This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

NVIDIA: Still a Wise Investment? (NVDA)

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

The Nvidia Stock Price: A Game Theory Analysis

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

GPU-Powered Database Market Outlook

According to our latest research, the global GPU-powered database market size reached USD 1.82 billion in 2024, demonstrating robust adoption across diverse industries. The market is projected to grow at a remarkable CAGR of 25.1% from 2025 to 2033, culminating in a forecasted value of USD 13.3 billion by 2033. This surge is driven by the escalating need for high-speed data processing, real-time analytics, and the proliferation of artificial intelligence (AI) and machine learning (ML) workloads. As organizations increasingly seek to harness the power of data for competitive advantage, the demand for GPU-accelerated database solutions continues to intensify, marking a pivotal shift from traditional CPU-centric architectures.

The primary growth factor fueling the GPU-powered database market is the exponential rise in data volume and complexity, necessitating more efficient data processing solutions. Traditional databases, while reliable, often struggle to meet the performance requirements of modern workloads, particularly those involving real-time analytics and deep learning. GPU-powered databases, leveraging the parallel processing capabilities of graphics processing units, offer significant performance gains by accelerating query execution and data analysis. This ability to process massive datasets at unprecedented speeds is especially valuable for sectors such as finance, healthcare, and retail, where timely insights can translate directly into enhanced decision-making and operational efficiency.

Another key driver is the integration of artificial intelligence and machine learning into mainstream business operations. As organizations increasingly deploy AI/ML models for predictive analytics, fraud detection, personalized recommendations, and other advanced applications, the need for databases capable of supporting these compute-intensive workloads has become paramount. GPU-powered databases are uniquely positioned to address these requirements, enabling faster model training and inference by handling complex mathematical computations more efficiently than traditional CPU-based systems. The synergy between AI/ML advancements and GPU-accelerated databases is expected to remain a cornerstone of market growth over the forecast period.

The expanding adoption of cloud computing further amplifies the growth trajectory of the GPU-powered database market. Cloud service providers are rapidly integrating GPU-based infrastructure into their offerings, making high-performance database solutions accessible to a broader range of enterprises, including small and medium-sized businesses. This democratization of access, coupled with the scalability and flexibility inherent in cloud deployments, is accelerating the transition from on-premises to cloud-based GPU database solutions. The combination of reduced upfront costs, simplified management, and the ability to dynamically scale resources in response to fluctuating workloads is proving highly attractive to organizations aiming to optimize their data infrastructure.

From a regional perspective, North America currently dominates the GPU-powered database market, supported by a robust technology ecosystem, significant investments in AI and analytics, and the presence of major industry players. However, Asia Pacific is rapidly emerging as a high-growth region, driven by digital transformation initiatives, rising investments in cloud infrastructure, and the increasing adoption of data-driven business models across sectors such as e-commerce, telecommunications, and manufacturing. Europe follows closely, benefiting from strong regulatory frameworks around data privacy and security, which are prompting organizations to invest in advanced database solutions that can deliver both performance and compliance.

Component Analysis

The GPU-powered database market is segmented by component into hardware, software, and services, each playing a distinct yet interrelated role in driving market expansion. Hardware constitutes the backbone of GPU-accelerated databases, encompassing high-performance GPUs, servers, and storage systems optimized for parallel processing. The demand for advanced GPU hardware has surged in recent years, propelled by the need for faster data processing and the growing complexity of analytical workloads. Leading hardware manufacturers are continuously innovating to deliver GPUs with higher memory bandwidth, increased core counts, an

GPU-Powered Database Market Outlook

According to our latest research, the global GPU-powered database market size reached USD 1.94 billion in 2024, driven by surging demand for high-performance data analytics and real-time processing across industries. The market is growing at a robust CAGR of 21.7% and is forecasted to reach USD 13.2 billion by 2033. This remarkable growth is primarily fueled by the exponential increase in unstructured data, the rapid adoption of artificial intelligence (AI) and machine learning (ML) workloads, and the need for accelerated query performance in data-intensive applications. As organizations worldwide invest in digital transformation and advanced analytics, GPU-powered databases are emerging as a critical technology for unlocking actionable insights from massive datasets.

