As a data scientist with five years of experience, I completed a project for a client that involved creating stunning 3D photos from a single image using motion parallax with an AI-based Context-aware Layered Depth Inpainting technique. In this post, I'd like to share some of the details of the project and how I approached it.

First, discuss what 3D photography is and why it's crucial. 3D photography involves creating a visual representation of a three-dimensional object or scene, which can bring it to life and provide a more immersive experience for the viewer. With the rise of virtual and augmented reality technologies, the demand for 3D photos has only increased in recent years.

To create 3D photos, various techniques, and tools are available. In this project, I used an AI-based Context-aware Layered Depth Inpainting technique, which involves generating a depth map from the original 2D image and using that information to generate multiple layers of the scene with different levels of depth. These layers are then combined to create a 3D effect, with the ability to adjust the depth of each layer to create a more dynamic and realistic 3D effect.

I used Python and TensorFlow, a popular machine-learning framework, to implement this technique. I also used an asynchronous implementation to execute long-running tasks in parallel, which helped save time and improve the project's overall efficiency.

One of the critical challenges in this project was ensuring that the final 3D images looked realistic and had smooth transitions between different layers. To accomplish this, I had to carefully tune the various parameters of the algorithm and perform extensive testing to ensure the results were satisfactory.

Overall, this project was an excellent opportunity to explore cutting-edge techniques and technologies in 3D photography and machine learning. By leveraging the power of AI and deep learning, we created stunning 3D photos from a single image, which can help bring any scene to life and provide a more immersive experience for viewers. I'm excited to see how this technology continues to evolve and be used in various applications in the future.

If you've ever had to deal with processing invoices, you know how tedious and time-consuming it can be. Luckily, we're living in an age where cutting-edge AI technology can streamline this process and make it faster, more accurate, and more efficient than ever before.

In this project, I use a combination of natural language processing (NLP), optical character recognition (OCR), computer vision (CV), and deep learning techniques to build a system that can process unstructured invoice data in any format - whether it's structured or unstructured or even in image form.

With AI-based invoice detection and data extraction system, we can quickly and accurately find patterns in invoices, identify the headers and rows, and extract the relevant data into an Excel sheet. We use NLP and named entity recognition (NER) to identify important invoice entities, and to find all the relevant information for both parties.

To ensure scalability and fast processing times, we're using parallel processing and an API that can handle thousands of users simultaneously. It's no wonder that this project has already gained more than 100,000 users every month!

The tech stack includes Python, TensorFlow, deep learning, machine learning, OCR deep learning, NER, and parallel processing. With this powerful combination, I was able to build a cutting-edge AI system that can handle even the most complex and challenging invoice data processing tasks.

In conclusion, AI-based invoice detection and data extraction is a game-changer for businesses and organizations that deal with large volumes of invoices. By automating this tedious and time-consuming task, we're freeing up valuable time and resources that can be better spent on other important tasks. We're excited to continue developing this project and to push the boundaries of what's possible with AI technology.

The robotics and artificial intelligence (AI) world has come a long way in recent years. The development of autonomous vehicles is one such breakthrough, which promises to revolutionize the transportation industry. However, what if we take it one step further? What if the vehicle could recognize and respond to road signs and text inputs with ease? That is precisely what our Sign Recognition Robot Autonomous AI Vehicle project aims to achieve.

I worked on a project and developed a unique robot car that uses cutting-edge technology to recognize and respond to road signs and text inputs. This AI-powered robot car has a camera that captures live video feeds, which are processed through an OCR (Optical Character Recognition) engine to detect and interpret the text on the signs.

The robot car has been built using a Python programming language and Raspberry Pi, making it highly versatile and scalable. The use of these technologies enables the robot car to execute complex tasks seamlessly and quickly.

In addition to recognizing and responding to road signs, our robot car can also talk, sprint, and stop. This functionality has been achieved by combining various technologies such as deep learning, machine learning, and NLP (Natural Language Processing)

I overcame various challenges while building this robot car, including creating a robust hardware platform to support the complex algorithms used to recognize and interpret the text. We have also used the hard way to build the robot, ensuring it is stable and can operate reliably.

