Medium·2yUnderstanding LLMs from scratch using middle school math
This post explains how large language models (LLMs) function using basic math concepts. It covers various components like neural networks, embeddings, self-attention, softmax, and the GPT and transformer architectures. The approach is highly educational, using simplified explanations and visual aids to make the concepts accessible to those with minimal mathematical background.
PyImageSearch·1yPNG Image to STL Converter in Python
Learn how to convert a PNG image to an STL file using TripoSR in Python. This guide walks through setting up the environment, importing necessary libraries, processing the image to create a 3D model, and converting the model from OBJ to STL format. Ideal for designers, engineers, or hobbyists aiming to create 3D printable objects from 2D images.
Machine Learning Mastery·2y10 Must-Know Python Libraries for Machine Learning in 2024
Machine learning in 2024 has seen significant evolution, with Python continuing to lead the way through its extensive libraries. The field has transitioned from foundational frameworks in 2020, like TensorFlow and PyTorch, to increased emphasis on transformers, AutoML, and scalability by 2024. Key trends include deep learning dominance, scalability, automation, optimization, ecosystem consolidation, and interactive data visualization. Understanding core ML frameworks, data manipulation libraries, visualization tools, and domain-specific utilities is crucial for modern ML tasks.
3Blue1Brown·2yLarge Language Models explained briefly
The post explains large language models (LLMs), how they function, and the complexities behind their training. LLMs predict the next word in a sequence based on probabilities, using vast amounts of text data for training. The introduction of transformers in 2017 allowed for parallel processing of text, enhancing computation efficiency. Pre-training is supplemented by reinforcement learning with human feedback to refine model predictions. The sheer scale of data and computation involved is formidable, taking advantage of specialized hardware like GPUs.
Machine Learning Mastery·2y7 Free Resource to Master LLMs
Large Language Models (LLMs) are increasingly popular, with many companies seeking expertise in this area for AI-driven automation and optimization. This post reviews seven free resources, including courses from Cohere, Stanford, and Microsoft, as well as roadmaps and tutorials on GitHub and DataCamp. These resources aim to equip learners with the skills needed to understand, build, and deploy LLMs in various applications.
Data Science Central·2yMachine Learning Algorithms: Linear Regression, Decision Trees, and K-Nearest Neighbors
Machine learning algorithms like linear regression, decision trees, and k-nearest neighbors are pivotal for predictive modeling and data analysis. Linear regression establishes a linear relationship between variables, while decision trees provide a hierarchical approach to decision-making through data splits. K-nearest neighbors assume that similar data points are clustered together, and the distance metric used can significantly impact performance. Implementing these algorithms in Python, specifically using libraries like scikit-learn and numpy, helps in building powerful predictive models. Moreover, handling multivariate data, applying ensemble methods, and dealing with outliers are crucial aspects for enhancing accuracy and reliability.
freeCodeCamp·2yDeep Learning Course – Math and Applications
Learn the math behind deep learning with a 14-hour course on the freeCodeCamp YouTube channel. Developed by Ayush Singh, the course covers fundamental concepts, deep learning techniques, mathematical insights, and practical applications. Topics include vectors, matrices, linear algebra, calculus, machine learning, and neural networks.
Medium·2yMy Machine Learning Journey: Perfect Roadmap for Beginners
A practical, project-based learning approach can be highly effective for mastering machine learning (ML). Starting with essential math concepts and gaining proficiency in Python and key libraries like NumPy, Pandas, and scikit-learn can lay a strong foundation. Engaging in projects not only aids in learning but also stands out to potential employers. Deploying projects and engaging in competitions like Kaggle or hackathons and networking with the community can further enhance skills. Transitioning to deep learning should be considered once ML fundamentals are mastered, with a focus on techniques like CNNs, RNNs, Transfer Learning, and more advanced methods like GANs and Transformers for specialized tasks.
ByteByteGo·2yWhere to get started with GenAI
Generative AI (GenAI) is rapidly advancing with new models and techniques emerging frequently. This guide helps developers get started by understanding terminologies, utilizing Model APIs, and building GenAI applications. Key concepts include AI, machine learning, NLP, transformer models, and prompt engineering. Practical steps for integrating GenAI into applications and customizing models through techniques like fine-tuning and retrieval-augmented generation (RAG) are also covered.
freeCodeCamp·2yPractical Guide to Linear Algebra in Data Science and AI
Linear algebra is a practical tool that can be used to solve real-world problems in data science and AI. It is applied across various industries, and understanding its core concepts is essential for working with machine learning, deep learning, computer vision, and generative AI. A linear algebra roadmap for 2024 is provided to guide your learning journey, and there are numerous resources available to help you master linear algebra.
