Javed Alam’s Post

Are LLMs really good at Math? A new paper reveals that LLMs have strong performance on individual math problems but struggle with chained problems where the answer to one informs the next. This reasoning gap is larger in smaller, specialized models. 👀 The reasoning gap is the difference between an LLM's expected performance (based on individual question accuracy) and its actual performance on chained reasoning tasks. Tested Models include Google DeepMind Gemini Series (1.5 Pro, 1.0 Pro, 1.5 Flash), OpenAI GPT-4o and mini, Meta Llama 3 (70B and 8B versions), specialized math models (Mathstral-7B, NuminaMath-7B-CoT), Mistral AI, Microsoft Phi and more. 1️⃣ Create pairs of grade-school math problems where the answer to the first (Q1) is needed for the second (Q2), Compositional GSM dataset. 2️⃣ Evaluate LLMs on both individual problems (Q1, Q2 separately) and the combined pairs. 3️⃣ Compare the combined accuracy (accuracy of Q1 * accuracy of Q2) with the expected individuall accuracy ⇒ reasoning gap Insights 💡 LLMs struggle with multi-hop reasoning, leading to a "reasoning gap" in chained math problems. 🤔 The reasoning gap might come from distraction and too much additional context (indicating missing training data?). 📈 Larger LLMs generally perform better than smaller, specialized models 📚 Fine-tuning on grade-school math can lead to overfitting, hindering generalization to chained problems. 💡 Instruction tuning and code generation improvements differ between model sizes. 📊 High scores on standard benchmarks don't reflect true reasoning abilities in multi-step problems. ❌ OpenAI o1 preview or o1 mini were not tested (probably weren't released at that time) Paper: https://lnkd.in/eaNcPdrS

I tried different ways to learn machine learning fast - only this worked — Learn at your pace Learning machine learning (ML) is a journey unique to each individual Your background—whether in mathematics, programming, or another field—plays a significant role in how quickly you grasp concepts But here’s the key: it's not about how long it takes, but about reaching true mastery Imagine two students, Student A and Student B. Each has a different academic background, say one with a strong foundation in statistics and the other in computer science. If we were to predict how long it would take each to master ML, we might model this with probability distributions. These predictions could be based on named academic predictors like prior experience with coding or understanding of linear algebra. The predicted learning time for each student would be a linear combination of the means of these distributions. Even though the means (average times) differ, what's important is that both students eventually reach the same level of mastery. In ML, as in life, the path to success isn’t about racing to the finish line—it’s about understanding the journey and mastering the skills, no matter how long it takes What you should focus on is getting the right roadmap and not asking how long it would take you To get the roadmap to Machine Learning, check the link on my profile: - no email collection - no requests whatsoever Just start learning!

🚀 Excited to share one of my favorite projects that I completed as part of a 35-hour course on Machine Learning 1 - Supervised Learning at Information Technology Institute (ITI) 🔍 Project Overview: I developed a facial landmark detection and pose estimation system using machine learning techniques. Leveraging the power of Mediapipe and scikit-learn libraries, I built a model that can accurately detect facial landmarks and estimate the pitch, yaw, and roll angles of a face in an image or video. 🤖 Key Features: - Utilized the FaceMesh module from Mediapipe for facial landmark detection. - Employed Support Vector Regression (SVR) for pose estimation, achieving impressive accuracy in predicting facial orientation angles. - Implemented Principal Component Analysis (PCA) for dimensionality reduction and feature extraction. 🌟 Learning Highlights: Through this project, I gained hands-on experience in: - Preprocessing and feature engineering techniques for machine learning models. - Model selection and hyperparameter tuning using GridSearchCV. - Creating interactive applications with real-time facial analysis. 💡 Next Steps: I'm thrilled to continue exploring machine learning and delve deeper into advanced topics. I'm also eager to apply these skills to real-world problems and contribute to innovative solutions. 🙌 Gratitude: A huge shoutout to the instructor eng Mohamed Hussien 💓 for their guidance and support throughout the course. You can see the full code here ⬇ https://lnkd.in/d7-NUSJs