The Sign Recognition, Robot Autonomous AI Vehicle project, is an exciting development in the field of robotics and AI, and we are thrilled to be at the forefront of this innovation. Our team continues to work on this project, constantly refining and enhancing the AI-powered robot car to perform even more complex tasks and offer an even more seamless experience.

In conclusion, the Sign Recognition Robot Autonomous AI Vehicle project is a testament to AI and robotics's possibilities. With this technology, we can create intelligent machines that can recognize and respond to their environment with ease, paving the way for a more automated and efficient future.

NFT-based gaming has taken the gaming industry by storm, offering players a new level of engagement and monetization. In this growing space, it's important for players to stay ahead of the competition to reap the maximum benefits. That's where an AI Bot for NFT Gaming comes in, providing players with a competitive edge to enhance their gaming experience.

In this project, we built an AI Bot for NFT Gaming that could read the game's interface and make strategic moves to win the game. The bot was designed to work without any reinforcement learning, which makes it unique from other bots in the market.

The bot could read the menus, analyze the situation, and start playing the game on its own. With its advanced algorithms and cutting-edge technology, it could build strategies and make decisions that gave it a 70% success rate, making it a valuable asset for players looking to maximize their earnings.

Tech Stack:
To build this AI Bot for NFT Gaming, I used Python as the primary programming language. We also utilized various libraries such as OpenCV, PIL, and PyAutoGUI to interact with the game's interface and simulate mouse clicks and keyboard inputs.

I trained the bot using various techniques, including image recognition, machine learning algorithms, and decision trees, to enable it to make strategic decisions during the game.

Conclusion:
In conclusion, the AI Bot for NFT Gaming is a game-changer for NFT-based gaming enthusiasts. With its advanced algorithms and high success rate, players can now stay ahead of their competition and increase their earnings. This project showcases the power of AI and how it can be leveraged to enhance the gaming experience.

Most important! The client was super happy!!

If you are in the car dealership business, you know the importance of keeping a track of the car classified markets. But it can be a daunting task to scrape thousands of website pages to find the best cars to purchase and sell. This is where the Car Classified Market Scraping Bot comes in.

The Car Classified Market Scraping Bot is an innovative tool designed to maximize your buying and selling potential. It is an AI-based bot that scrapes Autotrader, Cargurus, and Craigslist marketplaces to find the best cars to purchase and sell. With this tool, you can save time and effort and focus on more important aspects of your business.

The bot uses Python and parallel processing to process thousands of website pages concurrently. It extracts car details and validates them from two other websites before putting them into the database. The project was hosted on AWS, and the database was also hosted on AWS. The bot runs 24/7 and looks for new entries every moment. It is able to get 1500+ new entries every day and build up the database with hundreds and thousands of cars.

This project was a complex one, requiring a high level of expertise in Python, AI, and web scraping. The bot had to be designed to scrape multiple websites, process thousands of website pages concurrently, and validate car details from two other websites. The project required expertise in AWS to host the bot and the database and ensure that it runs 24/7.

Tech Stack:
- Python
- AI
- Web Scraping
- AWS

The Car Classified Market Scraping Bot is an innovative tool that can help car dealerships maximize their buying and selling potential. It saves time and effort by automating the process of scraping car classified markets and extracting car details. The bot is designed to be reliable and efficient, running 24/7 to ensure that you never miss an opportunity to purchase or sell a car.

Investing in the stock market is always a risky business. The unpredictable nature of stock prices can be a challenge for even the most experienced investors. However, with the latest advancements in artificial intelligence (AI) and machine learning, predicting stock prices with high accuracy has become more feasible.

In this project, I built a stock price prediction model that uses cutting-edge AI technology to predict stock prices with incredible accuracy and consistency. The model is based on the state-of-the-art NBEAT research paper implementation, which is known for its ability to accurately predict stock prices.

The NBEAT algorithm is based on neural networks and uses a combination of attention-based mechanisms and time-series data to accurately predict future stock prices. With the power of the NBEAT algorithm, my stock price prediction model is able to provide investors with reliable predictions for future stock prices.