Community Picks·2yMachine Learning and Deep Learning Courses on YouTube
Curated YouTube courses cover foundational machine learning, deep learning, specialized applications such as healthcare, NLP, and practical uses like deploying large language models. Courses are suitable for various learning stages, providing knowledge from basic concepts to real-world implementations.
Substack·2yHow I Aced Machine Learning Interviews: My Personal Playbook
Preparing for a machine learning interview can be daunting with various rounds such as ML breadth, depth, system design, and coding challenges. Effective preparation involves a balanced focus on fundamental ML topics, specialized knowledge for senior roles, and understanding of system design principles. Resources like Coursera, Udacity, and specific ML books are highly recommended. Every interview is a learning journey; plan accordingly and consult with hiring company guidelines for best results.
Daily Dose of Data Science | Avi Chawla | Substack·2yA Crash Course on Graph Neural Networks
Graph Neural Networks (GNNs) extend deep learning techniques to graph data, addressing the limitations of traditional models in capturing complex relationships. This piece covers the basics, benefits, tasks, data challenges, frameworks, and practical implementation of GNNs.
DigitalOcean Community·2yConstructing Neural Networks From Scratch: Part 1
Building neural networks from scratch provides foundational understanding of deep learning. While popular frameworks like TensorFlow and PyTorch simplify neural net implementation, they may obscure core concepts. This guide explains foundational neural network components using Python and NumPy to solve tasks like the XOR logic gate. It addresses mathematical foundations, forward and backward propagation, and outlines the steps to train a simple neural network without deep learning frameworks.
Medium·2y5 AI Books for Software Engineers
This post provides a list of 5 AI books for software engineers with a good balance of theory and practice. It covers topics such as machine learning with PyTorch and Scikit-Learn, deep learning, and understanding deep learning. The post also includes links to additional resources and recommends taking a slow and thorough approach to learning.
Machine Learning Mastery·2y5 Tips for Getting Started with Deep Learning
Deep learning, a subset of machine learning inspired by the human brain, has become essential in areas like computer vision, speech recognition, and text generation. To get started, focus on understanding machine learning basics, select a comfortable deep-learning framework (such as TensorFlow, PyTorch, or Keras), learn neural network architectures, start with simple projects, and practice regularly while engaging with the community for feedback and guidance.
GoPenAI·2yHow to build Neural Network with real-world dataset using PyTorch
Learn how to build and train a neural network model using the FitBit Fitness Tracker Dataset and PyTorch. The post provides a step-by-step guide and covers topics such as importing libraries, loading and preparing the data, defining the model, training and evaluating the model, and making predictions on new data. By following the post, readers can build and train their own neural network models for various use cases.
3Blue1Brown·2yHow might LLMs store facts | Chapter 7, Deep Learning
Large language models, like those using transformer architectures, can store factual information within their numerous parameters. Recent research has identified that this knowledge is often embedded in specific parts of the network called multi-layer perceptrons (MLPs). The process involves vectors in high-dimensional space, where different directions encode different types of information. Understanding how these models operate, particularly the role of the MLPs and the influence of nearly perpendicular vectors, provides insight into how AI models can store and recall vast amounts of data efficiently.
KDnuggets·2yTop 5 Free Machine Learning Courses to Level Up Your Skills
Highlighting five free machine learning courses to enhance your skills, this guide covers a range of options from deep learning with Andrew Ng's 'Generative AI for Everyone' to Stanford's classic 'CS229: Machine Learning'. It also includes specialized courses like 'Mathematics for Machine Learning' by Imperial College London and practical deep learning applications with fast.ai. Ideal for both beginners and those with some coding experience, these resources provide a solid foundation in the field of machine learning.
Community Picks·2y10 Critical AI Concepts Explained in 5 Minutes
Acquire a transversal understanding of high-relevance AI jargon in a concise guide covering 10 key concepts. These include artificial intelligence, machine learning, deep learning, generative AI, large language models, and responsible AI. Understand the foundational elements of AI, from algorithms and training data to ethical considerations and AI bias.