Want to learn machine learning for materials domain or other such application domains?! Our (Hariprasad Kodamana and Ravinder Bhattoo) book titled “Machine learning for materials discovery” is out! Special thanks to Markus J. Buehler for writing the foreword for the book! This book came out of a need to educate and train materials (or other such domains such as civil or mechanical) students/researchers on machine learning. Thanks to the excellent support and encouragement from our editor Zach Evenson! The book covers basics of machine learning in first eight chapters. All techniques are explained with the help of python codes which the students can try out on a hands on mode. Then, the next chapters discuss applications of these methods in different materials domain. We hope that the book will be useful for: 1. Students trying to learn machine learning for an application domain such as materials. 2. Engineers and industry personnel interested in learning machine learning in a hands-on fashion. 3. Researchers aiming to employ machine learning for their domain. 4. And finally, course instructors in materials and allied disciplines looking for a textbook for their course. Do check it out! https://lnkd.in/gWgQJ9qJ Finally, this book also marks my beginning as the series editor for a springer book series on “Machine intelligence for materials science” with Zach Evenson. https://lnkd.in/g-YsepXm If you are interested in writing a book (original or edited), do reach out! We are looking for contributions in the areas of LLMs for materials, AI-driven simulations, AI and experiments to name a few. Looking forward to excellent contributions!

Even if you did well in high school math, the math in machine learning papers looks like hieroglyphics. Minimal math skills for machine learning are at the undergraduate level. This article covers foundational math, classical machine learning, deep learning, and cutting-edge machine learning. It contains links to free resources for learning each topic. https://lnkd.in/gsQ36QU9

#Machine #Learning #Artificial #Intelligence #ai 🤖 Machine Learning with Artificial Intelligence 🤖 This free online course on Alison takes you through the rudiments and fundamentals of artificial intelligence. This free online course will be of great interest to students and individuals who have requisite background knowledge of computer programming and science. The course describes how artificial intelligence is used to tackle complex real world problems like speech recognition and machine translations. By the end of this course, you will be able to use artificial intelligence techniques to solve computational problems using real life scenarios.

🚀 Key Learnings from the Project: "Machine Learning Capstone" This project demonstrated developing a personalized course recommender system using machine learning techniques. Through exploratory data analysis (EDA), high demand for courses like Python, Machine Learning, and Big Data was uncovered, with significant user engagement insights. Content-based filtering was implemented using Word2Vec and dot product similarity to analyze course descriptions and titles. K-means clustering and PCA were applied to group users based on interests for improved personalisation, resulting in an average of 36+ new course suggestions per user. Collaborative filtering models, including KNN, NMF, and Neural Networks, were compared, with KNN performing the best. Embedding-based approaches further enhanced predictions by combining user and item embeddings, where Bagging SVM achieved the highest AUC for classification tasks. This end-to-end project highlights the power of data-driven recommendations in driving user satisfaction and optimizing learning pathways. 🚀

One of the biggest problems with Machine Learning courses: Many courses dive into intense theory and mathematics way too soon. Disclaimer: I have a lot of experience with the theory, studying math in undergrad at Georgia Tech, as well as in my Master’s degree in Machine Learning. I actually really enjoy theory and math. But I still believe that jumping into an excessive amount of theory, early on, is not the right approach. Multivariable calculus, for example, is often listed as a prerequisite for many ML courses. I know many students who have then gone on to complete entire courses in Multivariable Calculus before ever getting into the actual ML concepts. This delays their ML journey, and many of them have gotten overwhelmed or lost in the pages of calculus before ever getting into the impactful and practical concepts from Machine Learning. You don’t need to study absurd amounts of math in isolation before starting your first ML course. Instead, you can jump right in, and review the math as it comes up. That way the math is actually motivated, and you know why you’re studying a particular topic. I recently talked about this with Grant Sanderson, the founder of 3Blue1Brown, and he shared the same opinion. Subjects like multivariable calculus can be a co-requisite to ML, not a strict prerequisite. -- 👉 If you want to learn the unfair way to master AI/ML, follow Dev G 📕 Don’t miss out on the First-Principles Framework, a step-by-step guide that thousands are using master the fundamentals: https://lnkd.in/ee75YqAG