One of the most significant growth factors for the GPU-powered database market is the unprecedented surge in data generation across diverse sectors, including finance, healthcare, retail, and manufacturing. Traditional CPU-based databases are increasingly unable to keep pace with the real-time analytics and complex query requirements of modern enterprises. GPUs, with their massive parallel processing capabilities, offer a transformative solution by accelerating data ingestion, query execution, and analytics workloads. As a result, organizations are increasingly turning to GPU-powered databases to drive business intelligence, predictive analytics, and operational efficiency. The proliferation of IoT devices, digital transactions, and multimedia content further amplifies the need for high-throughput, low-latency data platforms, positioning GPU-powered databases as a cornerstone of next-generation data infrastructure.

Another crucial driver is the rapid integration of AI and ML into enterprise workflows, which demands unprecedented levels of computational power and scalability. GPU-powered databases excel in supporting AI-driven applications by handling complex algorithms, deep learning models, and large-scale data processing with remarkable speed and efficiency. Industries such as BFSI and healthcare are leveraging these capabilities to enhance fraud detection, risk assessment, diagnostics, and personalized medicine. Moreover, the convergence of GPU acceleration with cloud computing is democratizing access to high-performance databases, enabling small and medium enterprises to harness advanced analytics without significant upfront investments. This democratization, coupled with ongoing advancements in GPU architectures and database software, is propelling market growth at an accelerated pace.

The evolving data privacy and regulatory landscape is also shaping the GPU-powered database market. As governments and regulatory bodies impose stricter data protection standards, enterprises are prioritizing secure, scalable, and compliant data management solutions. GPU-powered databases, with their ability to efficiently process encrypted and anonymized data, are increasingly favored for mission-critical applications in regulated industries. Additionally, the growing focus on sustainability and energy efficiency is prompting organizations to adopt GPU-accelerated platforms, which typically offer superior performance-per-watt compared to traditional CPU-based systems. These factors collectively underscore the pivotal role of GPU-powered databases in enabling secure, sustainable, and high-performance data ecosystems.

Regionally, North America continues to dominate the GPU-powered database market, accounting for the largest revenue share in 2024, followed by Europe and Asia Pacific. The region's leadership is attributed to early adoption of advanced analytics, robust cloud infrastructure, and a strong presence of technology innovators. However, Asia Pacific is expected to witness the fastest growth through 2033, driven by rapid digitalization, expanding e-commerce, and substantial investments in AI and cloud computing. As global enterprises increasingly recognize the value of real-time data insights, the GPU-powered database market is set to experience widespread adoption and innovation across developed and emerging economies alike.

While a great variety of 3D cameras have been introduced in recent years, most publicly available datasets for object recognition and pose estimation focus on one single camera. This dataset consists of 32 scenes that have been captured by 7 different 3D cameras, totaling 49,294 frames. This allows evaluating the sensitivity of pose estimation algorithms to the specifics of the used camera and the development of more robust algorithms that are more independent of the camera model. Vice versa, our dataset enables researchers to perform a quantitative comparison of the data from several different cameras and depth sensing technologies and evaluate their algorithms before selecting a camera for their specific task. The scenes in our dataset contain 20 different objects from the common benchmark YCB object and model set. We provide full ground truth 6DoF poses for each object, per-pixel segmentation, 2D and 3D bounding boxes and a measure of the amount of occlusion of each object.

If you use this dataset in your research, please cite the following publication:

T. Grenzdörffer, M. Günther, and J. Hertzberg, “YCB-M: A Multi-Camera RGB-D Dataset for Object Recognition and 6DoF Pose Estimation,” in 2020 IEEE International Conference on Robotics and Automation, ICRA 2020, Paris, France, May 31-June 4, 2020. IEEE, 2020.

@InProceedings{Grenzdoerffer2020ycbm,
 title = {{YCB-M}: A Multi-Camera {RGB-D} Dataset for Object Recognition and {6DoF} Pose Estimation},
 author = {Grenzd{\"{o}}rffer, Till and G{\"{u}}nther, Martin and Hertzberg, Joachim},
 booktitle = {2020 {IEEE} International Conference on Robotics and Automation, {ICRA} 2020, Paris, France, May 31-June 4, 2020},
 year = {2020},
 publisher = {{IEEE}}
}

This paper is also available on arXiv: https://arxiv.org/abs/2004.11657

To visualize the dataset, follow these instructions (tested on Ubuntu Xenial 16.04):

# IMPORTANT: the ROS setup.bash must NOT be sourced, otherwise the following error occurs:
# ImportError: /opt/ros/kinetic/lib/python2.7/dist-packages/cv2.so: undefined symbol: PyCObject_Type

