Python

Python is an object-oriented programming language. Python can do anything almost any other language can do, at comparable speeds. This kit has some simple to use exercises in Python to help someone new to learn how to program in Python and get started with their journey.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Python Repositories with Basic Example Exercises

Basic python CLI programs as examples. This list has programs useful for someone who is a beginner and also someone willing to go advance level.

Some Python Games for Practice

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

AI fake news detector helps detect fake news through binary classification methods. It helps build experiences by controlling the flow of disinformation. It's built on top of various powerful machine learning libraries. The tool works by training a neural network to spot fake articles based on their text content. When you run your own data through the tool, it gives you back a list of articles that it thinks are likely to be fake. You can then train the model further or decide if those results are acceptable or not. In addition to identifying fake news, this model can also be trained to identify real news. This allows you to compare the model's performance across different domains (e.g., politics vs. sports). The following installer and deployment instructions will walk you through the steps of creating an AI fake news detector by using fakenews-detection, jupyter, vscode, and pandas. We will use fake news detection libraries (having fully modifiable source code) to customize and build a simple classifier that can detect fake news articles. kandi kit provides you with a fully deployable AI Fake News Detector. Source code included so that you can customize it for your requirement.

With this kit, you can

1. Use a pre-trained model for detecting fake news.

2. Train the model on your custom dataset.

3. Expose the fake news detection as an API

Add-on on examples are also included as given below

1. Use web scraper to automatically make your training dataset.

2. Visualise training and prediction data for useful insights.

Instructions to Run

Follow the below instructions to run the solution.

1. Locate and open the FakeNewsDetection-starter.ipynb notebook from the Jupyter Notebook browser window.

2. Execute cells in the notebook by selecting Cell --> Run All from Menu bar

3. Once all the cells of the notebook are executed, the prediction result will be written to the file 'fake_news_test_output.csv'

Training with your dataset:

1. Add news articles to a csv file under a column name 'news_text'.

2. Add corresponding labels as 'real' or 'fake' denoting whether a news article is real or not.

3. You can refer to the file 'fake_news_train.csv' for an example.

4. Set the variable for training file in the notebook under Variables section.

Testing with your dataset:

1. Add news articles to a csv file under a column name 'news_text'.

2. You can refer to the file 'fake_news_test.csv' for an example.

3. Set the variable for testing file in the notebook under Variables section.

You can execute the cells of notebook by selecting Cell from the menu bar.

For any support, you can reach us at FAQ & Support

Libraries useful for this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

Text mining

Libraries in this group are used for analysis and processing of unstructured natural language. The data, as in its original form aren't used as it has to go through processing pipeline to become suitable for applying machine learning techniques and algorithms.

Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning.

Data Visualization

The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.

Troubleshooting

1. If you encounter any error related to MS Visual C++, please install MS Visual Build tools

2.While running batch file, if you encounter Windows protection alert, select More info --> Run anyway.

3.During kit installer, if you encounter Windows security alert, click Allow.

4. If you encounter Memory Error, check if the available memory is sufficient and it is proportion to the size of the data being used. For our dataset, the minimum required memory is 8GB.

If your computer doesn't support standard commands from windows 10, you can follow the instructions below to finish the kit installation.

1. Click here to install python

2. Click here to download the repository

3. Extract the zip file and navigate to the directory 'fakenews-detection-main'

4. Open terminal in the extracted directory 'fakenews-detection-main'

5. Install dependencies by executing the command 'pip install -r requirements.txt'

6. Run the command ‘jupyter notebook’ and select the notebook ‘FakeNewsdetection-starter.ipynb’ on the browser window.

Support

For any support, you can reach us at FAQ & Support

Deepfake detection is identifying manipulated or synthetic media content using machine learning algorithms and computer vision techniques. It detects anomalies in facial and body movements, and other visual artifacts.

In this kit, we build a Deepfake Detection Engine using the popular Facenet_pytorch is a Python library that provides implementations of deep learning models for face recognition tasks. It includes pre-trained models such as

1. MTCNN (Multi-Task Cascaded Convolutional Networks) for face detection and alignment, and
2. InceptionResnetV1 for detecting whether an image is fake or real.

We use these two models to detect and recognize faces in images with high accuracy. The library is built on top of PyTorch, a popular open-source machine learning framework, and provides an easy-to-use API for face recognition tasks

Libraries used in this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Jupyter Notebook is used for our development.

Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

Kit Solution Source

API Integration

Support

For any support, you can reach us at OpenWeaver Community Support

Generative artificial intelligence (AI) describes algorithms that help in creating/generating new content, including audio, code, images, text and videos. 

In this kit, we build a real-time Voice-to-Image Generator using the concept of Generative AI. It is carried out in two steps:

• Voice-to-text conversion - The speech is captured in real-time through the microphone and converted to text using state-of-the-art Opensource AI models from OpenAI and Whisper libraries.

• Text to Image Generation - The converted text is provided as input to the state-of-the-art Image Generation models like Dalle-2, and the image is thus generated.

Libraries used in this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Jupyter Notebook is used for our development.

Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

Kit Solution Source

UI App Integration

Support

For any support, you can reach us at OpenWeaver Community Support

This Predictive Analytics kit provides an analytical view of students’ performance in mathematics and predicts grades to be scored in the final test.

The key features of this solution are:

• Analysis of grades of students
• Visualisation of patterns
• Prediction of grade in the final test

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Data Mining

Our solution integrates data from various sources, and we have used below libraries for exploring patterns in these data and understanding correlation between the features.

Data Visualisation

The patterns and relationships are identified by representing data visually and below libraries are used for that.

Machine learning

Below libraries and model collections helps to create the machine learning models for the core prediction of use case in our solution.

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

The use case of AI Course Recommender System is to provide personalized recommendation to the user based on their interest, course they can take and their current knowledge. This system will be able to recommend course based on user’s interest, current knowledge, analytical view of students’ performance in mathematics and recommends if a student can consider math subject for his/ her higher education. The recommended course will be based on the information of user’s profile, analysis of grades of students, visualization of patterns, prediction of grade in final test, and some rules that were set by their instructor. Using machine learning algorithms, we can train our model on a set of data and then predict the ratings for new items. This is all done in Python using numpy, pandas, matplotlib, scikit-learn and seaborn. kandi kit provides you with a fully deployable AI Course Recommender System. Source code included so that you can customize it for your requirement.

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Data Mining

Our solution integrates data from various sources, and we have used below libraries for exploring patterns in these data and understanding correlation between the features.

Data Visualisation

The patterns and relationships are identified by representing data visually and below libraries are used.

Machine learning

Below libraries and model collections helps to create the machine learning models for the core prediction of use case in our solution.

Federated Learning can train machine learning models on data from different hospitals, banks and autonomous vehicles without sharing sensitive data. But how do you create a Federated learning application? The answer is the kandi 1-click solution kit for Credit-risk-federated-learning.

Certainly, Federated Learning can be applied in the credit risk scenario to improve credit risk models' accuracy without compromising customer data privacy.

Banks collect and centralize customer data to train their credit risk models in the traditional approach. However, this approach can be challenging due to regulatory compliance, data privacy, and security concerns. Federated Learning addresses these challenges by allowing banks to train their credit risk models on customer data without transferring it to a centralized location.

This fully editable source code builds your Credit risk federated learning in minutes. The entire solution is available as a package to download from the source code repository.

Federated Learning in credit risk scenarios can have several benefits, including:

• Improved accuracy: Federated Learning allows banks to train models on a larger and more diverse dataset, leading to better accuracy.
• Data privacy: Federated Learning ensures that sensitive customer data is kept private and secure, which is critical in the context of credit risk.
• Regulatory compliance: Federated Learning can help banks comply with regulations around data privacy and security.

Troubleshooting

1. Install the Microsoft Visual C++ Redistributable for Visual Studio 2022 in case the kit doesn't successfully run on your windows system.
2. In case, step 1 doesn't solve your issue, set up Microsoft build Tools.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Data Pre-processing

Numpy and Pandas are powerful tools for data preprocessing in machine learning. They provide tools for handling missing data, feature scaling, one-hot encoding, data normalization, and transformation.

These tools can help you to prepare your data for machine learning and improve the performance of your models.

Machine learning

Scikit-learn is a powerful and versatile machine learning library in Python that provides a wide range of tools and algorithms for building and training machine learning models. It is widely used in academia and industry for various machine learning applications.

Federated Learning Framework

Flower is an open-source framework for Federated Learning that provides tools and APIs to simplify the development and deployment of Federated Learning models. Flower is designed to make it easier for developers to implement Federated Learning in their applications by providing a flexible and scalable platform for building and training models.

Kit Solution Source

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

Large Language Models are foundation models that utilize deep learning in natural language processing and natural language generation tasks. Typically these models are trained on billions of parameters with a huge corpus of data.

