Hey guys,
As you all know, the purpose of this community is to share notes and grow together. Hence, today I am sharing with you an app called DevBytes. It keeps you updated about dev and tech news.
This brilliant app provides curated, bite-sized updates on the latest tech news/dev content. Whether it’s new frameworks, AI breakthroughs, or cloud services, DevBytes brings the essentials straight to you.
If you're tired of information overload and want a smarter way to stay informed, give DevBytes a try.
Download here: https://play.google.com/store/apps/details?id=com.candelalabs.devbytes&hl=en-IN
It’s time to read less and know more!
As you all know, the purpose of this community is to share notes and grow together. Hence, today I am sharing with you an app called DevBytes. It keeps you updated about dev and tech news.
This brilliant app provides curated, bite-sized updates on the latest tech news/dev content. Whether it’s new frameworks, AI breakthroughs, or cloud services, DevBytes brings the essentials straight to you.
If you're tired of information overload and want a smarter way to stay informed, give DevBytes a try.
Download here: https://play.google.com/store/apps/details?id=com.candelalabs.devbytes&hl=en-IN
It’s time to read less and know more!
Google Play
DevBytes-For Busy Developers – Apps on Google Play
Get the latest tech news, coding tips, and programming insights for developers.
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Kaggle Data Hub
Hey guys, As you all know, the purpose of this community is to share notes and grow together. Hence, today I am sharing with you an app called DevBytes. It keeps you updated about dev and tech news. This brilliant app provides curated, bite-sized updates…
I highly recommend downloading the app, there is a solid guide to mastering AI.
👍2
📈How to make $15,000 in a month in 2024?
Easy!!! Lisa is now the hippest trader who is showing crazy results in the market!
She was able to make over $15,000 in the last month! ❗️
Right now she has started a marathon on her channel and is running it absolutely free. 💡
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2. Write in private messages : “Marathon” and start participating!
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Easy!!! Lisa is now the hippest trader who is showing crazy results in the market!
She was able to make over $15,000 in the last month! ❗️
Right now she has started a marathon on her channel and is running it absolutely free. 💡
To participate in the marathon, you will need to :
1. Subscribe to the channel SIGNALS BY LISA TRADER 📈
2. Write in private messages : “Marathon” and start participating!
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👍1
Forwarded from Tomas
📈How to make $15,000 in a month in 2024?
Easy!!! Lisa is now the hippest trader who is showing crazy results in the market!
She was able to make over $15,000 in the last month! ❗️
Right now she has started a marathon on her channel and is running it absolutely free. 💡
To participate in the marathon, you will need to :
1. Subscribe to the channel SIGNALS BY LISA TRADER 📈
2. Write in private messages : “Marathon” and start participating!
👉CLICK HERE👈
Easy!!! Lisa is now the hippest trader who is showing crazy results in the market!
She was able to make over $15,000 in the last month! ❗️
Right now she has started a marathon on her channel and is running it absolutely free. 💡
To participate in the marathon, you will need to :
1. Subscribe to the channel SIGNALS BY LISA TRADER 📈
2. Write in private messages : “Marathon” and start participating!
👉CLICK HERE👈
❤1👍1
Eye-dataset
Eye seeing in that direction
Context:
The image data was gathered for automating an wheelchair. It can controlled by direction given by eye.
Content:
The image dataset consists of four folder and comprising a total of 14.5k images.
The images in the dataset are regularized and augmented so that we can achieve more than 96% accuracy on the first training session.
https://news.1rj.ru/str/datasets1👍
Eye seeing in that direction
Context:
The image data was gathered for automating an wheelchair. It can controlled by direction given by eye.
Content:
The image dataset consists of four folder and comprising a total of 14.5k images.
The images in the dataset are regularized and augmented so that we can achieve more than 96% accuracy on the first training session.
https://news.1rj.ru/str/datasets1
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Handwriting Recognition
Trannoscriptions of 400,000 handwritten names
About Dataset
Overview
This dataset consists of more than four hundred thousand handwritten names collected through charity projects.