# nvdu requires Python 3.5 or 3.6
sudo add-apt-repository -y ppa:deadsnakes/ppa  # to get python3.6 on Ubuntu Xenial
sudo apt-get update
sudo apt-get install -y python3.6 libsm6 libxext6 libxrender1 python-virtualenv python-pip

# create a new virtual environment
virtualenv -p python3.6 venv_nvdu
cd venv_nvdu/
source bin/activate

# clone our fork of NVIDIA's Dataset Utilities that incorporates some essential fixes
pip install -e 'git+https://github.com/mintar/Dataset_Utilities.git#egg=nvdu'

# download and transform the meshes
# (alternatively, unzip the meshes contained in the dataset
# to 

The dataset used in this study experiment was from the preliminary competition dataset of the 2018 Guangdong Industrial Intelligent Manufacturing Big Data Intelligent Algorithm Competition organized by Tianchi Feiyue Cloud (https://tianchi.aliyun.com/competition/entrance/231682/introduction). We have selected the dataset, removing images that do not meet the requirements of our experiment. All datasets have been classified for training and testing. The image pixels are all 2560×1960. Before training, all defects need to be labeled using labelimg and saved as json files. Then, all json files are converted to txt files. Finally, the organized defect dataset is detected and classified.Description of the data and file structureThis is a project based on the YOLOv8 enhanced algorithm for aluminum defect classification and detection tasks.All code has been tested on Windows computers with Anaconda and CUDA-enabled GPUs. The following instructions allow users to run the code in this repository based on a Windows+CUDA GPU system already in use.Files and variablesFile: defeat_dataset.zipDescription:SetupPlease follow the steps below to set up the project:Download Project RepositoryDownload the project repository defeat_dataset.zip from the following location.Unzip and navigate to the project folder; it should contain a subfolder: quexian_datasetDownload data1.Download data .defeat_dataset.zip2.Unzip the downloaded data and move the 'defeat_dataset' folder into the project's main folder.3. Make sure that your defeat_dataset folder now contains a subfolder: quexian_dataset.4. Within the folder you should find various subfolders such as addquexian-13, quexian_dataset, new_dataset-13, etc.softwareSet up the Python environment1.Download and install the Anaconda.2.Once Anaconda is installed, activate the Anaconda Prompt. For Windows, click Start, search for Anaconda Prompt, and open it.3.Create a new conda environment with Python 3.8. You can name it whatever you like; for example. Enter the following command: conda create -n yolov8 python=3.84.Activate the created environment. If the name is , enter: conda activate yolov8Download and install the Visual Studio Code.Install PyTorch based on your system:For Windows/Linux users with a CUDA GPU: bash conda install pytorch==1.10.0 torchvision==0.11.0 torchaudio==0.10.0 cudatoolkit=11.3 -c pytorch -c conda-forgeInstall some necessary libraries:Install scikit-learn with the command: conda install anaconda scikit-learn=0.24.1Install astropy with: conda install astropy=4.2.1Install pandas using: conda install anaconda pandas=1.2.4Install Matplotlib with: conda install conda-forge matplotlib=3.5.3Install scipy by entering: conda install scipy=1.10.1RepeatabilityFor PyTorch, it's a well-known fact:There is no guarantee of fully reproducible results between PyTorch versions, individual commits, or different platforms. In addition, results may not be reproducible between CPU and GPU executions, even if the same seed is used.All results in the Analysis Notebook that involve only model evaluation are fully reproducible. However, when it comes to updating the model on the GPU, the results of model training on different machines vary.Access informationOther publicly accessible locations of the data:https://tianchi.aliyun.com/dataset/public/Data was derived from the following sources:https://tianchi.aliyun.com/dataset/140666Data availability statementThe ten datasets used in this study come from Guangdong Industrial Wisdom Big Data Innovation Competition - Intelligent Algorithm Competition Rematch. and the dataset download link is https://tianchi.aliyun.com/competition/entrance/231682/information?lang=en-us. Officially, there are 4,356 images, including single blemish images, multiple blemish images and no blemish images. The official website provides 4,356 images, including single defect images, multiple defect images and no defect images. We have selected only single defect images and multiple defect images, which are 3,233 images in total. The ten defects are non-conductive, effacement, miss bottom corner, orange, peel, varicolored, jet, lacquer bubble, jump into a pit, divulge the bottom and blotch. Each image contains one or more defects, and the resolution of the defect images are all 2560×1920.By investigating the literature, we found that most of the experiments were done with 10 types of defects, so we chose three more types of defects that are more different from these ten types and more in number, which are suitable for the experiments. The three newly added datasets come from the preliminary dataset of Guangdong Industrial Wisdom Big Data Intelligent Algorithm Competition. The dataset can be downloaded from https://tianchi.aliyun.com/dataset/140666. There are 3,000 images in total, among which 109, 73 and 43 images are for the defects of bruise, camouflage and coating cracking respectively. Finally, the 10 types of defects in the rematch and the 3 types of defects selected in the preliminary round are fused into a new dataset, which is examined in this dataset.In the processing of the dataset, we tried different division ratios, such as 8:2, 7:3, 7:2:1, etc. After testing, we found that the experimental results did not differ much for different division ratios. Therefore, we divide the dataset according to the ratio of 7:2:1, the training set accounts for 70%, the validation set accounts for 20%, and the testing set accounts for 10%. At the same time, the random number seed is set to 0 to ensure that the results obtained are consistent every time the model is trained.Finally, the mean Average Precision (mAP) metric obtained from the experiment was tested on the dataset a total of three times. Each time the results differed very little, but for the accuracy of the experimental results, we took the average value derived from the highest and lowest results. The highest was 71.5% and the lowest was 71.1%, resulting in an average detection accuracy of 71.3% for the final experiment.All data and images utilized in this research are from publicly available sources, and the original creators have given their consent for these materials to be published in open-access formats.The settings for other parameters are as follows. epochs: 200，patience: 50，batch: 16，imgsz: 640，pretrained: true，optimizer: SGD，close_mosaic: 10，iou: 0.7，momentum: 0.937，weight_decay: 0.0005，box: 7.5，cls: 0.5，dfl: 1.5，pose: 12.0，kobj: 1.0，save_dir: runs/trainThe defeat_dataset.(ZIP)is mentioned in the Supporting information section of our manuscript. The underlying data are held at Figshare. DOI: 10.6084/m9.figshare.27922929.The results_images.zipin the system contains the experimental results graphs.The images_1.zipand images_2.zipin the system contain all the images needed to generate the manuscript.tex manuscript.