GPT4all provides an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue. GPT4All is a 7B parameter LLM trained using a Low-Rank Adaptation (LoRA) method, yielding 430k post-processed instances, on a vast curated corpus of over 800k high-quality assistant interactions.

In this kit, we will use GPT4All to create a content generator, similar to ChatGPT, without the need for API keys and Internet to create content.

Libraries used in this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Jupyter Notebook is used for our development.

Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

Kit Solution Source

API Integration

Support

For any support, you can reach us at OpenWeaver Community Support

Angry Birds python game is a Finnish action-based media franchise. It is created by Rovio Entertainment.

The game series focuses on a flock of birds referred to by the same name who try to save their eggs from the green-colored pigs. Angry Birds game is written in python using Pygame and Pymunk which are open-source modules specifically intended to help you make games and other multimedia applications. Pygame can be used to load background images, sounds, and buttons, which makes the UI interactions more efficient. Pymunk is best when you need 2d physics from python which can be used for demos or simulations. Pymunk is basically built on top of the 2D physics library Chipmunk.

Troubleshooting

1. While running batch file, if you encounter Windows protection alert, select More info --> Run anyway
2. During kit installer, if you encounter Windows security alert, click Allow

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode is used for development and debugging. VSCode is used to get a typical experience of IDE for developers.

Gaming Libraries

Pygame helps in providing computer graphics and audio libraries.

Pymunk is a easy-to-use pythonic 2d physics library that can be used whenever you need 2d rigid body physics from Python.

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

A Tower Defense Game between Humans and Aliens. Kill as much as aliens as you can to upgrade and discover new humans.

Development Environment

VSCode is used for development and debugging. VSCode is used to get a typical experience of IDE for developers.

Gaming Libraries

Pygame helps in providing computer graphics and audio libraries.

Pymunk is an easy-to-use pythonic 2d physics library that can be used whenever you need 2d rigid body physics from Python.

Troubleshooting

1. While running batch file, if you encounter Windows protection alert, select More info --> Run anyway
2. During kit installer, if you encounter Windows security alert, click Allow

Support

If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc.

Bias is prevalent in every aspect of our lives. Our brains are hardwired to categorize things we encounter in order to make sense of the complicated world around us. However, biases can cause us to form prejudices against others, which allows for egregious inequalities to form between different demographics.

While bias comes in many forms, bias words in writing is one form. Implicit bias in letter writing or evaluations negatively affects individuals at every stage of their career.

In this challenge, we are inviting to build a solution for detecting bias in writings such as letter of recommendations, Job Descriptions etc with respect to gender and race for promoting equity. You can choose any topic of your choice. The sample solution kit helps to detect gender bias.

Instruction to Run

Follow below instructions to run the solution.

1. Locate and open the gender-bias.ipynb notebook from the Jupyter Notebook browser window.
2. Execute cells in the notebook by selecting Cell --> Run All from Menu bar

For running it with your text,

1. Open letterofRecW file from the location data/input from gender-bias.ipynb location.
2. Update text in the letterofRecW file.
3. Execute cells in the notebook by selecting Cell --> Run All from Menu bar.
4. Output will be stored in a file gender-biased-words.txt in the location data/output. Output text is in json format. Output data format is: name - detector name. e.g. "Terms biased towards women" summary - summary of the detected bias flags - flag the detected bias words. e.g. "leader" You can additionally create your own detectors for race and dictionary dataset as well as other enhancements for additional score.

Troubleshooting

1. While running batch file, if you encounter Windows protection alert, select More info --> Run anyway
2. During kit installer, if you encounter Windows security alert, click Allow

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Text Mining

Libraries in this group are used for analysis and processing of unstructured natural language. The data, as in its original form aren't used as it has to go through processing pipeline to become suitable for applying machine learning techniques and algorithms.

Testing

The libraries listed here can be used for unit testing as well as integration testing

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

The next word predictor is an exciting feature that helps you type faster on your mobile phone. It predicts the next word in the context you want to type. It is a very useful tool for people who type often and make mistakes while typing. It can be leveraged for auto-suggestion features in messenger and search engine apps.

The next word predictor makes it easy for readers to understand what exactly you are trying for them to read about.

• Next word predictor is a very useful feature as it increases the readability of your content as well as makes it more understandable for readers.
• Saves time by reducing the number of typos and grammatical errors in your content.
• Modify source code to customize as per your requirements.

