Best of Neural Networks — 2024
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Medium·2y
12 Fundamental Math Theories Needed to Understand AI
Understanding AI requires knowledge of several key mathematical theories, including the Curse of Dimensionality, Law of Large Numbers, Central Limit Theorem, Bayes’ Theorem, Overfitting and Underfitting, Gradient Descent, Information Theory, Markov Decision Processes, Game Theory, Statistical Learning Theory, Hebbian Theory, and Convolution. These concepts are foundational in AI and enhance understanding of its development.
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The Palindrome·2y
Machine Learning From Zero is ready to go!
Machine Learning From Zero (mlfz) is an open-source project featuring a tensor library built from scratch and an interactive 100+ pages textbook on neural networks. The project aims to help readers understand neural networks by breaking them down and rebuilding them. Topics include computational graphs, backpropagation, and vectorization.
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Daily Dose of Data Science | Avi Chawla | Substack·2y
15 DS/ML Cheat Sheets
This post collates 15 cheat sheets covering essential data science and machine learning concepts. It includes resources on translating between different data manipulation libraries, multi-GPU training strategies, testing ML models in production, neural network optimization, and more. Detailed links are provided for further reading.
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YouTube·2y
All Machine Learning algorithms explained in 17 min
Tim, a data scientist with over 10 years of experience, offers an intuitive overview of critical machine learning algorithms to help you choose the right one for your problem. The post covers supervised learning (like regression and classification), unsupervised learning (like clustering), and dives into specific algorithms such as linear regression, logistic regression, K-nearest neighbors (KNN), support vector machine (SVM), naive Bayes classifier, decision trees, random forests, boosting, neural networks, and dimensionality reduction. Each algorithm is explained with examples to build an intuitive understanding of their functions and applications.
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Machine Learning Mastery·2y
5 Influential Machine Learning Papers You Should Read
Discover five influential machine learning papers that have shaped the field. Highlights include the introduction of the Transformer model in 'Attention is All You Need,' the interpretation of neural networks as decision trees, the impact of unsupervised preprocessing on cross-validation bias, low-rank adaptations for large language models with LoRA, and insights into overcoming overfitting on small datasets with 'grokking.' These papers have significantly advanced model architecture, evaluation, adaptation, and generalization in machine learning.
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freeCodeCamp·2y
Deep 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.
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Quastor Daily·2y
The Engineering behind Instagram's Recommendation Algorithm
The post discusses the engineering behind Instagram's recommendation algorithm, including the candidate generation/retrieval phase, the use of Two Tower Neural Network models, and the ranking stages in the recommendation system.
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A crash course on RAG systems—Part 5
Part 5 of the RAG crash course focuses on the implementation of key components for multimodal RAG systems, such as CLIP embeddings, multimodal prompting, and tool calling. The series aims to educate readers on building reliable RAG systems that can reduce costs and handle complex data types, ultimately aiding businesses in achieving greater impact.
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A 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.
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DigitalOcean Community·2y
Constructing 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.
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Collections·2yBuild Your Own Llama, LLMs From Scratch, and Understanding Meta’s Transfusion Model
Discover a comprehensive guide to improving your Large Language Model (LLM) skills in 2024. Learn to create a local-first vector database with RxDB and transformers.js, delve into Meta's Transfusion model merging text and image generation, and understand how to build the Llama 3 architecture from scratch using PyTorch. Explore essential concepts like genetic algorithms and neural networks, and discover strategies for local-first AI solutions for document management and chat applications. Engage with the community and remember the importance of taking breaks for productivity.
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Stephen Wolfram Writings·2y
What’s Really Going On in Machine Learning? Some Minimal Models
Machine learning's fundamentals remain a mystery despite extensive engineering progress in neural networks. This post delves into simplified models that help visualize and understand core phenomena underlying machine learning, revealing its complex nature and dependence on computational irreducibility. The findings align machine learning with biological evolution's adaptive processes, suggesting that success in training neural networks often comes from exploiting inherent computational complexity rather than structured mechanisms.
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5 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.
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Understand AI and Neural Networks by Manually Adjusting Parameters
Learn AI and Neural Networks by adjusting parameters manually in a custom playground. Dr. Radu's course covers key topics like Neural Networks' math, hidden layers, and Dijkstra's algorithm. Gain hands-on experience and a solid understanding of AI fundamentals.
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Data Science Central·2y
30 Features that Dramatically Improve LLM Performance
The post covers innovative features that significantly enhance Large Language Model (LLM) performance by improving speed, reducing resource usage, and enhancing security. Key highlights include techniques like approximate nearest neighbor search, nested hash tables for sparse databases, and adaptive loss functions. It also emphasizes the importance of contextual tokens, agentic LLMs, and data augmentation through dictionaries for professional usage.
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Google for Developers·2y
Machine Learning Crash Course: Neural Networks Intro
This explains the transition from linear models to neural networks for modeling nonlinear relationships. It covers how traditional linear models use feature crosses and introduces the concept of hidden layers in neural networks. The key highlight is the use of activation functions, like ReLU, to introduce nonlinearity, enabling neural networks to approximate complex functions and automatically learn nonlinear relationships during training.
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Towards AI·1y
LLaMA explained !
LLaMA, an advanced open-source large language model by Meta, brings several enhancements over conventional transformer architecture, such as grouped multi-query attention, RMS normalization, and rotary positional embeddings. These innovations result in more efficient computation and dynamic learning capabilities, making LLaMA a competitive choice for large language model applications.
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A Practical Guide to Choosing the Right Algorithm for Your Problem: From Regression to Neural Networks
This guide provides clear guidelines for selecting an appropriate machine learning algorithm based on the type of problem, data complexity, interpretability needs, and data volume. It features a question-based template for identifying the right algorithm and a table of real-world use cases with recommended algorithms and key considerations.
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Hacker News·2y
Crash Course in Deep Learning (for Computer Graphics)
The post provides a comprehensive guide to deep learning for computer graphics. It introduces neural networks, specifically multilayer perceptrons (MLPs), and their structure, explaining key concepts such as neurons, layers, and activation functions. The guide further covers the implementation and training of these networks, including gradient descent and backpropagation. It also touches upon advanced topics like input encodings and the Adam optimizer, and discusses common challenges in training neural networks. Recommended practices and resources for further study are provided.
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Machine Learning Crash Course: Neural Networks Backprop
Neural networks utilize backpropagation to adjust the weights of nodes to improve accuracy in classification tasks. This method assigns blame to different nodes based on their contribution to the error, adjusting parameters more significantly when the error is high. Techniques like these are crucial for tasks such as image classification, although different neural network configurations might be required for specific types of problems.
