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Introduction to Machine learning

• by Refresh Science
• July 17, 2020 January 22, 2023

Machine learning is an application of artificial intelligence that involves algorithms and data that automatically analyse and make decision by itself without human intervention. It describes how computer perform tasks on their own by previous experiences. Therefore we can say in machine language artificial intelligence is generated on the basis of experience.

The difference between normal computer software and machine learning is that a human developer hasn’t given codes that instructs the system how to react to situation, instead it is being trained by a large number of data.

Uses of Machine Learning

Some of the machine learning algorithms are:

• Neural Networks
• Random Forests
• Decision trees
• Genetic algorithm
• Radial basis function

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Types of Machine Learning

There are three types of machine learning

Supervised learning

• Unsupervised learning
• Reinforcement learning

Supervised learning is a technique where the program is given labelled input data and the expected output data. It gets the data from training data containing sets of examples. They generate two kinds of results :

Classification: They notify the class of the data it is presented with.

Regression: they expect the product to produce a numerical value.

UNSUPERVISED LEARNING

This type of algorithm consists of input data without labelled response. There will not be any pre existing labels and human intervention is also less. It is mostly used in exploratory analysis as it can automatically identify the structure in data.

REINFORCEMENT LEARNING

This model is used in making a sequence of decisions. It is an learning by interacting with the environment. It is based on the observation that intelligent agents tend to repeat the action that are rewarded for and refrain from action that are punished for. It can be said that it is an trail and error method in finding the best outcome based on experience.

Machine Learning Uses:

• Traffic prediction
• Virtual Personal Assistant
• Speech recognition
• Email spam and malware filtering
• Bioinformatics
• Natural language processing

Real Time Examples for Machine Learning

Traffic prediction:.

By using GPS navigation service out location are saved at the central server for managing traffic. Based on the number of gps tracked at the location traffic at the particular Street is identified.

Virtual Personal Assistant: 

Smart Speakers, Smartphones and apps like google allo.

Online Transportation:

In apps like uber the available vehicle near our area, the estimated cost and distance of the travel are computed using this technique.

Social Media Services:

In app like Facebook personalizing our news feed, people you may know are done using Machine learning.

Email spam filtering:

There are number of approaches clients use. These filters are continuously updated and powered by machine learning.

Product Recommendation:

In online shopping while we search for a product all its relavant products are displayed in our screen . It is based on the technique of machine learning.

Online Fraud detection:

Tracking monetary frauds online by making cyber space a secure place is an example of machine learning.

Best Programming Languages for Machine Learning:

Some of the best and most commonly used machine learning programs are

• JavaScript,

Machine Learning vs Artificial Intelligence

Difference Between Machine Learning And Artificial Intelligence

Artificial Intelligence is a concept of creating intelligent machines that stimulates human behaviour whereas Machine learning is a subset of Artificial intelligence that allows machine to learn from data without being programmed.

Advantages of ML

• Fast, Accurate, Efficient.
• Automation of most applications.
• Wide range of real life applications.
• Enhanced cyber security and spam detection.
• No human Intervention is needed.
• Handling multi dimensional data.

Disadvantages of ML

• It is very difficult to identify and rectify the errors.
• Data Acquisition.
• Interpretation of results Requires more time and space.

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How to Write a Machine Learning Project Report

Introduction, methodology, future work, acknowledgements.

If you’re working on a machine learning project, then you’ll need to write a project report at some point. This guide will show you how to write a great report that will help you communicate your findings to others.

Checkout this video:

A machine learning project report is a document that describes the process and results of your machine learning project. It should include a description of your data, your models, your results, and your conclusions. The report should be clear and concise, and it should be written in a professional tone.

Here are some tips for writing a machine learning project report:

– Start with an introduction that describes what you did and why you did it. – Be sure to include a description of your data, your models, and your results. – Conclude with a discussion of your findings and their implications. – Be sure to proofread your report carefully before submitting it.