Instructions to Run

Follow the below instructions to run the solution.

1. Locate and open the 'Next Word Predictor.ipynb' notebook from the Jupyter Notebook browser window.
2. Execute cells in the notebook by selecting Cell --> Run All from the Menu bar.
3. Once all the cells of the notebook are executed, the last interactive cell (Customisation) will be active, there we can give the input data or we can give the input text in the variable 'text_seq' under the variable section.

Input

text_seq = "I'm gonna make him an offer he can't"

Output

['refuse', 'resist', 'take', 'deny', 'get']

Troubleshooting

1. If you encounter any error related to MS Visual C++, please install MS Visual Build tools
2. While running batch file, if you encounter Windows protection alert, select More info --> Run anyway.
3. During kit installer, if you encounter Windows security alert, click Allow.
4. If you encounter Memory Error, check if the available memory is sufficient and it is proportion to the size of the data being used. For our dataset, the minimum required memory is 8GB.

If your computer doesn't support standard commands from windows 10, you can follow the instructions below to finish the kit installation.

1. Install python
2. Download the repository
3. Extract the zip file and navigate to the directory 'next-word-prediction-main'
4. Open terminal in the extracted directory 'next-word-prediction-main'
5. Install dependencies by executing the command 'pip install -r requirements.txt'
6. Run the command ‘jupyter notebook’ and select the notebook ‘Next Word Predictor.ipynb’ on the browser window.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web-based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

Text Mining

Libraries in this group are used for analysis and processing of unstructured natural language.

Machine Learning

The library offers state-of-the-art pre-trained models for Natural Language Processing (NLP).

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

AI-powered emoji detectors can help increase engagement with their customers. It will help them to build strong relationships with their customers. The emoji detector will help you in analyzing your audience and their preferences so that you can deliver the right content. You can also use the technology to provide customer support to your customers by providing customized answers.

One of the most important aspects of AI-Powered Emoji Detector is that it will help you in detecting any kind of emotions and expressions on your face OR hand gestures from a web camera. It will help in detecting whether you are happy, sad, or angry, and so on. This technology is also used for predicting different kinds of expressions like happiness, fear, sadness, etc.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Image Preparation and Processing

These libraries help in preparing data by annotating and labelling images. Also processes images for running machine learning algorithm. We use opencv library for capturing frames from live streaming videocam.

Data Analysis/Manipulation

These libraries help in analyzing data and doing data manipulations.

Machine Learning

Below libraries and model collections helps to create the machine learning models for the core recognition use cases in our solution.

Utilities

The below utility library helps in storing huge amounts of numerical data and manipulate that data easily from NumPy.

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

Real-time object tracking system is a technology used to track objects in real time. It can be used for security purposes or for commercial purposes. Tracking can be done for video formats and live streaming webcam.

The real-time object tracking system has many applications, such as in retail stores, airports, stadiums and other places where security is important. The system can be used to monitor customer activity in stores, track inventory and detect shoplifting. It can also be used to increase safety in public places by monitoring the movements of pedestrians or vehicles.

For a detailed tutorial on installing & executing the solution as well as learning resources including training & certification opportunities, please visit the OpenWeaver Community

Development Environment

VSCode and Jupyter Notebook can be used for development and debugging. Jupyter Notebook is a web-based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Object Detection and Tracking

The following libraries have a set of pre-trained models which could be used to identify objects and track them from live streaming videos.

Machine Learning Libraries

The following libraries could be used to create machine learning models which focus on the vision, extraction of data, image processing, and more. Thus making it handy for the users.

Support

If you need help using this kit, you may reach us at the OpenWeaver Community.

Disease predictor is a way to recognize patient’s health by applying data mining and machine learning techniques on patient treatment history.

Symptoms, Diagnosis for Personalized Healthcare Services for a Predictive Analytic Perspective. Pandas library is used in this kandi kit to predict the probability of disease. The kit has used pandas to load datasets and visualize the data, NumPy to implement our algorithm, and sklearn-pandas to build our model.

In this project we will be using Pandas and Scikit-Learn to create a model that predicts whether or not a patient has a disease based on their demographics and lab results. We will also be using Jupyter Notebook to write code interactively so that we can see how our model performs when we change various parameters such as the number of features, amount of training data, etc.

kandi kit provides you with a fully deployable Disease Predictor. Source code included so that you can customize it for your requirement.

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers. Jupyter Notebook is used for our development.

