Adeel Naim Khan’s Post

Subtracting Matrices: A Step-by-Step Guide with Applications in AI and Machine Learning https://lnkd.in/dz98qzGB

Vector Institute's Researcher Wenhu Chen at University of Waterloo is pushing the boundaries of AI by improving and benchmarking foundation models. These efforts are crucial in developing more efficient, accurate, and scalable models that power a wide range of applications, from language understanding to data processing. Learn more about how Wenhu Chen’s team is shaping the future of AI research. https://lnkd.in/gr8CVrvg #AI #Research #FoundationModels #Innovation #MachineLearning

📢 **Excited to Share My Work on Support Vector Machines (SVM)** I'm thrilled to present a comprehensive PDF that I prepared, explaining the concepts and mathematics behind **Support Vector Machines (SVM)**. This resource dives into: - The intuition behind hyperplanes and support vectors - The role of kernels in transforming feature spaces - The dual formulation and optimization process - Applications of SVM in real-world scenarios This document was created as part of my journey to deepen my understanding of Machine Learning and to share knowledge with peers. I hope it can serve as a helpful guide for those exploring this powerful supervised learning algorithm. Feel free to check it out and share your feedback! Let's keep learning and growing together in the field of AI and ML. 🚀 #MachineLearning #SupportVectorMachines #SVM #AI #DataScience #LearningJourney

I am extremely happy to announce that yesterday, after ~12mo of hard work, I handed in the second edition of my book "Deep Generative Modeling" (https://lnkd.in/euZyg6ad) to the publisher. 💯 There are ~100 pages of new content! 📌 I will keep you updated! ToC: 💡 (Updated) Why Deep Generative Modeling? 📣 (NEW) Probabilistic modeling: From Mixture Models to Probabilistic Circuits 💡 (Updated) Autoregressive Models 📄 Flow-based Models 💡 (Updated) Latent Variable Models 📄 Hybrid Modeling 📄 Energy-based Models 📄 GANs 📣 (NEW) Score-based Generative Models 📄 Neural Compression 📣 (NEW) LLMs & GenAISys #GenerativeAI #DeepGenerativeModeling #LLMs #GenAISys

The Vital Role of Algebra in Machine Learning In the realm of machine learning, algebra is more than just a set of equations—it's the backbone of how models understand and interpret data. 1. Linear Algebra: Essential for operations involving vectors and matrices, linear algebra helps in transforming data, optimizing algorithms, and understanding concepts like singular value decomposition (SVD) and eigenvalues/eigenvectors. Key in neural networks, image processing, and recommendation systems. 2. Vector Spaces: These provide a framework for understanding how data points relate to each other, crucial for tasks like clustering and classification. Understanding vector spaces allows for more efficient data manipulation and feature extraction. 3. Matrix Operations: Matrices are used extensively in machine learning, from representing datasets to transforming data through operations like rotation, scaling, and translation. Matrix multiplication is particularly important in the optimization processes of training algorithms. 4. Optimization: Algebraic techniques are used to minimize cost functions, a core task in training models. Gradient descent, a fundamental optimization algorithm, relies heavily on algebraic principles to iteratively find the minimum of a function. Understanding these algebraic principles equips machine learning practitioners with the tools to develop more efficient, accurate, and scalable models. So, whether you're diving into deep learning or fine-tuning a simple regression model, remember: algebra is your ally. #MachineLearning #DataScience #LinearAlgebra #AI #DeepLearning #Optimization #startup #codesurgeai

Math is the building block of machine learning; it is founded on four major areas which enables the use of algorithms: Algebra, Calculus, Probability and Statistics. Below is a quick look at each: Linear Algebra: The foundation for data structures, used to manage and manipulate high dimensional data. Calculus: Deals with change and helps in optimising algorithms for better learning from data. Probability & Statistics: These two fields are the basis of decision making thereby facilitating predictions as well as inference from data. I will post my weekly notes here so follow along if you want more in-depth explanations about mathematical concepts behind machine learning! Artificial Intelligence, Generative AI, Machine Learning #ArtificialIntelligence #MachineLearning #EducationTechnology #EdTech #LifelongLearning #StudyNotes #DigitalLearning #AI #ML #LinkedInLearning #ProfessionalDevelopment #PersonalBranding #CareerGrowth #LearningAndDevelopment #SkillUp

🎓 Thrilled to have completed the MIT 6.86x Artificial Intelligence and Machine Learning course! 🚀 From mastering linear classifiers to diving deep into neural networks, reinforcement learning, and unsupervised learning, this journey was densely packed with challenging projects like digit recognition, automatic review analysis, and even building collaborative filtering systems! 💡✨ I explored linear classifiers and learned how to use perceptron algorithms to separate data with precision. 💡 I dived into nonlinear classification with Support Vector Machines (SVMs), discovering how to handle complex data structures and ensure optimal predictions. 🧠 The hands-on digit recognition project helped me apply neural networks to real-world problems, making it easy for me to understand how machines can "see" and interpret data. 🔢 Through collaborative filtering, I learned how K-Means clustering is used to build a movie recommendation system, tailoring suggestions based on user preferences. 🍿🎬 Understanding reinforcement learning showed me how AI agents can learn from their environment and improve through trial and error—perfect for gaming applications! 🎮🕹️ This course has been an incredible journey into the world of AI and ML! 🌍MIT 6.86x taught me not just theory, but how to apply it with projects like the automatic review analyzer and text-based games, unlocking the full potential of AI. 🔍📈 The world-class content offered at MIT and the focus on hands-on projects has set this course apart—it has helped me develop my AI coding skills through real-world applications and pushed my understanding of AI/ML to the next level. 📊🤖 #MachineLearning #ArtificialIntelligence #MIT #NeuralNetworks #ReinforcementLearning #Clustering #AIProjects #TechInnovation 

The term "overcomplete basis" gets thrown around casually in AI research, but wearing the hat of a mathematician, I find it misleading and imprecise. A basis, by definition, is linearly independent, and anything overcomplete introduces redundancy and ceases to be a basis at all. The correct mathematical framework lies in frames and redundant dictionaries, which provide rigorous tools for understanding sparsity, robustness, and expressiveness in AI models. In this blog, I dissect the misuse of "overcomplete basis," explain its proper mathematical foundation, and explore its critical role in sparse coding, neural representations, and signal reconstruction. Precision matters, especially where math meets AI. #ArtificialIntelligence #Math #Research https://lnkd.in/gCPi62HB

 Dive into the world of high-performance machine learning with Pixel Pioneers JAX tutorials! 🌟 JAX, with its unique ability to combine NumPy-like syntax with automatic differentiation and GPU/TPU acceleration, is transforming the landscape of machine learning research and development. Our tutorials cover advanced topics like reinforcement learning, anomaly detection, and sentiment analysis, enabling developers and researchers to leverage JAX's power for cutting-edge applications. 🔑 Key Benefits: Speed and Efficiency: JAX's just-in-time (JIT) compilation and automatic vectorization accelerate computations, making it ideal for large-scale models. Flexibility: Seamlessly integrates with neural network libraries like Flax and Haiku, allowing for versatile model building. Scalability: Optimized for running on GPUs and TPUs, ensuring your models scale with your ambitions. Explore our JAX tutorials to master these tools and take your machine learning projects to the next level! 🚀 https://lnkd.in/gSCA-nWF #MachineLearning #JAX #AI #DeepLearning #Tutorials #DataScience