Every machine learning project seeks to answer a question. In this post, we will take a look at what a machine learning project report should contain in order to answer the question effectively. We will also provide a template for creating a machine learning project report.

A machine learning project report should contain the following sections:

-Title -Outline -Introduction -Related Work -Methods -Experimental Setup -Results and Discussion -Conclusion and Future Work -Acknowledgements

When it comes to writing a machine learning project report, data is king. After all, a machine learning project is all about the data. Without data, there is no machine learning. Consequently, the first step in writing a machine learning project report is to gather all of the data that will be used in the project. This data can come from a variety of sources, such as online databases, surveys, and experiments. Once all of the data has been gathered, it should be organized into a format that can be easily read and understood by the reader.

The next step in writing a machine learning project report is to choose a model. This model will be used to make predictions based on the data that was gathered in the first step. There are many different types of models that can be used for machine learning, so it is important to choose one that is well suited for the task at hand. After the model has been chosen, it should be trained on the data that was gathered in the first step. This training process will teach the model how to make predictions based on the data.

Finally, once the model has been trained, it can be used to make predictions on new data. This new data can be used to evaluate how accurate the predictions made by the model are. Additionally, this new data can also be used to improve the model by increasing its accuracy.

Your methodology section should concisely describe the steps you took in your project and why you took them. It should answer the following questions: -What data did you use? -How did you preprocess it (if at all)? -What models did you try? -What hyperparameter settings did you use? -How did you evaluate your model(s)? This section should be no more than a few paragraphs long. For more complex projects, consider adding a subsection for each major step in your project.

If you’ve completed a machine learning project, congratulations! You’ve accomplished a lot. But your work isn’t quite done yet. In order to communicate your findings to others (and to yourself), you need to write a project report.

A project report is different from a research paper. In a research paper, you focus on presenting the results of your research. In a project report, you focus on describing what you did and what you learned from doing it. Therefore, a project report should:

– Be clear and concise – Be organized around the steps you took in your project – Describe what you did at each step – Describe what you learned at each step – Include code snippets and figures – Include any important resources that you used

A well-written project report will not only help you communicate your findings, but also help you reflect on your own learning process. It can be helpful to think of a project report as a journal article about your own work. And like any journal article, it should have an introduction, body, and conclusion.

The discussion section is where you talk about what you did in your project, how you did it, and what the results mean. This is also where you interpret your results and draw conclusions. The discussion section should be well-organized and clearly presented. It should flow smoothly from one point to the next, and each point should be supported with evidence from your data. Be sure to describe any limitations of your study and discuss the implications of your findings.

After all of your hard work, it’s time to conclude your machine learning project report. In this section, you will want to briefly summarize your findings and discuss the implications of your work. You should also include a discussion of any limitations in your study and suggest areas for future research. Finally, be sure to thank your reader for their time and interest in your work.

There is always room for improvement, and no machine learning project is ever truly finished. In your report, you should discuss any possible areas of future work that could be done to improve the performance of your model. For example, you might want to try a different machine learning algorithm, or fine-tune the parameters of the algorithm that you used. You might also want to collect more data, or pre-process the data in a different way.

Whatever direction you choose to take your project in the future, make sure to document your plans in your report so that others can pick up where you left off!

I would like to express my gratitude to my supervisor, Dr XXX, for her continuous support and encouragement during my research. I would also like to thank the other members of my supervisory panel, Professors XXX and YYY, for their valuable comments and suggestions. I am grateful to my family and friends who have supported me throughout my studies. Finally, I would like to thank the funding agency, XXX, for their financial support.

This guide will explain how to write a machine learning project report.

First, you need to understand what a machine learning project report is. A machine learning project report is a document that explains what machine learning is, how it works, and why it is important.