The model uses a range of historical data, such as the opening price, closing price, high and low price, and trading volume, to predict the future price of a particular stock. The AI model is trained on historical data to learn patterns and trends in the market and uses this information to make predictions about the future.

The prediction model is built using Python and TensorFlow, a powerful open-source software library for data analysis and machine learning. The project involves a combination of data processing, machine learning, and deep learning techniques to provide accurate and reliable predictions.

One of the major benefits of this stock price prediction model is its ability to adapt to changing market conditions. The model continuously learns from new data and adapts to new market trends, allowing it to make more accurate predictions over time.

However, building and maintaining such a complex model is not an easy task. The model requires a significant amount of computing power and storage to store and process large amounts of historical data. Additionally, the model requires ongoing maintenance and updates to ensure that it is accurate and up-to-date with the latest market trends.

In conclusion, the Stock Price Prediction Model built on the NBEAT algorithm is a powerful tool for investors looking to make informed decisions about the stock market. The model uses cutting-edge AI technology to predict future stock prices with incredible accuracy and consistency. However, it's important to remember that investing always carries a risk, and accurate predictions cannot guarantee success.

Instagram has become one of the most popular social media platforms in recent years, with millions of users sharing their photos and videos on a daily basis. The abundance of content on the platform can make it difficult for users to showcase their photos in a creative and eye-catching way. That's where the Instagram Auto Collage Maker comes in - an innovative AI-based tool designed to transform your Instagram feed into stunning collages with ease.

The project involved building a website bot in Python that allows users to select a collage template and input their Instagram ID. The system would then automatically download the user's images from Instagram and fill the template accordingly, using AI technology to create a seamless and smooth experience.

This project was not without its challenges. The client had talked to several developers who said that the project was impossible to do, but we were determined to find a way. We had to overcome several technical hurdles to create a tool that was both efficient and effective.

The Instagram Auto Collage Maker was built using several key technologies, including Python, OpenCV, and AI. Python was used as the primary programming language, providing a solid foundation for building the website bot. TensorFlow, a popular open-source machine learning platform, was used to train the AI models that allowed the system to automatically fill the templates with the user's photos.

The AI technology used in the project was a critical component of its success. By leveraging the power of AI, we were able to create an algorithm that automatically arranged the user's photos in a way that was both visually appealing and optimized for each specific template. This allowed users to create stunning collages with ease, without having to worry about the time-consuming and sometimes frustrating task of manually arranging their photos.

Overall, the Instagram Auto Collage Maker was a challenging but rewarding project that showcased the power of AI and the potential for innovative solutions to seemingly impossible problems. With this tool, users can now showcase their photos in a creative and visually appealing way, thanks to the power of AI and our team's dedication to creating a seamless and user-friendly experience.

FIFA Trading Bot built in Python to automate the trading process on the FIFA marketplace. The bot uses a web scraping technique to extract data from the FIFA marketplace, including player prices, ratings, and attributes. It then uses machine learning algorithms to predict the market trends and identify potential opportunities for trading.

The FIFA Trading Bot can manage multiple accounts on different proxies and IP addresses, bypassing the security checks of the FIFA marketplace. The bot uses an advanced algorithm to calculate the best price for buying and selling players, ensuring maximum profit margins for the user.

The FIFA Trading Bot can run 24/7, allowing users to make trades even when they are away from their computers. The bot can be customized to suit the user's trading strategy and preferences, including the choice of players to trade, the frequency of trades, and the profit margin.

How We Completed the Project
Building the FIFA Trading Bot was a complex project that required expertise in Python programming, web scraping, machine learning, and trading strategies. Our team of developers followed a structured approach to complete the project.

1. Understanding the Requirements: Project started by understanding the requirements of the client and the FIFA marketplace. Identified the key features of the bot, including web scraping, machine learning, trading algorithms, and account management.
2. Designing the Architecture: Designed the architecture of the bot, including the modules, functions, and APIs. We chose Python as the programming language and used several libraries and frameworks, including Beautiful Soup, Pandas, NumPy, Scikit-Learn, and Flask.
3. Developing the Features: Developed the features of the bot, including web scraping, machine learning, trading algorithms, and account management. Used a combination of supervised and unsupervised learning algorithms to predict the market trends and identify potential opportunities for trading.
4. Testing and Deployment: Tsted the bot extensively to ensure that it was functioning correctly and meeting the client's requirements. Deployed the bot on the client's server and provided documentation and training to ensure smooth operation.

