Any help for creating a Dataset for fine-tuning ML models?

Dear Professor/Researcher,

Book Chapter proposal is invited for the edited book titled “Quantum Machine Learning (QML): Platform, Tools & Applications”.

The main goal of this book is to deliberate upon the various aspects of Quantum Machine Learning in distributed systems, cryptography and security by a galaxy of intellectuals from academia, researcher, professional community and industry. While this book would dwell on the foundations of Quantum Machine Learning as a part of transparency, scalability, integrity, security, it will also focus on contemporary topics for Research and Development on QML.

Topics for which Chapter proposals are invited:

Topic 4. Quantum Error Mitigation(QEM)

4.1 Introduction to quantum errors and noise

4.2 Quantum error mitigation techniques

4.3 Integrating QEM to the QML framework

Topic 5. Quantum Error Correction(QEC)

5.1. Introduction to quantum error correction

5.2 Quantum error correction techniques

5.3 Fault-tolerant quantum computing

Publisher:

ELSEVIER

Series: Advances in Computers Serial

Volume 140

Editors

Prof Shiho Kim[Chief Editor]

School of Integrated Technology, Yonsei University, South Korea

Ganesh Chandra Deka

Directorate General of Training, Ministry of Skill Development and Entrepreneurship, INDIA

With warm regards,

Shiho Kim

https://scholar.google.com/citations?hl=en&user=X3gOnQ0AAAAJ&view_op=list_works&sortby=pubdate

GC Deka

https://scholar.google.co.in/citations?user=Qw5HblgAAAAJ&hl=en

Usha manjari sikharam

Creating a high-quality dataset for fine-tuning machine learning models is a crucial step in building robust and accurate models. The process of creating a dataset involves data collection, preprocessing, labeling, and validation. Here's a step-by-step guide to help you create a dataset for fine-tuning ML models:

Define Your Task:Clearly define the machine learning task you want to address. Determine the type of data you need, such as text, images, audio, or tabular data.

Data Collection:Depending on your task, collect data from relevant sources. This could involve web scraping, data acquisition from APIs, manual data entry, or data generation.

Data Preprocessing:Clean and preprocess the collected data to ensure it's in a usable format. This may include:Data cleaning (handling missing values, outliers, and noise). Data normalization or scaling. Text preprocessing (tokenization, stemming, stop word removal). Image resizing or cropping. Audio feature extraction.

Data Labeling:For supervised learning tasks, you need labeled data where each data point is associated with a ground truth label. Labeling can be a time-consuming process, and you may consider these options:Manual labeling: Have human annotators label the data. Semi-supervised or active learning: Start with a small labeled dataset and iteratively label more data based on model uncertainty. Crowdsourcing: Use platforms like Amazon Mechanical Turk to label data.

Data Splitting:Split your dataset into training, validation, and test sets. Typically, you'll use a larger portion for training and smaller portions for validation and testing. The exact split depends on the size of your dataset.

Data Augmentation (Optional):In computer vision tasks, you can apply data augmentation techniques to increase the diversity of your training data. This can involve random rotations, flips, brightness adjustments, and more.

Data Balancing (Optional):If your dataset is imbalanced (one class has significantly more samples than others), consider techniques like oversampling, undersampling, or generating synthetic data to balance the classes.

Data Validation:Carefully validate the quality and correctness of your dataset. Check for labeling errors, data distribution, and consistency.

Data Storage and Versioning:Organize and store your dataset in a structured manner, and consider using version control systems to keep track of changes.

Documentation:Create documentation for your dataset, including a data dictionary, metadata, and information about the data collection process. This helps other researchers understand and use your dataset.

Legal and Ethical Considerations:Ensure that you have the necessary permissions to use the data, especially if it contains sensitive or personal information. Address privacy and ethical concerns.

Data Sharing (Optional):Consider sharing your dataset with the research community, which can lead to valuable insights and collaborations. Be mindful of data sharing policies and licensing.

Continuous Maintenance:Keep your dataset up-to-date and maintain it as needed. Over time, you may need to re-label data or add new samples to adapt to changing conditions.

Creating a high-quality dataset is a foundational step in machine learning, and it often requires substantial effort. Properly curated datasets are essential for training and fine-tuning models effectively.

Shafagat Mahmudova

Dear Yasir Nooruldeen Ibrahim ,

To create an instruction dataset for fine-tuning an LLM, start by cleaning and formatting domain-specific text, then use a pre-trained LLM to fine-tune it on the text dataset, and finally generate synthetic instruction-based fine-tuning data sets for the desired domain.

https://eightify.app/summary/artificial-intelligence-and-machine-learning/creating-an-instruction-dataset-for-fine-tuning-llm-tools-and-techniques#:~:text=and%20answer%20datasets.-,To%20create%20an%20instruction%20dataset%20for%20fine-tuning%20an%20LLM,sets%20for%20the%20desired%20domain.

Regards,

Shafagat

Why Do TDS and EC Increase with Larger Wastewater Volumes, While BOD and COD Decrease?

How to enrich pig excreta for increasing nutrient quality organically ?

Is it possible to plot the atom-projected band structure using GPAW?

Unusual intensity drop in some sections of chromatograms in DDA?

Leaf area of tomato ?

Why did the authors extrapolate a phenotype that they experimentally proved in one bacterial strain across the whole genus of the organism?

How to preform densitometry on SDS-page bands?

XRD Analysis is showing only Calcium carbonate. It is not showing other compounds. Can anyone help me get the other compounds?

Which solvent is better to dissolve with secondary metabolites extracted from fungi?

What are the limitations and challenges of using machine learning for predicting concrete compressive strength in practical applications?

The Bigger You Are, the Harder You Fall (some lessons from Dinosaurs)?

Are air moisture harvesting technologies effective in combating desertification?

I need the datasets of Microgrid for system identification?

State of art in natural disasters?

Broca’s area must be intact for the learning of new movement sequences?

How can I get my Granzyme B flow cytometry stain to be consistent?

The Origin of Human Language?

Posthoc test lettering in JAMOVI?

Creating an Automaton/Using Language as the Model?

What are the roles of innovation in achieving the Sustainable Development Goals (SDG)?