Next, you need to understand the structure of a machine learning project report. A machine learning project report should have the following sections:

– Background – Objectives – Methods – Results – Discussion – Conclusion – References

Finally, you need to understand how to write each section of a machine learning project report. Background: The background section should explain what machine learning is and why it is important. Objectives: The objectives section should explain the goals of the project. Methods: The methods section should describe the approach taken in the project. Results: The results section should present the results of the project. Discussion: The discussion section should discuss the implications of the results. Conclusion: The conclusion section should summarize the findings of the project. References: The references section should list all of the sources used in the project.

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Machine Learning Infographics

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Machine learning is the future of science! It allows computers to identify trends, patterns, manage data… and all that while improving themselves on their own! Since it is so profitable, machine learning and data science studies are becoming more and more common on universities and employers are always hiring. Explain how computers went from “Hello world!” to being almost independent with these infographics: they are full of editable resources so that you can explain neural networks, algorithms, the differences between supervised, unsupervised and reinforcement learning… Science won’t be complicated if you use visual elements like these to explain it!

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Poster Presentations

Most computer science conferences include a poster session where presenters stand beside large-format posters that summarize research results. Conference attendees then have the opportunity to sip tasty beverages while circulating among the posters and talking to the presenters about their work.

The goal of our poster session is to give you a chance to research some area of machine learning that interests you, and to present what you have learned. I'll be flexible about the topics of the posters. Possibilities include:

• A machine learning application area.
• A machine learning algorithm that we did not get a chance to cover in detail.
• A machine learning project that you are proposing - possibly with the first steps completed.

Your topic should be narrow enough for you to present it in some depth. Your poster should be visually appealing, include appropriate figures, and be technically accurate. Your poster should include properly formatted references to peer-reviewed papers.

You will get a deeper understanding of your topic if you dive in and actually write some code or try out some existing tools. A small component of your grade will be based on presenting original results. These results need not be groundbreaking or publishable. You should clearly establish an experimental question and show results that address that question.

• Deadline 1: Topic proposal + preliminary bibliography.
• Deadline 2: Annotated bibliography.
• Deadline 3: Electronic poster submission.
• Deadline 4: Poster presentation.

Topic Proposal

For this deadline you should submit a short (1-2 paragraph) description of your topic, along with at least three representative references. I suggest that you follow the The AAAI Press Reference Style in formatting your references.

Annotated Bibliography

Your annotated bibliography must include at least six peer-reviewed conference or journal papers. At least three of these papers must contain a reference to some other paper in your bibliography. At least three of your papers must have been published within the last four years. For each paper, you must provide complete bibliographic information as well as a brief summary of the paper. The summary should describe the key results, note any references to other papers in your bibliography, and explain the connection to those referenced papers.

Exact formatting requirements TBD.

Finding Papers

Here are some possible starting points for finding high quality papers.

Google Scholar is probably your best starting point. A good way to get started is do some keyword searches related to your topic and take a look at the most highly cited papers that appear relevant. There are many low-quality or uninteresting papers out there. Citation counts provide a good mechanism for focusing attention on noteworthy papers. Once you find an interesting paper you can follow forward and backward citations to get a deeper understanding of the topic.

Conference publications are the main avenue for disseminating research results in computer science. I've highlighted a few of the top conferences in several AI areas below. Except where noted, the proceedings for these conferences should be available on-line.

First-tier Conferences:

• International Conference on Machine Learning (ICML)
• Neural Information Processing Systems (NeurIPS)
• Computer Vision and Pattern Recognition (CVPR)
• Association for the Advancement of Artificial Intelligence (AAAI)
• International Joint Conference on Artificial Intelligence (IJCAI)
• Innovative Applications of Artificial Intelligence (IAAI)

First-tier Journals

• Journal of Machine Learning Research

A Note on arXiv.org

The arXiv.org web site provides a popular avenue for quickly disseminating research results that may not have undergone a formal process of peer review. Sometimes arXiv papers are under review for a journal or conference, sometimes they are pre-publication versions of papers that have since appeared in a peer-reviewed publication, sometimes they are more like informal white papers that are not intended for formal peer review.

