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.
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.
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.
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.
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.
Machine Learning Mastery·2y5 Free Books on Computer Vision
Computer vision, a branch of AI focused on interpreting visual data, has evolved significantly with deep learning architectures like Convolutional Neural Networks. For mastering this field, the post lists five free books catering to both foundational knowledge and advanced models: 'Computer Vision: Algorithms and Applications', 'Computer Vision: Models, Learning, and Inference', Stanford course notes, 'Programming Computer Vision with Python', and 'Deep Learning' by MIT Press.
YouTube·2yHow to Learn AI and Get Certified by NVIDIA
NVIDIA offers a range of AI courses, some free and some paid, to help you learn and get certified in AI. These courses cover various aspects of AI including generative AI, retrieval augmented generation, CUDA, deep learning, and prompt engineering. NVIDIA certifications can validate your skills and make you stand out in the job market. Learning paths are available for foundational AI skills and more advanced topics like large language models and transformers.
Windows Developer·2yHow Machine Learning Models Actually Work... the Easy Way
AI models autonomously make decisions or predictions without human intervention. They can be trained using algorithms which apply to inputs and generate desired outputs. Machine learning models, a subset of AI, improve their performance over time. AI models can be classified into supervised, unsupervised, and reinforcement learning based on their training methodologies. Additionally, models can be categorized as generative or discriminative, depending on their approach to predicting outputs. Models also vary by task, including classification or regression purposes, making them versatile for various applications from recommendation engines to natural language processing.
Daily Dose of Data Science | Avi Chawla | Substack·2y6 Graph Feature Engineering Techniques
Discover essential techniques for graph feature engineering, crucial for building effective graph neural networks (GNNs). Learn how to create a dummy social networking graph dataset and derive key features like node degree and centrality measures using NetworkX. The post highlights the significance of these features in enhancing model performance and provides real-world examples of graph machine learning applications by tech giants. Gain insights into various GNN tasks, data challenges, frameworks, and advanced architectures.
Machine Learning News·2ySpeechBrain: A PyTorch-based Speech Toolkit
SpeechBrain is a PyTorch-based toolkit designed to address the complexities of modern speech and audio processing tasks, including automatic speech recognition, text-to-speech synthesis, and speaker recognition. It offers a modular and flexible framework that leverages PyTorch’s efficient tensor operations and GPU acceleration to enable faster training and inference. Researchers and developers can experiment with different neural network architectures and techniques to adapt models to specific tasks and datasets, achieving state-of-the-art results.
Hacker News·2yneural-maze/agentic_patterns: Implementing the 4 agentic patterns from scratch
This repository showcases the implementation of four agentic patterns defined by Deeplearning.ai using Groq. It includes simple API calls without relying on frameworks like LangChain or LlamaIndex. The patterns covered are Reflection, Tools for external access, Planning through subgoals, and Multi-agent roles. Each pattern is demonstrated with step-by-step explanations and complete Python implementations.