Character Recognition utilizes image processing technologies to convert characters on scanned documents into digital forms. It typically performs well in machine-printed fonts. However, it still poses difficult challenges for machines to recognize handwritten characters, because of the huge variation in individual writing styles.
There are 206,799 first names and 207,024 surnames in total. The data was divided into a training set (331,059), testing set (41,382), and validation set (41,382) respectively.
Content
The input data here are hundreds of thousands of images of handwritten names. In the Data, you’ll find the transcribed images broken up into test, training, and validation sets.
Image Lable follow the following naming format enabling you to extend the data set with your own data.
https://news.1rj.ru/str/datasets1⭐️
Trannoscriptions of 400,000 handwritten names
About Dataset
Overview
This dataset consists of more than four hundred thousand handwritten names collected through charity projects.
Character Recognition utilizes image processing technologies to convert characters on scanned documents into digital forms. It typically performs well in machine-printed fonts. However, it still poses difficult challenges for machines to recognize handwritten characters, because of the huge variation in individual writing styles.
There are 206,799 first names and 207,024 surnames in total. The data was divided into a training set (331,059), testing set (41,382), and validation set (41,382) respectively.
Content
The input data here are hundreds of thousands of images of handwritten names. In the Data, you’ll find the transcribed images broken up into test, training, and validation sets.
Image Lable follow the following naming format enabling you to extend the data set with your own data.
https://news.1rj.ru/str/datasets1
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archive.zip
1.3 GB
Handwriting Recognition
Trannoscriptions of 400,000 handwritten names
https://news.1rj.ru/str/datasets1💫
Trannoscriptions of 400,000 handwritten names
https://news.1rj.ru/str/datasets1
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Arabic Handwritten Characters Dataset
Content
The data-set is composed of 16,800 characters written by 60 participants, the age range is between 19 to 40 years, and 90% of participants are right-hand. Each participant wrote each character (from ’alef’ to ’yeh’) ten times on two forms as shown in Fig. 7(a) & 7(b). The forms were scanned at the resolution of 300 dpi. Each block is segmented automatically using Matlab 2016a to determining the coordinates for each block. The database is partitioned into two sets: a training set (13,440 characters to 480 images per class) and a test set (3,360 characters to 120 images per class). Writers of training set and test set are exclusive. Ordering of including writers to test set are randomized to make sure that writers of test set are not from a single institution (to ensure variability of the test set).
In an experimental section we showed that the results were promising with 94.9% classification accuracy rate on testing images. In future work, we plan to work on improving the performance of handwritten Arabic character recognition.
https://news.1rj.ru/str/datasets1💫
Content
The data-set is composed of 16,800 characters written by 60 participants, the age range is between 19 to 40 years, and 90% of participants are right-hand. Each participant wrote each character (from ’alef’ to ’yeh’) ten times on two forms as shown in Fig. 7(a) & 7(b). The forms were scanned at the resolution of 300 dpi. Each block is segmented automatically using Matlab 2016a to determining the coordinates for each block. The database is partitioned into two sets: a training set (13,440 characters to 480 images per class) and a test set (3,360 characters to 120 images per class). Writers of training set and test set are exclusive. Ordering of including writers to test set are randomized to make sure that writers of test set are not from a single institution (to ensure variability of the test set).
In an experimental section we showed that the results were promising with 94.9% classification accuracy rate on testing images. In future work, we plan to work on improving the performance of handwritten Arabic character recognition.
https://news.1rj.ru/str/datasets1
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Skin-Disease-Dataset
skin infections, such as bacteria, viruses, fungi, and parasites.
About Dataset
Some types of pathogens — notably bacteria and fungi — are typically present on the skin, but if they become too numerous, the immune system can no longer manage them.In this case, an infection can result.The cause of a skin infection depends on the pathogen involved.