The arXiv is great, but for the purposes of this project you should only use arXiv papers if they have actually appeared in a peer reviewed publication, or if they are clearly seminal papers (hundreds of citations). Full publication information will often appear in the "comments" section of a paper's arXiv page. The reference information you provide must include the full publication information.

Reading Research Papers

Reading a research paper is not like reading a novel or even a textbook. Research papers are usually written under the assumption that the audience will be other researchers in the same field. In addition, papers are often written under strict page limits that restrict amount of background information the authors can provide. The keys to making sense of research papers are patience and perseverance. I suggest the following steps.

Start by reading the abstract, the introduction and the conclusion. At this point the goal is to figure out the big-picture claims that the authors are making. What have they accomplished? Why does it matter? At this stage you may determine that the paper is not worth reading. If so, move on to a different paper.

• Next, read the whole paper all the way through. Twice. Read carefully, but don't get bogged down by equations or details. Get a sense for the overall organization of the paper and the main points that are being made in each section. Underline or highlight points that seem particularly important or difficult to understand.
• Finally, go back and try to puzzle out the parts of the paper that were unclear on the first few read-throughs. You will probably find that there are terms or concepts that are important, but are not explained in the paper. The Internet/Wikipedia may be helpful. You may also find that the authors are assuming that you are familiar with some other paper or body of work. You obviously won't be able to read every cited paper (and all of the papers cited in those papers), but some extra reading may be unavoidable.

By the time you finish, you should understand the key points that are being made in the paper. You may not understand every sentence and every equation, but you should know what you don't know, and be in a position to discuss it.

The grade for this project will be calculated as follows:

Your poster presentation will be evaluated by me, as well as by other members of the class, and possibly other members of the department. You will be evaluated both on the poster itself, and on your ability to present the contents.

Acknowledgments

This project is based on a similar project developed by George Ferguson at the University of Rochester.

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Computer Vision Projects

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• 100 Days of Machine Learning

100+ Machine Learning Projects with Source Code [2024]

Classification projects.

• Wine Quality Prediction - Machine Learning
• ML | Credit Card Fraud Detection
• Disease Prediction Using Machine Learning
• Recommendation System in Python
• Detecting Spam Emails Using Tensorflow in Python
• SMS Spam Detection using TensorFlow in Python
• Python | Classify Handwritten Digits with Tensorflow
• Recognizing HandWritten Digits in Scikit Learn
• Identifying handwritten digits using Logistic Regression in PyTorch
• Python | Customer Churn Analysis Prediction
• Online Payment Fraud Detection using Machine Learning in Python
• Flipkart Reviews Sentiment Analysis using Python
• Loan Approval Prediction using Machine Learning
• Loan Eligibility prediction using Machine Learning Models in Python
• Stock Price Prediction using Machine Learning in Python
• Bitcoin Price Prediction using Machine Learning in Python
• Handwritten Digit Recognition using Neural Network
• Parkinson Disease Prediction using Machine Learning - Python
• Spaceship Titanic Project using Machine Learning - Python
• Rainfall Prediction using Machine Learning - Python
• Autism Prediction using Machine Learning
• Predicting Stock Price Direction using Support Vector Machines
• Fake News Detection Model using TensorFlow in Python
• CIFAR-10 Image Classification in TensorFlow
• Black and white image colorization with OpenCV and Deep Learning
• ML | Kaggle Breast Cancer Wisconsin Diagnosis using Logistic Regression
• ML | Cancer cell classification using Scikit-learn
• ML | Kaggle Breast Cancer Wisconsin Diagnosis using KNN and Cross Validation
• Human Scream Detection and Analysis for Controlling Crime Rate - Project Idea
• Multiclass image classification using Transfer learning
• Intrusion Detection System Using Machine Learning Algorithms
• Heart Disease Prediction using ANN