Accident-Detection-Model

Accident Detection Model is made using YOLOv8, Google Collab, Python, Roboflow, Deep Learning, OpenCV, Machine Learning, Artificial Intelligence. It can detect an accident on any accident by live camera, image or video provided. This model is trained on a dataset of 3200+ images, These images were annotated on roboflow.

Problem Statement

• Road accidents are a major problem in India, with thousands of people losing their lives and many more suffering serious injuries every year.
• According to the Ministry of Road Transport and Highways, India witnessed around 4.5 lakh road accidents in 2019, which resulted in the deaths of more than 1.5 lakh people.
• The age range that is most severely hit by road accidents is 18 to 45 years old, which accounts for almost 67 percent of all accidental deaths.

Accidents survey

https://user-images.githubusercontent.com/78155393/233774342-287492bb-26c1-4acf-bc2c-9462e97a03ca.png" alt="Survey">

Literature Survey

• Sreyan Ghosh in Mar-2019, The goal is to develop a system using deep learning convolutional neural network that has been trained to identify video frames as accident or non-accident.
• Deeksha Gour Sep-2019, uses computer vision technology, neural networks, deep learning, and various approaches and algorithms to detect objects.

Research Gap

• Lack of real-world data - We trained model for more then 3200 images.
• Large interpretability time and space needed - Using google collab to reduce interpretability time and space required.
• Outdated Versions of previous works - We aer using Latest version of Yolo v8.

Proposed methodology

• We are using Yolov8 to train our custom dataset which has been 3200+ images, collected from different platforms.
• This model after training with 25 iterations and is ready to detect an accident with a significant probability.