Conclusion
The FIFA Trading Bot is an innovative solution that can help FIFA players automate the trading process and make profits by buying low and selling high. I used Python programming, web scraping, machine learning, and trading strategies to build the bot. The bot can manage multiple accounts on different proxies and IP addresses, bypassing the security checks of the FIFA marketplace. With the FIFA Trading Bot, you can sit back, relax, and let the bot do the trading for you.

The project by collecting a large dataset of images with labeled emotions, age, and gender. I then used this dataset to train a deep learning model using TensorFlow, a popular open-source machine learning framework. Our model used convolutional neural networks (CNNs) to extract features from the input images and make predictions about the emotions, age, and gender of the person in the image.

To improve the accuracy of our model, used transfer learning, a technique that involves using a pre-trained model as a starting point for our own model. We used the ResNet-50 pre-trained model as a starting point for our own model and fine-tuned it for our specific task of face detection and emotion, age, and gender recognition.

Once our model was trained, we integrated it into a real-time face detection system using OpenCV, a popular computer vision library. Our system was able to detect faces in video frames and provide real-time predictions about the emotions, age, and gender of the person in the frame.

Technologies/Tools Used:
- TensorFlow
- OpenCV
- Python
- NumPy
- Matplotlib
- Jupyter Notebook

Conclusion:
The project involved building an advanced face detection model that can recognize emotions, age, and gender from video frames with incredible accuracy. We achieved this by using deep learning techniques, including transfer learning, and integrating our model into a real-time face detection system using OpenCV. The project demonstrated the power of deep learning in computer vision and the potential applications of facial recognition technology in various fields.

U-Net Medical Image Segmentation: A Complex and Rewarding Project

As a data scientist, you may have faced challenging projects that require in-depth knowledge of machine learning, computer vision, and deep learning. U-Net Medical Image Segmentation is one such complex and rewarding project that involves transforming medical images like CT and MRI scans into actionable insights using U-Net medical image segmentation. In this blog post,let's discuss how this project was completed and the technologies and tech-stack used to achieve final complex goals.

Project Introduction
The client approached me with a challenging project that involved segmenting medical images to identify tumors in patients. The primary goal of the project was to develop a deep learning model that could accurately segment CT and MRI images and detect the presence of tumors. The client wanted to use the model to aid doctors in diagnosing and treating cancer patients.

Project Execution
The project started by researching and selecting the best deep learning architecture for medical image segmentation. We decided to use the U-Net architecture because of its high accuracy and speed. We then collected and annotated a dataset of CT and MRI images, where each pixel was labeled with a specific class such as tumor, healthy tissue, or other anomalies. Python was used as our programming language and TensorFlow as our deep learning framework.

We preprocessed the dataset by normalizing the images and splitting the data into training, validation, and testing sets. We then used data augmentation techniques such as rotation, flipping, and scaling to increase the size of the dataset and reduce overfitting. We trained the U-Net model on the training dataset and evaluated its performance on the validation dataset. We fine-tuned the model by adjusting the hyperparameters, such as learning rate, batch size, and number of epochs.

After several iterations, we achieved a high accuracy of over 90% on the validation dataset. We used the trained model to segment new CT and MRI images and detect the presence of tumors. We also developed a web application that allowed doctors to upload medical images and receive predictions from the model in real-time.

Technologies and Tech-stack Used
To complete this project, we used the following technologies and tech-stack:
- Python: as our programming language
- TensorFlow: as our deep learning framework
- NumPy: for numerical computations
- Pandas: for data manipulation
- Matplotlib and Seaborn: for data visualization
- Flask: for developing the web application
- HTML, CSS, and JavaScript: for designing and developing the user interface

Conclusion U-Net Medical Image Segmentation is a complex and rewarding project that involves transforming medical images into actionable insights. By using the U-Net architecture and implementing best practices in deep learning, we were able to develop a model that accurately segmented CT and MRI images and detected tumors. With this project, we were able to aid doctors in diagnosing and treating tumor patients, and make a significant impact in the medical field.