Regression Projects

• IPL Score Prediction using Deep Learning
• Dogecoin Price Prediction with Machine Learning
• Zillow Home Value (Zestimate) Prediction in ML
• Calories Burnt Prediction using Machine Learning
• Vehicle Count Prediction From Sensor Data
• Analyzing selling price of used cars using Python
• Box Office Revenue Prediction Using Linear Regression in ML
• House Price Prediction using Machine Learning in Python
• ML | Boston Housing Kaggle Challenge with Linear Regression
• Stock Price Prediction Project using TensorFlow
• Medical Insurance Price Prediction using Machine Learning - Python
• Inventory Demand Forecasting using Machine Learning - Python
• Ola Bike Ride Request Forecast using ML
• Waiter's Tip Prediction using Machine Learning
• Predict Fuel Efficiency Using Tensorflow in Python
• Microsoft Stock Price Prediction with Machine Learning
• Share Price Forecasting Using Facebook Prophet
• Python | Implementation of Movie Recommender System
• How can Tensorflow be used with abalone dataset to build a sequential model?
• OCR of Handwritten digits | OpenCV
• Cartooning an Image using OpenCV - Python
• Count number of Object using Python-OpenCV
• Count number of Faces using Python - OpenCV
• Text Detection and Extraction using OpenCV and OCR
• FaceMask Detection using TensorFlow in Python
• Dog Breed Classification using Transfer Learning
• Flower Recognition Using Convolutional Neural Network
• Emojify using Face Recognition with Machine Learning
• Cat & Dog Classification using Convolutional Neural Network in Python
• Traffic Signs Recognition using CNN and Keras in Python
• Lung Cancer Detection using Convolutional Neural Network (CNN)
• Lung Cancer Detection Using Transfer Learning
• Pneumonia Detection using Deep Learning
• Detecting Covid-19 with Chest X-ray
• Skin Cancer Detection using TensorFlow
• Age Detection using Deep Learning in OpenCV
• Face and Hand Landmarks Detection using Python - Mediapipe, OpenCV
• Detecting COVID-19 From Chest X-Ray Images using CNN
• Image Segmentation Using TensorFlow
• License Plate Recognition with OpenCV and Tesseract OCR
• Detect and Recognize Car License Plate from a video in real time
• Residual Networks (ResNet) - Deep Learning

Natural Language Processing Projects

• Twitter Sentiment Analysis using Python
• Facebook Sentiment Analysis using python
• Next Sentence Prediction using BERT
• Hate Speech Detection using Deep Learning
• Image Caption Generator using Deep Learning on Flickr8K dataset
• Movie recommendation based on emotion in Python
• Speech Recognition in Python using Google Speech API
• Voice Assistant using python
• Human Activity Recognition - Using Deep Learning Model
• Fine-tuning BERT model for Sentiment Analysis
• Sentiment Classification Using BERT
• Sentiment Analysis with an Recurrent Neural Networks (RNN)
• Autocorrector Feature Using NLP In Python
• Python | NLP analysis of Restaurant reviews
• Restaurant Review Analysis Using NLP and SQLite

Clustering Projects

• Customer Segmentation using Unsupervised Machine Learning in Python
• Music Recommendation System Using Machine Learning
• K means Clustering - Introduction
• Image Segmentation using K Means Clustering

Recommender System Project

• AI Driven Snake Game using Deep Q Learning

Machine Learning gained a lot of popularity and become a necessary tool for research purposes as well as for Business. It is a revolutionary field that helps us to make better decisions and automate tasks. Machine Learning is the field of study that gives computers the capability to learn without being explicitly programmed.

In this article, you’ll find the top 100+ latest Machine Learning projects and Ideas which are beneficial for both beginners and as well experienced professionals. Whether you’re a final-year student aiming for a standout resume or someone building a career, these machine learning projects provide hands-on experience, launching you into the exciting world of Machine Learning and Data Science .

