Column Descriptions:
Patient_ID (int): Unique identifier for each patient.
Age (int): Age of the patient.
Gender (object): Patient’s gender (Male/Female).
Country (object): Country of residence.
Ethnicity (object): Patient’s ethnic background.
Family_History (object): Whether the patient has a family history of thyroid cancer (Yes/No).
Radiation_Exposure (object): History of radiation exposure (Yes/No).
Iodine_Deficiency (object): Presence of iodine deficiency (Yes/No).
Smoking (object): Whether the patient smokes (Yes/No).
Obesity (object): Whether the patient is obese (Yes/No).
Diabetes (object): Whether the patient has diabetes (Yes/No).
TSH_Level (float): Thyroid-Stimulating Hormone level (µIU/mL).
T3_Level (float): Triiodothyronine level (ng/dL).
T4_Level (float): Thyroxine level (µg/dL).
Nodule_Size (float): Size of thyroid nodules (cm).
Thyroid_Cancer_Risk (object): Estimated risk of thyroid cancer (Low/Medium/High).
Diagnosis (object): Final diagnosis (Benign/Malignant).

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Problem Statement
As a data scientist at a leading home electronics company, my goal is to create an innovative gesture control feature for smart televisions. By utilizing a webcam mounted on the TV, the system will recognize five specific gestures, enabling users to interact with the TV hands-free, without needing a remote control.

The five gestures and their corresponding actions are:

Thumbs Up: Increases the volume.
Thumbs Down: Decreases the volume.
Left Swipe: Rewinds the content by 10 seconds.
Right Swipe: Jumps forward by 10 seconds.
Stop: Pauses the content.

Machine learning algorithms will train the system to recognize these gestures in real-time using the webcam, providing seamless interaction and enhancing the overall user experience.

Objectives
The primary objective is to develop a gesture-based control feature for smart TVs, enabling users to adjust volume, skip, rewind, and pause content using five distinct gestures detected by a webcam. Machine learning will be employed to train the model to recognize these gestures instantly, offering a hands-free and intuitive TV experience.

Understanding the Dataset
The training dataset consists of several hundred videos, each categorized into one of five gesture classes. Each video lasts 2-3 seconds, divided into 30 frames (images). Captured by various individuals performing the gestures in front of a webcam, these videos simulate real-world smart TV use. The gestures include thumbs up, thumbs down, left swipe, right swipe, and stop, and serve as individual training samples for the gesture recognition model.

Generator
The generator will preprocess the video data by cropping, resizing, and normalizing it to ensure proper formatting before passing it to the model. The generator should efficiently process batches of video data, ensuring smooth training without errors.

Model
The objective is to create a model that trains efficiently with minimal inference time. The model’s architecture should be optimized to balance performance and speed, with fewer parameters leading to faster predictions. The model will be evaluated based on accuracy in recognizing gestures, starting with a small dataset to assess initial performance before scaling up.

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About Dataset

This dataset contains 499 images, each with bounding box annotations for cars.
The annotations are provided in the YOLO text format, which includes class labels and bounding box coordinates.
This dataset is useful for object detection tasks such as vehicle recognition and traffic analysis.

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Dataset description

OpenR1-Math-220k is a large-scale dataset for mathematical reasoning. It consists of 220k math problems with two to four reasoning traces generated by DeepSeek R1 for problems from NuminaMath 1.5. The traces were verified using Math Verify for most samples and Llama-3.3-70B-Instruct as a judge for 12% of the samples, and each problem contains at least one reasoning trace with a correct answer.

The dataset consists of two splits:

default with 94k problems and that achieves the best performance after SFT.
extended with 131k samples where we add data sources like cn_k12. This provides more reasoning traces, but we found that the performance after SFT to be lower than the default subset, likely because the questions from cn_k12 are less difficult than other sources.

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Dataset Description:
This dataset includes two folders of images of people. One folder contains images of men, and the other contains images of women. The images include faces, upper bodies, and full bodies. This dataset can be used for various projects like gender recognition, human identification, and image classification.

Use Cases:
Gender Recognition: For algorithms that recognize gender based on images.
Human Identification: To improve models for identifying humans in images and videos.
Image Classification: For classifying images into categories of men and women.

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The data collected for this project were from the following sources:

Fabric Defect Dataset from Kaggle (Ranathunga, 2020)
Fabric Stain Dataset from Kaggle (Pathirana, 2020)
Aitex Fabric Image Database (Silvestre-Blanes et al., 2019)
Dataset from the Author of the Literature Review Paper (Peng, 2020): Only the non-defect images from the dataset were used.