Learning mathematics for machine learning involves several key areas of study, as mathematical concepts are foundational to understanding algorithms and models used in the field. Here’s a structured approach to guide your learning: 1. Linear Algebra Key Topics: Vectors, matrices, matrix operations, eigenvalues, eigenvectors, and singular value decomposition. Resources: Books: "Linear Algebra and Its Applications" by Gilbert Strang. Online Courses: MIT OpenCourseWare Linear Algebra course. 2. Calculus Key Topics: Derivatives, integrals, multivariable calculus, gradients, and optimization. Resources: Books: "Calculus" by James Stewart. Online Courses: Khan Academy’s Calculus course or Coursera’s Calculus courses. 3. Probability and Statistics Key Topics: Probability distributions, Bayes' theorem, expectation, variance, hypothesis testing, and statistical inference. Resources: Books: "Introduction to Probability" by Dimitri P. Bertsekas and John N. Tsitsiklis. Online Courses: Stanford’s "Statistical Learning" course on Coursera. 4. Optimization Key Topics: Convex optimization, gradient descent, and Lagrange multipliers. Resources: Books: "Convex Optimization" by Stephen Boyd and Lieven Vandenberghe. Online Courses: Stanford's Convex Optimization course. 5. Additional Topics Information Theory: Understanding concepts like entropy and mutual information can be beneficial. Graph Theory: Useful for understanding certain algorithms in machine learning. 6. Practical Application Implement Concepts: Use programming languages like Python with libraries such as NumPy and SciPy to implement mathematical concepts. Projects: Work on machine learning projects that require you to apply mathematical theories, such as regression analysis or neural networks. 7. Online Platforms and Communities Kaggle: Participate in competitions to apply your skills in real-world scenarios. Forums: Engage with communities like Stack Overflow, Reddit (e.g., r/MachineLearning), and specialized Discord servers for discussions and resources. 8. Study Approach Consistent Practice: Regularly solve problems and exercises to reinforce your understanding. Combine Theory with Practice: Balance theoretical study with practical implementation to see how mathematics underpins machine learning algorithms. By following this structured approach and utilizing the recommended resources, you can build a solid mathematical foundation for understanding machine learning. #Mathematics #MATLAB #MATHEMATICA #Algebra #Calculus

I just finished the Machine Learning course provided by Coursera from Andrew Ng. Here's what my experience was like: Pros: - Comprehensive Coverage: Covers most features of supervised and unsupervised learning. - Important Concepts: Learned the concepts of Linear and Logistic Regression, Neural Networks, SVM, Recommender Systems, Clustering Algorithms, Dimensionality Reduction, PCA, and methods of Model Evaluation and Validation. -Solid Foundation: You will be well-equipped to specialize in machine learning with a well-versed knowledge base. Cons: -Programming Tools: The course uses Octave and MATLAB, which are painful compared to C++ or Python. -Examples and Exercises: Sometimes, the course becomes a little monotonous, and there are very few practical examples. Further, the programming assignments are usually much more challenging than the examples provided in the lessons, so you will have to study extra and take a lot of time to solve them. -Explanation of Advanced Topics: Many times, the course does not explain some things that are hard or advanced as well as it should, especially the equations and their notations. Recommendation: ○ Whom it is for: I would recommend this course to people who have some knowledge about AI, algebra, calculus, and at least some programming background. ○Whom it is not for: I wouldn't recommend it to people who have absolutely no idea about AI or don't know any math. Despite all the cons, I think it's a very good basis for further learning and deepening in machine learning