What makes Python language an ideal choice for developing applications? It offers higher-level functions and higher-level data types than other programming languages. It also provides easy way to access and manipulate those data in an efficient way. Python is used regularly in mainstream software such as AI, data science, networking, gaming and more.

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youtube-dl 2021.12.17

TensorFlow Official Models 2.7.1

v4.18.0: Checkpoint sharding, vision models

youtube-dl

youtube-dl 2021.12.17

models

TensorFlow Official Models 2.7.1

thefuck

transformers

v4.18.0: Checkpoint sharding, vision models

flask

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Trending Kits in Python

object-tracking-system

Object Tracking System

<div><img src="https://kandi.dev/owassets/object-tracking-system-banner.png" alt="Object Tracking" style="height:auto;max-width:100%;"/> Real-time object tracking system is a technology used to track objects (from images, videos, and webcam) 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. Please see below a sample solution kit to jumpstart your solution on Real-time object tracking system. To use this kit to build your own solution, scroll down to the Kit Deployment Instructions sections. Source code included so that you can customize it for your requirement. <button class="MuiButtonBase-root MuiButton-root MuiButton-contained editexp MuiButton-containedSecondary click_collections_oneclickfiledownload " onclick="location.href='https://github.com/kandikits/Yolov5_DeepSort_Pytorch/raw/master/kit_installer.zip'" type="button"> ⬇️ Download 1-Click Installer </button>

kandi

1-Click Install

food-wastage-recommender-system

food Wastage Recommender system

Data Summary : Resources that have been shared for the problem statement has info about food items and their description. Also we had order info from both donor and from consumer side orders on daily basis. We have done data cleaning and preprocessing as required. Recommendation system : In order to control the food wastage we have built Recommendation engine using "item-item based collaborative filtering" to recommend the items which expire early and are more in consumption. Data Analysis : We have developed a dashboard on tableau using cleaned datasets and these analysis can be used to match supply-demand of different types of food and to give an overview to the NGO, donors and consumers on how to reduce the food wastage. Use the open source, cloud APIs, or public libraries listed below in your application development based on your technology preferences, such as primary language. The below list also provides a view of the components' rating on different dimensions such as community support availability, security vulnerability, and overall quality, helping you make an informed choice for implementation and maintenance of your application. Please review the components carefully, having a no license alert or proprietary license, and use them appropriately in your applications. Please check the component page for the exact license of the component. You can also get information on the component's features, installation steps, top code snippets, and top community discussions on the component details page. The links to package managers are listed for download, where packages are readily available. Otherwise, build from the respective repositories for use in your application. You can also use the source code from the repositories in your applications based on the respective license types.

kandi

1-Click Install

buildwithai2021

Team CE.net

This kit is helpful for audio analysis. Audio information plays a rather important role in the increasing digital content that is available today; resulting in a need for methodologies that automatically analyze such content. Speaker Identification is one of the vital field of research based upon Voice Signals. Its other notable fields are: Speech Recognition, Speech-to-Text Conversion, and vice versa, etc. Mel Frequency Cepstral Coefficient (MFCC) is considered a key factor in performing Speaker Identification. But, there are other features lists available as an alternate to MFCC; like- Linear Predictor Coefficient (LPC), Spectrum Sub-band Centroid (SSC), Rhythm, Turbulence, Line Spectral Frequency (LPF), ChromaFactor, etc. Gaussian Mixture Model (GMM) is the most popular model for training on our data. The training task can also be executed on other significant models; viz. Hidden Markov Model (HMM). Recently, most of the model training phase for a speaker identification project is executed using Deep learning; especially, Artificial Neural Networks (ANN). In this project, we are mainly focused on implementing MFCC and GMM in pair to achieve our target. We have considered MFCC with “tuned parameters” as the primary feature and delta- MFCC as secondary feature. And, we have implemented GMM with some tuned parameters to train our model. We have performed this project on two different kinds of Dataset; viz. “VoxForge” Dataset and a custom dataset which we have prepared by ourselves. We have obtained an outstanding result on both of these Datasets; viz. 100% accuracy on VoxForge Dataset and 95.29 % accuracy on self prepared Dataset. We demonstrate that speaker identification task can be performed using MFCC and GMM together with outstanding accuracy in Identification/ Diarization results.