Top Machine Learning Project with Source Code [2024]

We mainly include projects that solve real-world problems to demonstrate how machine learning solves these real-world problems like: – Online Payment Fraud Detection using Machine Learning in Python, Rainfall Prediction using Machine Learning in Python, and Facemask Detection using TensorFlow in Python.

• Machine Learning Project for Beginners
• Advanced Machine Learning Projects

These projects provide a great opportunity for developers to apply their knowledge of machine learning and make an application that benefits society. By implementing these projects in data science, you be familiar with a practical working environment where you follow instructions in real time.

Machine Learning Project for Beginners in 2024 [Source Code]

Let’s look at some of the best new machine-learning projects for beginners in this section and each project deals with a different set of issues, including supervised and unsupervised learning, classification, regression, and clustering. Beginners will be better prepared to tackle more challenging tasks by the time they have finished reading this article and have a better understanding of the fundamentals of machine learning.

1. Healthcare

• ML | Heart Disease Prediction Using Logistic Regression
• Prediction of Wine type using Deep Learning
• Parkinson’s Disease Prediction using Machine Learning in Python
• ML | Kaggle Breast Cancer Wisconsin Diagnosis using KNN and Cross-Validation

2. Finance and Economics

• Credit Card Fraud Detection

3. Food and Beverage

• Wine Quality Prediction

4. Retail and Commerce

• Sales Forecast Prediction – Python
• IPL Score Prediction Using Deep Learning

6. Health and Fitness

• Medical Insurance Price Prediction using Machine Learning in Python

7. Transportation and Traffic

8. environmental science.

• Rainfall Prediction using Machine Learning in Python

9. Text and Image Processing

• Cartooning an Image using OpenCV – Python
• Count number of Faces using Python – OpenCV

10. Social Media and Sentiment Analysis

11. other important machine learning projects.

• Human Scream Detection and Analysis for Controlling Crime Rate
• Spaceship Titanic Project using Machine Learning in Python
• Inventory Demand Forecasting using Machine Learning in Python
• Waiter’s Tip Prediction using Machine Learning
• Fake News Detection using Machine Learning

Advanced Machine Learning Projects With Source Code [2024]

We have discussed a variety of complex machine-learning ideas in this section that are intended to be challenging for users and span a wide range of topics. These subjects involve creating deep learning models, dealing with unstructured data, and instructing sophisticated models like convolutional neural networks, gated recurrent units, large language models, and reinforcement learning models.

1. Image and Video Processing

• Cat & Dog Classification using Convolutional Neural Network in Python
• Residual Networks (ResNet) – Deep Learning

2. Recommendation Systems

• Ted Talks Recommendation System with Machine Learning

3. Speech and Language Processing

• Fine-tuning the BERT model for Sentiment Analysis
• Sentiment Analysis with Recurrent Neural Networks (RNN)
• Autocorrect Feature Using NLP In Python

4. Health and Medical Applications

5. security and surveillance.

• Detect and Recognize Car License Plate from a video in real-time

6. Gaming and Entertainment

• AI-Driven Snake Game using Deep Q Learning

7. Other Advanced Machine Learning Projects

• Face and Hand Landmarks Detection using Python
• Human Activity Recognition – Using Deep Learning Model
• How can Tensorflow be used with the abalone dataset to build a sequential model?

Machine Learning Projects – FAQs

What are some good machine-learning projects.

For beginners, recommended machine learning projects include sentiment analysis, sales forecast prediction, and image recognition.  

How do I start an ML project?

To start a machine learning project, the first steps involve collecting data, preprocessing it, constructing data models, and then training those models with that data.

Which Language is used for machine learning?

Python and R are most popular and widely-used programming languages for machine learning.

Why do we need to build machine learning projects?

We need to build machine learning projects to solve complex problems, automate tasks and improve decision-making.

What is the future of machine learning?

Machine learning is a fast-growing field of study and research, which means that the demand for machine learning professionals is also growing. 