Model Set-up

Preparing Custom dataset

• We have collected 1200+ images from different sources like YouTube, Google images, Kaggle.com etc.
• Then we annotated all of them individually on a tool called roboflow.
• During Annotation we marked the images with no accident as NULL and we drew a box on the site of accident on the images having an accident
• Then we divided the data set into train, val, test in the ratio of 8:1:1
• At the final step we downloaded the dataset in yolov8 format.
  #### Using Google Collab
• We are using google colaboratory to code this model because google collab uses gpu which is faster than local environments.
• You can use Jupyter notebooks, which let you blend code, text, and visualisations in a single document, to write and run Python code using Google Colab.
• Users can run individual code cells in Jupyter Notebooks and quickly view the results, which is helpful for experimenting and debugging. Additionally, they enable the development of visualisations that make use of well-known frameworks like Matplotlib, Seaborn, and Plotly.
• In Google collab, First of all we Changed runtime from TPU to GPU.
• We cross checked it by running command ‘!nvidia-smi’
  #### Coding
• First of all, We installed Yolov8 by the command ‘!pip install ultralytics==8.0.20’
• Further we checked about Yolov8 by the command ‘from ultralytics import YOLO from IPython.display import display, Image’
• Then we connected and mounted our google drive account by the code ‘from google.colab import drive drive.mount('/content/drive')’
• Then we ran our main command to run the training process ‘%cd /content/drive/MyDrive/Accident Detection model !yolo task=detect mode=train model=yolov8s.pt data= data.yaml epochs=1 imgsz=640 plots=True’
• After the training we ran command to test and validate our model ‘!yolo task=detect mode=val model=runs/detect/train/weights/best.pt data=data.yaml’ ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt conf=0.25 source=data/test/images’
• Further to get result from any video or image we ran this command ‘!yolo task=detect mode=predict model=runs/detect/train/weights/best.pt source="/content/drive/MyDrive/Accident-Detection-model/data/testing1.jpg/mp4"’
• The results are stored in the runs/detect/predict folder.
  Hence our model is trained, validated and tested to be able to detect accidents on any video or image.

Challenges I ran into

I majorly ran into 3 problems while making this model

• I got difficulty while saving the results in a folder, as yolov8 is latest version so it is still underdevelopment. so i then read some blogs, referred to stackoverflow then i got to know that we need to writ an extra command in new v8 that ''save=true'' This made me save my results in a folder.
• I was facing problem on cvat website because i was not sure what

The global Binary Drivers market is poised for substantial growth, driven by the increasing demand for sophisticated software applications across diverse sectors. The market, estimated at $5 billion in 2025, is projected to experience a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $14 billion by 2033. This robust growth is fueled by several key factors. The proliferation of IoT devices and the expansion of cloud computing necessitate efficient and reliable binary drivers for seamless data transfer and device operation. Furthermore, the growing adoption of advanced technologies like artificial intelligence and machine learning is creating a higher demand for specialized binary drivers capable of handling complex data processing tasks. The market is segmented by application (Household and Commercial use) and type (Database Binary Drivers, Executable Binary Drivers, Application Data Binary Drivers, Media Binary Drivers, Configuration Binary Drivers, and Others). While the commercial sector currently dominates, the household segment is anticipated to witness significant growth driven by the increasing penetration of smart home devices. Companies like Intel, Microsoft, and NVIDIA are key players, constantly innovating to enhance driver performance and compatibility. The market's growth trajectory is not without challenges. Security concerns related to vulnerabilities in binary drivers pose a significant restraint. Ensuring robust security measures and regular updates is critical for maintaining market trust and adoption. Furthermore, the complexity of developing and maintaining binary drivers for a wide range of hardware and software platforms presents a technical hurdle for smaller companies. Despite these challenges, the overall market outlook remains optimistic, with continued innovation in driver technology and the expanding demand for connected devices expected to propel growth throughout the forecast period. The geographical distribution of the market reflects the global technological landscape, with North America and Europe holding significant market share initially, while the Asia-Pacific region is anticipated to showcase the fastest growth due to rapid technological advancements and expanding digital infrastructure.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Nvidia (NVDA) Chip Giant's Next Chapter

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

The minimalist histopathology image analysis dataset (MHIST) is a binary classification dataset of 3,152 fixed-size images of colorectal polyps, each with a gold-standard label determined by the majority vote of seven board-certified gastrointestinal pathologists. MHIST also includes each image’s annotator agreement level. As a minimalist dataset, MHIST occupies less than 400 MB of disk space, and a ResNet-18 baseline can be trained to convergence on MHIST in just 6 minutes using approximately 3.5 GB of memory on a NVIDIA RTX 3090. As example use cases, the authors use MHIST to study natural questions that arise in histopathology image classification such as how dataset size, network depth, transfer learning, and high-disagreement examples affect model performance.