buildwithai-theme1

BuildWithAI Challenge 1

The Pandemic has impacted education - classes have moved online, students have been isolated on screens and coping with this change. Despite the challenges, the digital school has the potential to transform education. How can we empower students and teachers in this new digital school paradigm. In this challenge, we are inviting AI-powered solutions for the digital school of tomorrow. <h3>DATASET: Feel free to use any dataset of your choice.</h3> There is no restriction and you can use any data set. <b>Please see the section - DATASETS below for sample datasets to help as a reference. </b> Here are sample areas you could choose to tackle in this challenge. Feel free to come up with your own ideas as well. 1. Higher Education and Career Recommendation 2. Mental Health Monitor and Virtual Companion 3. Adaptive Learning Curriculum 4. Class availability scheduling for social distancing 5. Compliance of COVID guidelines - masking, distancing, temperature Please see below for guidelines and reusable libraries to jumpstart your solution. This kit provides reference to open-source libraries which can be reused as core building blocks for creating a predictive solution. You may use any other open-source libraries also as relevant to your solution. Reusability is a key design principle and will be scored positively in your submission. These reference reusable libraries are spread over functions in Data Analysis and Mining, Data Visualization, Machine Learning, and other key areas to build AI solution. Below are the guidelines for creating your submission kit for this challenge. 1. See Product Tour > Creating a kit from the kandi header. This will guide you on creating your kit. 2. Your submission kit should contain the kit heading/ name, description of the solution, and other relevant information. 3. Create groups with logical names and add the libraries to the respective sections. 4. You solution can be built with any libraries other than the libraries provided here for reference. 5. The project source library for the solution built in the hackathon should be hosted in GitHub and listed in your kit under 'Kit Solution Source' section. 6. Any deployment instructions should be added under 'Kit Deployment Instructions' section of the kit. 7. Add any additional information, links under the kit description or group descriptions.

object-tracking-system

Object Tracking System

<div><img src="https://kandi.dev/owassets/object-tracking-system-banner.png" alt="Object Tracking" style="height:auto;max-width:100%;"/> Real-time object tracking system is a technology used to track objects (from images, videos, and webcam) 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. Please see below a sample solution kit to jumpstart your solution on Real-time object tracking system. To use this kit to build your own solution, scroll down to the Kit Deployment Instructions sections. Source code included so that you can customize it for your requirement. <button class="MuiButtonBase-root MuiButton-root MuiButton-contained editexp MuiButton-containedSecondary click_collections_oneclickfiledownload " onclick="location.href='https://github.com/kandikits/Yolov5_DeepSort_Pytorch/raw/master/kit_installer.zip'" type="button"> ⬇️ Download 1-Click Installer </button>

kandi

1-Click Install

food-wastage-recommender-system

food Wastage Recommender system

Data Summary : Resources that have been shared for the problem statement has info about food items and their description. Also we had order info from both donor and from consumer side orders on daily basis. We have done data cleaning and preprocessing as required. Recommendation system : In order to control the food wastage we have built Recommendation engine using "item-item based collaborative filtering" to recommend the items which expire early and are more in consumption. Data Analysis : We have developed a dashboard on tableau using cleaned datasets and these analysis can be used to match supply-demand of different types of food and to give an overview to the NGO, donors and consumers on how to reduce the food wastage. Use the open source, cloud APIs, or public libraries listed below in your application development based on your technology preferences, such as primary language. The below list also provides a view of the components' rating on different dimensions such as community support availability, security vulnerability, and overall quality, helping you make an informed choice for implementation and maintenance of your application. Please review the components carefully, having a no license alert or proprietary license, and use them appropriately in your applications. Please check the component page for the exact license of the component. You can also get information on the component's features, installation steps, top code snippets, and top community discussions on the component details page. The links to package managers are listed for download, where packages are readily available. Otherwise, build from the respective repositories for use in your application. You can also use the source code from the repositories in your applications based on the respective license types.