In this dataset image collected from internet.
this dataset there are total 8 class ,They are
Bacterial Infections- cellulitis
Bacterial Infections- impetigo
Fungal Infections - athlete -foot
Fungal Infections - nail-fungus
Fungal Infections - ringworm
Parasitic Infections - cutaneous-larva-migrans
Viral skin infections - chickenpox
Viral skin infections - shingles
https://news.1rj.ru/str/datasets1💫
skin infections, such as bacteria, viruses, fungi, and parasites.
About Dataset
Some types of pathogens — notably bacteria and fungi — are typically present on the skin, but if they become too numerous, the immune system can no longer manage them.In this case, an infection can result.The cause of a skin infection depends on the pathogen involved.
In this dataset image collected from internet.
this dataset there are total 8 class ,They are
Bacterial Infections- cellulitis
Bacterial Infections- impetigo
Fungal Infections - athlete -foot
Fungal Infections - nail-fungus
Fungal Infections - ringworm
Parasitic Infections - cutaneous-larva-migrans
Viral skin infections - chickenpox
Viral skin infections - shingles
https://news.1rj.ru/str/datasets1
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OASIS Alzheimer's Detection
Large-scale brain MRI dataset for deep neural network analysis
About Dataset
The dataset used is the OASIS MRI dataset (https://sites.wustl.edu/oasisbrains/), which consists of 80,000 brain MRI images. The images have been divided into four classes based on Alzheimer's progression. The dataset aims to provide a valuable resource for analyzing and detecting early signs of Alzheimer's disease.
To make the dataset accessible, the original .img and .hdr files were converted into Nifti format (.nii) using FSL (FMRIB Software Library). The converted MRI images of 461 patients have been uploaded to a GitHub repository, which can be accessed in multiple parts.
For the neural network training, 2D images were used as input. The brain images were sliced along the z-axis into 256 pieces, and slices ranging from 100 to 160 were selected from each patient. This approach resulted in a comprehensive dataset for analysis.
Patient classification was performed based on the provided metadata and Clinical Dementia Rating (CDR) values, resulting in four classes: demented, very mild demented, mild demented, and non-demented. These classes enable the detection and study of different stages of Alzheimer's disease progression.
During the dataset preparation, the .nii MRI scans were converted to .jpg files. Although this conversion presented some challenges, the files were successfully processed using appropriate tools. The resulting dataset size is 1.3 GB.
https://news.1rj.ru/str/datasets1🌟
Large-scale brain MRI dataset for deep neural network analysis
About Dataset
The dataset used is the OASIS MRI dataset (https://sites.wustl.edu/oasisbrains/), which consists of 80,000 brain MRI images. The images have been divided into four classes based on Alzheimer's progression. The dataset aims to provide a valuable resource for analyzing and detecting early signs of Alzheimer's disease.
To make the dataset accessible, the original .img and .hdr files were converted into Nifti format (.nii) using FSL (FMRIB Software Library). The converted MRI images of 461 patients have been uploaded to a GitHub repository, which can be accessed in multiple parts.
For the neural network training, 2D images were used as input. The brain images were sliced along the z-axis into 256 pieces, and slices ranging from 100 to 160 were selected from each patient. This approach resulted in a comprehensive dataset for analysis.
Patient classification was performed based on the provided metadata and Clinical Dementia Rating (CDR) values, resulting in four classes: demented, very mild demented, mild demented, and non-demented. These classes enable the detection and study of different stages of Alzheimer's disease progression.
During the dataset preparation, the .nii MRI scans were converted to .jpg files. Although this conversion presented some challenges, the files were successfully processed using appropriate tools. The resulting dataset size is 1.3 GB.
https://news.1rj.ru/str/datasets1
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Fashion Dataset of Women with Text and Images to build Recommendar System.
Context: The data was created to build a Content Based Recommendation System using Text Data or Image Data which can Recommend similar Items to user.
https://news.1rj.ru/str/datasets1
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Kaggle Data Hub
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Forwarded from Tomas
🤑EARN YOUR $100 TODAY! EASY!
Lisa Trader has launched a free marathon on her VIP channel.
Now absolutely everyone can earn from trading. It has become even easier to earn in the cryptocurrency market, you can start today!
