deploying the app. In this scenario, I have chosen a streamlit application which enables us to create custom dashboards or web-apps.

Pre Requisite:

Have the files of the application on a Github Repository along with the requirements.txt file which contains the necessary packages to be installed.
Sign up on Render

Deployment Flow

Access your dashboard which contains the list of apps once you have deployed it.

Dashboard:

2.Create a new web service which is accessible from the dashboard.

3.Choose the option of deploying from a GitHub Repository.

4.Select the desired repository and connect it.

5.Name your app with a unique name and do not alter any other details for maximum performance.

6.Enter the runline or start command to initialise the app. i.e.for streamlit apps : streamlit run appname.py
for wsgi apps : gunicorn run appname.wsgi

7.The process has been complete and the deployment has been after clicking the button to create web service.The app should be deployed automatically in 5 minutes.

You can access the app that has been deployed with the url allotted to your account.
Checkout the basic app that has been deployed.

Stay Tuned for my next blog on hosting in a custom domain.

Streaming PC audio to phone

Monish Kumar — Wed, 26 Jan 2022 07:00:57 +0000

Many people have use Chromecast or screen mirroring tool to cast their video and audio from phone to tv or pc to tv when connected to the same Wi-Fi network.

There was no possibility of streaming audio from pc to phone via Wi-Fi until I found a tool to do it. It was greatly helpful as my pc's Bluetooth stopped working for which I was not able to use my Bluetooth earphone or speaker.

This is the reason I am sharing this article here as it would be helpful to others facing this problem.

Now Let's get started with the steps to Stream PC audio to phone through which you can stream it to you Bluetooth headset or speaker.

You can also use your phone as a mic in this method by choosing the relevant device.

1.Download Audio Relay in your PC or Laptop and phone.

Windows
Some screenshots to make sure that you have the original version:

Android

2.Make Sure that your PC and Phone are connected to the same Wi-Fi network and open the software in PC or Laptop.

3.Open the app in your Phone. If both the devices are connected on the same Wi-Fi network, The name of the other device will appear in the app along with the temporary ip-address.

Click on the Desired Device.

5. If you need to stream it to the Bluetooth devices just connect to the phone via Bluetooth. I am pretty sure that every phone has a Bluetooth.

6.Choose the device on pc(Speaker or Mic) which you would like to use form you phone.

That's it. It's Simple as the 6 steps.

If you face any problem do mention it in the discussion. I would love to resolve it.

RPA: Catalyst of Industrial Revolution 4.0

Monish Kumar — Thu, 30 Dec 2021 06:56:59 +0000

Robotic Process Automation popularly known as RPA. It is used to automate a repetitive task such as sending 500 emails per day, extracting some data from any number of invoices which are partially structured and a storing it in a excel file which is structured.

It was invented to convert unstructured data generated in the third industrial revolution into structured data by analyzing it. It is capable of mimicking human actions.

There are many IDE's (Integrated Development Environment) to handle RPA. The environment can be categorized into

No Code Platform
Coding Platform

No Code Platform is preferred by everyone as it is efficient in many ways. The major Platforms are as follows:

UiPath
Blue Prism
Automation Anywhere

Here is a automations that I have made which makes my life easy.

During the pandemic everyone has to join meetings or classes virtually.

Joining classes or meeting on the scheduled time

Here is the link for the video for the automation:

Video Link

Subscribe for more automations.

Connecting Multiple Devices to a usb printer through a network

Monish Kumar — Thu, 26 Aug 2021 16:42:51 +0000

In the modern era, Everyone likes to stay connected to the internet which enables us to gain knowledge in various fields in just a click. Connectivity is the most essential in our day today life. Social Media Platforms can be used to connect with people around the World.

As we have seen the importance of connectivity, Lets move into our topic.

The Basic Terms that we should know before entering into our topic

1.Admin Device (The device that receives all the print job)

2.Client Device (All the devices that can use the print option but requires permission from the admin)

3.Network (refers to Wi-Fi or a mobile hotspot)

Step 1:

We have to connect the admin device to the usb printer so that the admin device can communicate with the printer and send the print jobs that is received from the client devices.

Step 2:

We require a mode of connectivity to connect multiple client devices. To create a mode of connectivity we can use a Wi-Fi or a mobile hotspot (with or without the internet access).

Step 3:

Ensure that all the devices (admin and client) are connected to the same network because this is the mode of communication for the devices.

Step 4:

We should enable the printer sharing option from the admin device so that the client devices has the option to send print jobs to the printer via the admin device.

In order to enable the sharing option from the admin device

1.Head to the Control Panel (it can be accessed by searching in the start menu)

2.As our printer is a hardware, Select the Hardware and sound option. Then we select the devices and printers option.

3. Right Click on the desired printer and select the printer properties and choose the sharing tab and enable the sharing option.

Step 5:

1.To add the printer in the client's device, Head to the Control Panel and choose the Hardware and Sound option.

2.Click on add a device and select the printer that is shared from the admin device.

Congratulations🎈✨🎉🎉, We have successfully completed all the steps.

Now we can print from multiple devices in a usb printer.