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Pharmacy Times: How AI, machine learning can benefit pharmaceutical development, research

Pharmacy Times highlighted a presentation from the University of Cincinnati's Shawn Xiong discussing the potential for artificial intelligence (AI) and machine learning to bolster pharmaceutical development and research.

Xiong, PhD, assistant professor at UC's James L. Winkle College of Pharmacy, presented the American Pharmacists Association (APhA)-Academy of Pharmaceutical Research and Science Keynote at the 2024 APhA Meeting and Exposition March 22.

In the pharmaceutical industry, Xiong said the top three use cases for AI are predictive maintenance, quality inspection and assurance and manufacturing process optimization. In clinical practice, Xiong said early detection and personalized treatment are particularly exciting areas of potential.

“This is not only about identifying the disease itself,” Xiong said. “It’s also about providing insights into the unique situations of the patient so that we can make personalized treatments for the patient, especially considering the concept of patient-centered care.”

While the potential is encouraging, Xiong noted there are still limitations and concerns, including nuances of medical language and privacy and security concerns with patient data. As these issues are addressed, however, Xiong said AI and machine learning could significantly improve the future of pharmacy.

“AI and machine learning can help automate the medication filling process, improving accuracy and also saving time,” Xiong said. “The AI can help check and double-check errors in the medication orders, and they can help us identify errors and reduce errors so that patients are getting what they need and how they need it.”

Read the Pharmacy Times article.

Featured image courtesy of Adobe Stock.

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• 🐝 Partnership and Promotion

Conclusion 🌟

The collaboration between MLOps and DevOps is essential for achieving excellence in managing vector databases for ML projects. By combining the strengths of both disciplines, MLOps’ focus on automating the ML lifecycle, and DevOps’ expertise in software development and operations, teams can ensure that their ML models are developed, deployed efficiently, and maintained effectively in production environments. This synergy facilitates the creating of robust, scalable, and high-performing ML applications that can drive significant value for businesses and users.

Adnan Hassan

Hello, My name is Adnan Hassan. I am a consulting intern at Marktechpost and soon to be a management trainee at American Express. I am currently pursuing a dual degree at the Indian Institute of Technology, Kharagpur. I am passionate about technology and want to create new products that make a difference.

• Adnan Hassan https://www.marktechpost.com/author/adnanhassan_01/ Exploration of How Large Language Models Navigate Decision Making with Strategic Prompt Engineering and Summarization
• Adnan Hassan https://www.marktechpost.com/author/adnanhassan_01/ LLM2LLM: UC Berkeley, ICSI and LBNL Researchers’ Innovative Approach to Boosting Large Language Model Performance in Low-Data Regimes with Synthetic Data
• Adnan Hassan https://www.marktechpost.com/author/adnanhassan_01/ Researchers from Imperial College and GSK AI Introduce RAmBLA: A Machine Learning Framework for Evaluating the Reliability of LLMs as Assistants in the Biomedical Domain
• Adnan Hassan https://www.marktechpost.com/author/adnanhassan_01/ How do ChatGPT, Gemini, and other LLMs Work?

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Computer Science > Machine Learning

Title: generating potent poisons and backdoors from scratch with guided diffusion.

Abstract: Modern neural networks are often trained on massive datasets that are web scraped with minimal human inspection. As a result of this insecure curation pipeline, an adversary can poison or backdoor the resulting model by uploading malicious data to the internet and waiting for a victim to scrape and train on it. Existing approaches for creating poisons and backdoors start with randomly sampled clean data, called base samples, and then modify those samples to craft poisons. However, some base samples may be significantly more amenable to poisoning than others. As a result, we may be able to craft more potent poisons by carefully choosing the base samples. In this work, we use guided diffusion to synthesize base samples from scratch that lead to significantly more potent poisons and backdoors than previous state-of-the-art attacks. Our Guided Diffusion Poisoning (GDP) base samples can be combined with any downstream poisoning or backdoor attack to boost its effectiveness. Our implementation code is publicly available at: this https URL .

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