kandi

1-Click Install

buildwithai2021

Team CE.net

This kit is helpful for audio analysis. Audio information plays a rather important role in the increasing digital content that is available today; resulting in a need for methodologies that automatically analyze such content. Speaker Identification is one of the vital field of research based upon Voice Signals. Its other notable fields are: Speech Recognition, Speech-to-Text Conversion, and vice versa, etc. Mel Frequency Cepstral Coefficient (MFCC) is considered a key factor in performing Speaker Identification. But, there are other features lists available as an alternate to MFCC; like- Linear Predictor Coefficient (LPC), Spectrum Sub-band Centroid (SSC), Rhythm, Turbulence, Line Spectral Frequency (LPF), ChromaFactor, etc. Gaussian Mixture Model (GMM) is the most popular model for training on our data. The training task can also be executed on other significant models; viz. Hidden Markov Model (HMM). Recently, most of the model training phase for a speaker identification project is executed using Deep learning; especially, Artificial Neural Networks (ANN). In this project, we are mainly focused on implementing MFCC and GMM in pair to achieve our target. We have considered MFCC with “tuned parameters” as the primary feature and delta- MFCC as secondary feature. And, we have implemented GMM with some tuned parameters to train our model. We have performed this project on two different kinds of Dataset; viz. “VoxForge” Dataset and a custom dataset which we have prepared by ourselves. We have obtained an outstanding result on both of these Datasets; viz. 100% accuracy on VoxForge Dataset and 95.29 % accuracy on self prepared Dataset. We demonstrate that speaker identification task can be performed using MFCC and GMM together with outstanding accuracy in Identification/ Diarization results.

buildwithai-theme1

BuildWithAI Challenge 1

The Pandemic has impacted education - classes have moved online, students have been isolated on screens and coping with this change. Despite the challenges, the digital school has the potential to transform education. How can we empower students and teachers in this new digital school paradigm. In this challenge, we are inviting AI-powered solutions for the digital school of tomorrow. <h3>DATASET: Feel free to use any dataset of your choice.</h3> There is no restriction and you can use any data set. <b>Please see the section - DATASETS below for sample datasets to help as a reference. </b> Here are sample areas you could choose to tackle in this challenge. Feel free to come up with your own ideas as well. 1. Higher Education and Career Recommendation 2. Mental Health Monitor and Virtual Companion 3. Adaptive Learning Curriculum 4. Class availability scheduling for social distancing 5. Compliance of COVID guidelines - masking, distancing, temperature Please see below for guidelines and reusable libraries to jumpstart your solution. This kit provides reference to open-source libraries which can be reused as core building blocks for creating a predictive solution. You may use any other open-source libraries also as relevant to your solution. Reusability is a key design principle and will be scored positively in your submission. These reference reusable libraries are spread over functions in Data Analysis and Mining, Data Visualization, Machine Learning, and other key areas to build AI solution. Below are the guidelines for creating your submission kit for this challenge. 1. See Product Tour > Creating a kit from the kandi header. This will guide you on creating your kit. 2. Your submission kit should contain the kit heading/ name, description of the solution, and other relevant information. 3. Create groups with logical names and add the libraries to the respective sections. 4. You solution can be built with any libraries other than the libraries provided here for reference. 5. The project source library for the solution built in the hackathon should be hosted in GitHub and listed in your kit under 'Kit Solution Source' section. 6. Any deployment instructions should be added under 'Kit Deployment Instructions' section of the kit. 7. Add any additional information, links under the kit description or group descriptions.

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