As the world becomes increasingly digitized, organizations and individuals are looking for more secure and efficient ways to protect sensitive data and assets. Biometric iris recognition is one such technology that has gained popularity in recent years due to its high accuracy and reliability. In this blog post, Let's discuss how we leveraged John Daughman's research paper on iris recognition to develop an accurate and efficient biometric security system.

Project Introduction
This was my university's final year project involved developing a biometric security system that could identify and authenticate individuals based on their iris patterns. The project's primary goal was to enhance security and prevent unauthorized access to sensitive areas or information. The plan was for the system to be accurate, efficient, and easy to use.

Project Execution
I started the project by researching and understanding the concept of iris recognition and the techniques used for feature extraction and matching. I then studied John Daughman's research paper on iris recognition, which is considered a seminal work in the field. I used Python as our programming language and OpenCV, a popular computer vision library, to implement the algorithms.

I collected a dataset of iris images from different datasets available online and preprocessed the images by removing noise and enhancing the contrast. I then used Daughman's method for feature extraction, which involves segmenting the iris region and encoding the iris patterns into a unique template. We used the Hamming distance algorithm for template matching, which compares two templates and returns a similarity score.

After several iterations, achieved a high accuracy of over 95% on the dataset, which was split into training and testing sets. We also optimized the system for speed by using parallel processing and caching techniques. We developed a graphical user interface that allowed users to enroll and authenticate themselves by capturing their iris images and comparing them to the stored templates.

Technologies and Tech-stack
Used To complete this project, we used the following technologies and tech-stack:
- Python: as our programming language
- OpenCV: for image processing and feature extraction
- NumPy: for numerical computations
- Matplotlib: for data visualization
- PyQt5: for developing the graphical user interface

Conclusion
Biometric iris recognition is a powerful technology that can enhance security and prevent unauthorized access to sensitive data and assets. By leveraging John Daughman's research paper on iris recognition and implementing the best practices in computer vision and machine learning, I was able to develop an accurate and efficient biometric security system. With this project, we were able to make a significant impact in the field of security and build with a cutting-edge solution for their security needs.

After completing my university project, I sold it to a major tech company 😉.

In the age of artificial intelligence, chatbots have emerged as a game-changer in customer service and user engagement. With the power of natural language processing, chatbots can provide personalized and efficient responses to user queries, 24/7. In this blog post, we will discuss our project on building a chatbot powered by (open-source, free) GPT-3, designed to provide a seamless user experience.

Project Introduction
Our client approached us with a project to develop a chatbot that could interact with users in a natural and conversational manner. The chatbot needed to have the ability to understand and respond to user queries, provide relevant information, and perform actions based on user intent. The client wanted the chatbot to be powered by the latest language model, GPT-3, and integrate with their existing website and mobile application.

Project Execution
We started the project by researching and understanding the concept of natural language processing and the state-of-the-art techniques used for chatbot development. We then explored the capabilities of GPT-3 and its ability to generate human-like responses to a wide range of queries.

We used Python as our programming language and integrated GPT-3 with two different chatbot frameworks: Jarvis and DialoGPT. Jarvis is a lightweight chatbot framework that provides a simple and intuitive interface for building conversational agents, while DialoGPT is a more complex framework that allows for greater customization and control over the chatbot's responses.

We fine-tuned GPT-3 using our client's domain-specific data and integrated the chatbot with their website and mobile application. The chatbot was designed to provide personalized responses to user queries and perform actions such as booking appointments or providing directions.

We tested the chatbot extensively using a range of user queries and scenarios and achieved a high accuracy of over 90%. We also optimized the chatbot for speed and responsiveness, ensuring a seamless user experience.