Exploratory Data Analysis

For extensive analysis and exploration of data, and to deal with arrays, these libraries are used. They are also used for performing scientific computation and data manipulation.

Data Visualization

The patterns and relationships are identified by representing data visually and below libraries are used for generating visual plots of the data.

Support

If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc .

Super Mario is frequently cited as one of the greatest video games of all time. This kit is a classic remake of Super Mario Bros. developed in python. This kit includes only level 1-1.

Development Environment

VSCode is used for development, debugging and are used to get a typical experience of IDE for developers.

Gaming Libraries

Pygame helps in providing computer graphics and audio libraries. PyTMX helps to load maps for games.

Support

If you need help to use this kit, you can email us at kandi.support@openweaver.com or direct message us on Twitter Message @OpenWeaverInc.

Python Machine Learning libraries help develop supervised and unsupervised learning, data pre-processing, feature extraction tools, and deep learning. 

Following are the top use cases of these shortlisted libraries for Python Machine Learning,

• Pre-processing of data that includes data cleaning and feature engineering tasks such as normalization, imputation, missing value treatment, and outlier detection.
• Model selecting and optimizing, such as cross-validation, hyperparameter tuning, and model selection metrics.
• Visualizations to understand data and results. This includes visualizing data distributions, feature importance, and model performance.
• Developing algorithms, including supervised learning algorithms (e.g. linear regression, logistic regression, support vector machines, decision trees, random forests, and neural networks) as well as unsupervised learning algorithms (e.g. clustering, dimensionality reduction, and anomaly detection).
• Calculating performance metrics such as accuracy, precision, recall, and F1 score.

The following is a list of the 18 most popular open-source Python libraries for Machine Learning,

keras:  

• It provides a high-level API for building and training deep neural networks.   
• Keras allows you to define and incorporate custom layers and loss functions.    
• Configure Keras to run on top of deep learning frameworks like TensorFlow, etc.

Scikit-Learn:  

• It is an essential library in the field of machine learning and data science.  
• It provides tools for cross-validation, hyperparameter tuning, and model selection.    
• The library runs on top of other scientific Python libraries like NumPy and SciPy.   

Pandas:  

• It is a popular Python library for data manipulation and analysis.   
• It offers tools for data cleaning. This includes handling missing values, data alignment, and data type conversion.   
• It supports time series data, making it valuable for financial analysis and forecasting.   

YOLOv5:  

• "You Only Look Once version 5," is a popular computer vision model for object detection. 
• It is popular for its real-time object detection capabilities. 
• It has improved upon the accuracy of its predecessors while maintaining its speed. 

Ray:  

• It is an open-source distributed computing framework used in Python.   
• It enables you to parallelize and distribute Python applications.   
• It helps with low-latency, high-throughput computing tasks.   

ML-From-Scratch:  

• This helps you gain a deep understanding of the underlying algorithms and mathematics.   
• This allows you to customize it for your specific problem and data. This makes it more effective and efficient.   
• Building models from scratch provides insight into optimization techniques.   

examples:  

• It helps in AI, ML, DL, Pytorch, TensorFlow applications.   
• This library in PyTorch is essential for working with computer vision tasks.   
• You can access pre-trained models like ResNet, VGG, and AlexNet through "torchvision.models".   

Paddle:  

• It is an open-source deep learning platform developed by Baidu.  
• It is a powerful deep learning framework, like TensorFlow and PyTorch.   
• It focuses on simplicity and efficiency.  

rasa:  

• It is an open-source Python library designed for building conversational AI apps.   
• It provides tools for creating and managing conversational flows.  
• It supports many languages and can helps in a global context.  

horovod:  

• It is a popular library in Python used for distributed deep learning.   
• It enables you to scale your DL models to many GPUs and even across many machines.   
• It supports various deep learning frameworks like TensorFlow, PyTorch, and MXNet.   

mlflow:  

• It is an open-source platform for managing the end-to-end machine learning lifecycle.  
• It allows you to log and compare experiments.  
• It provides tools for packaging models in a standard format.

imgaug:  

• It is an important tool for image augmentation. It is especially used in machine learning and computer vision tasks.   
• It allows you to customize augmentation pipelines to suit your specific needs.    
• It works well with other popular libraries like OpenCV and NumPy.  