WHAT DO YOU NEED TO START?
1. Subscribe to the channel SIGNALS BY LISA TRADER 📈.
2. Write “MARATHON” in private messages. She will then tell you how to get on the vip channel for absolutely FREE!
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Lisa Trader has launched a free marathon on her VIP channel.
Now absolutely everyone can earn from trading. It has become even easier to earn in the cryptocurrency market, you can start today!
WHAT DO YOU NEED TO START?
1. Subscribe to the channel SIGNALS BY LISA TRADER 📈.
2. Write “MARATHON” in private messages. She will then tell you how to get on the vip channel for absolutely FREE!
👉CLICK HERE👈
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👍3
Traffic 🚦 Signs Detection
Signs Detection For Self-Driving Cars (Computer Vision Project)
About Dataset
Name of Classes: Green Light, Red Light, Speed Limit 10, Speed Limit 100, Speed Limit 110, Speed Limit 120, Speed Limit 20, Speed Limit 30, Speed Limit 40, Speed Limit 50, Speed Limit 60, Speed Limit 70, Speed Limit 80, Speed Limit 90, Stop
Here are a few use cases for this project:
Autonomous Vehicle Navigation: The model can be used in self-driving car systems to recognize traffic signs accurately. This would enable autonomous vehicles to follow traffic rules and regulations, analyzing every sign whether it’s about speed limit or stop-and-go indications to navigate the roads safely.
Traffic Rule Compliance: This model can be used in driver assistance systems to ensure that drivers comply with all traffic rules. Alerts can be generated when drivers exceed the speed limit or don't stop at red lights, fostering safer roads.
Road Safety Training Programs: This model allows Driving schools and automotive companies to build simulations and education programs. These programs can guide new drivers in identifying and responding to different traffic signs, thus enhancing road safety knowledge.
Smart City Infrastructure: City authorities could use this model in connected CCTV or IoT infrastructure to track and monitor traffic compliance in real time, helping identify areas with frequent rule violations for potential improvement.
Road Network Analysis: Transportation engineering researchers can use this model to analyze how efficiently different sign classes are distributed and recognized around the city. This data can be instrumental in planning more efficient and safer road networks.
This dataset is a traffic sign image dataset containing 4969 samples, which dataset (as you can see in the image) is correctly divided into three parts: Train, Valid, and Test!!
Signs Detection For Self-Driving Cars (Computer Vision Project)
About Dataset
Name of Classes: Green Light, Red Light, Speed Limit 10, Speed Limit 100, Speed Limit 110, Speed Limit 120, Speed Limit 20, Speed Limit 30, Speed Limit 40, Speed Limit 50, Speed Limit 60, Speed Limit 70, Speed Limit 80, Speed Limit 90, Stop
Here are a few use cases for this project:
Autonomous Vehicle Navigation: The model can be used in self-driving car systems to recognize traffic signs accurately. This would enable autonomous vehicles to follow traffic rules and regulations, analyzing every sign whether it’s about speed limit or stop-and-go indications to navigate the roads safely.
Traffic Rule Compliance: This model can be used in driver assistance systems to ensure that drivers comply with all traffic rules. Alerts can be generated when drivers exceed the speed limit or don't stop at red lights, fostering safer roads.
Road Safety Training Programs: This model allows Driving schools and automotive companies to build simulations and education programs. These programs can guide new drivers in identifying and responding to different traffic signs, thus enhancing road safety knowledge.
Smart City Infrastructure: City authorities could use this model in connected CCTV or IoT infrastructure to track and monitor traffic compliance in real time, helping identify areas with frequent rule violations for potential improvement.
Road Network Analysis: Transportation engineering researchers can use this model to analyze how efficiently different sign classes are distributed and recognized around the city. This data can be instrumental in planning more efficient and safer road networks.
This dataset is a traffic sign image dataset containing 4969 samples, which dataset (as you can see in the image) is correctly divided into three parts: Train, Valid, and Test!!
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Kaggle Data Hub
archive.zip
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