Technologies and Tech-stack Used
To complete this project, we used the following technologies and tech-stack:
- Python: as our programming language
- GPT-3 (open-source and free): for language generation and response generation
- Jarvis and DialoGPT: for chatbot development and integration
- Flask: for web application development
- HTML, CSS, and JavaScript: for website application development

Conclusion
With the power of GPT-3 and advanced chatbot frameworks like Jarvis and DialoGPT, we were able to develop a chatbot that can interact with users in a natural and conversational manner. By integrating the chatbot with our client's website and mobile application, we provided a seamless user experience and enabled them to engage with their users 24/7. With this project, we explored the limitless possibilities of talking AI and demonstrated the potential of chatbots in revolutionizing customer service and user engagement.

Instaloader - Open-Source Contribution: Becoming a Contributor to the Popular Instagram Downloader

Open-source software has become a driving force in the world of software development. It allows developers to collaborate and contribute to projects that have a significant impact on the community. Instaloader is one such open-source project that has become very popular among Instagram users who want to download photos, videos, and stories from the platform. In this post, we will discuss our experience contributing to Instaloader and the benefits of open-source software.

Project Introduction
I am passionate about contributing to open-source software and wanted to make a meaningful contribution to a project that could benefit many users. I discovered Instaloader, a Python-based tool that allows users to download media from Instagram and access profile information. Instaloader was well-maintained, had a large community of contributors, and had several feature requests and bug reports on its GitHub repository. I decided to take up the challenge and contribute to the project.

Project Execution
I started by studying the Instaloader codebase and familiarizing ourselves with the tool's architecture. Then explored the open issues and feature requests on the project's GitHub repository and selected a few that we could work on. I created a fork of the project's repository to make changes.

I implemented bug fixes and feature requests, including improvements to the media download functionality and the addition of new command-line options. I submitted pull requests to the main repository and engaged with the project's maintainers to ensure that our changes aligned with the project's vision.

I received feedback on our changes, made the necessary revisions, and finally had our pull requests merged into the main repository. I was excited to have my contributions accepted by the community and to have made a meaningful impact on a widely-used tool.

Benefits of Open-Source Contribution
Contributing to open-source projects like Instaloader has several benefits, including:
- Gaining experience with real-world software development practices
- Improving technical skills and knowledge of programming languages and frameworks
- Collaborating with a community of developers and receiving feedback on code
- Building a portfolio of work that can be showcased to potential employers

Conclusion
In this project, I had the opportunity to contribute to Instaloader, a popular open-source project that provides a valuable service to Instagram users. By implementing bug fixes and feature requests, I learned about real-world software development practices and improved our technical skills. I also engaged with a community of developers and received feedback on the code. I encourage developers to consider contributing to open-source projects and to experience the benefits of collaborating with a global community of developers.

Disco Diffusion is an open-source project that uses deep learning techniques to generate visually stunning AI art and animations. This project was created by a group of developers who wanted to explore the potential of AI art and bring it to a wider audience.

As a developer, I had the opportunity to contribute to this project by adding new features and fixing bugs. The project was built using the Python programming language and the PyTorch deep learning library.

To get started, I first cloned the Disco Diffusion GitHub repository and set up a development environment on my local machine. I then reviewed the existing codebase and the project's issue tracker to find areas where I could make a contribution.

One of the first contributions I made was to add a new feature that allowed users to specify the number of frames to use when generating an animation. This feature was requested by several users who wanted more control over the animation generation process.

To implement this feature, I added a new command-line argument to the Disco Diffusion script and modified the existing code to use this argument to control the number of frames used in the animation. I also added documentation to the project's README file to explain how to use this new feature.

Another contribution I made was to fix a bug that caused the generated animations to sometimes have artifacts or glitches. After some debugging, I discovered that this was caused by a bug in the PyTorch library that was being used by Disco Diffusion. To fix this issue, I updated the PyTorch library to a newer version that had the bug fix.

Throughout my contributions to Disco Diffusion, I learned a lot about deep learning and AI art. I also gained experience working with an open-source project and collaborating with other developers.

Overall, Disco Diffusion is a complex and innovative project that demonstrates the power of deep learning and AI art. Contributing to this project was a challenging and rewarding experience that allowed me to further develop my technical skills and gain valuable experience working with a real-world project.