ChatterBot:  

• It provides a framework and pre-built components. That makes it easier to create chatbots.   
• This library often includes NLP capabilities. This allows chatbots to understand and generate human-like text responses.  
• These libraries offer options for customizing the behavior and responses of chatbots.

nni:  

• NNI handles distributed training, making it suitable for large-scale experiments.  
• NNI is important for streamlining and improving the machine learning model development process.  
• It automates and optimizes ML model selection and hyperparameter tuning.    

numpy-ml:  

• It is a fundamental library in the Python ecosystem. It is especially used in the context of machine learning and data science.  
• It is open-source and has a large and active community.  
• It is crucial for performing efficient numerical and array-based operations.  

tpot: 

• It is a Python library for automated machine learning (AutoML).  
• This includes feature selection, data preprocessing, and the choice of models.
• It employs techniques like cross-validation to reduce the risk of overfitting.    

autokeras:  

• It is an open-source library for automated machine learning (AutoML).  
• It simplifies the process of building and training machine learning models.  
• It is accessible to both beginners and experienced ML practitioners.  

pattern:  

• It is often referred to as a design pattern library.   
• It is a collection of reusable solutions to common software design problems.
• These patterns help developers create more efficient, maintainable, and scalable code.  

FAQ 

1. What is scikit-learn?  

It is an ML library for Python. That provides simple and efficient tools for data analysis and modeling. It offers a wide range of algorithms for classification, regression, clustering, and more.    

2. What is PyTorch?   

PyTorch is an open-source machine learning library. It is developed by Facebook's AI Research lab. It helps with deep learning and provides dynamic computation graphs. This makies it popular among researchers.    

3. What is Keras?   

Keras is an open-source deep learning API. That runs on top of other deep learning frameworks like TensorFlow and Theano. It's designed to be and allows for rapid prototyping of neural networks.   

4. How do I install these libraries?  

You can install these libraries using Python's package manager, pip. For example, you can install scikit-learn with pip install scikit-learn. Also, install TensorFlow with pip install tensorflow, and PyTorch with pip install torch.    

5. What is the difference between a tensor and an array in TensorFlow?  

In TensorFlow, a tensor is a multi-dimensional array. This array can be placed on GPU for accelerated computation. It is like NumPy arrays but optimized for deep learning tasks. 

This is related to water treatment and purification.

DataViz Group

This helps with DaaViz

Cyber Data Analytics Group

This is for Cyber Data Analytics Group

zeronet group

this is zeronet group

Automation Group

this is part of Automation Group

AI has been to build intelligent agents that can understand the vision and language inputs and communicate with humans through natural language. 

Vision and language, two of the most fundamental methods for humans to perceive the world, are also two key cornerstones of AI. A longstanding goal of AI has been to build intelligent agents that can understand the world through vision and language inputs, and communicate with humans through natural language.

In order to achieve this goal, vision-language pre-training has emerged as an effective approach, where deep neural network models are pre-trained on large scale image-text datasets to improve performance on downstream vision-language tasks, such as image-text retrieval, image captioning, and visual question answering.

Image Captioning and Visual Question and Answering involves the usage of Large Multimodal Models (LMMs). Multimodal Learning seeks to allow computers to represent real-world objects and concepts using multiple data streams. We make use of one such model - Saleforce's BLIP (Bootstrapping Language-Image Pre-training)

Libraries used in this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Jupyter Notebook is used for our development.

Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

Kit Solution Source

App User Interface

Support

For any support, you can reach us at OpenWeaver Community Support

Image Restoration is the process of converting a noisy/blur/low quality image to a better-quality image in terms of resolution, color, noise, and blur.

Image Restoration techniques involve Deblurring, colorization, super resolution enhancement and other popular techniques as well.

This kit provides the solution for

• Restoring old images to colored images
• Upscale and enhance image resolution

The Image colorization is done by a technique called NoGAN from an open source project called "deoldify". Deoldify is a shading rendering model for images and videos that enables the restoration of native color to black and white photos and videos.

The Image Upscaling and enhancement is done through a technique called GFPGAN which uses Generative Adversarial Networks comprising of Deep learning techniques for Face/Image restoration.

Libraries used in this solution

Development Environment

VSCode and Jupyter Notebook are used for development and debugging. Jupyter Notebook is a web based interactive environment often used for experiments, whereas VSCode is used to get a typical experience of IDE for developers.

Jupyter Notebook is used for our development.

Machine Learning

Machine learning libraries and frameworks here are helpful in providing state-of-the-art solutions using Machine learning

App User Interface

Kit Solution Source

Support

For any support, you can reach us at OpenWeaver Community Support