Blog #154: Machine Learning Review April 2023

Photo by Glen Carrie on Unsplash

[ED: There’s a bit of a mix of content here. On balance, it’s 3/5 propeller hats.]

Here’s my review of all of the interesting things that happened in machine intelligence in April 2023.

Transformers in 5 minutes This article provides a concise and simplified explanation of the transformer architecture, a key technology in natural language processing, explaining its components like encoders, decoders, and attention mechanism with code examples. #MachineLearning #Transformers #NaturalLanguageProcessing #DataScience #AIExplained

Transformers from scratch This in-depth guide offers a comprehensive explanation of transformers in machine learning, focusing on their application in natural language processing, and provides a step-by-step breakdown of key concepts like matrix multiplication and attention mechanisms, making it accessible for beginners. #MachineLearning #Transformers #NLP #TechEducation #DataScienceBasics

Formal Algorithms for Transformers This paper presents a comprehensive and mathematically precise overview of transformer architectures and algorithms in machine learning, emphasising formal algorithms and pseudocode, covering aspects like attention, encoding, and decoding, and offering a deeper understanding for both theoreticians and practitioners. #Transformers #MachineLearning #AIResearch #DeepLearning #PseudocodeInAI

A Visual and Interactive Guide to the Basics of Neural Networks Jay Alammar’s post offers an engaging and visual introduction to neural networks for beginners, using simple examples and interactive elements to explain fundamental concepts like weights, bias, and training processes in machine learning. #NeuralNetworks #MachineLearningBasics #InteractiveLearning #TechEducation #AIforBeginners

A Visual And Interactive Look at Basic Neural Network Math Jay Alammar’s post offers an interactive and visual exploration of feedforward neural networks, using the Titanic dataset as an example. It explains key concepts like input neurons, weights, biases, and activation functions like sigmoid and ReLU, making it accessible and engaging for those new to neural network math. #NeuralNetworks #InteractiveLearning #MachineLearning #DataScience #AIExploration

Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention) Jay Alammar’s post is an insightful and visually rich exploration of the mechanics of sequence-to-sequence models and attention mechanisms in neural machine translation. It provides clear visualisations and explanations of how these models work, including the attention process, making it a valuable resource for those seeking to understand complex AI translation models. #NeuralMachineTranslation #Seq2Seq #AttentionMechanism #AIandLanguage #DeepLearningVisualisation

The Illustrated Transformer Jay Alammar’s post offers a comprehensive and accessible guide to understanding the Transformer model in machine learning. It uses clear illustrations and explanations to demystify complex concepts like self-attention, multi-headed attention, and positional encoding, making it an invaluable resource for anyone interested in deep learning and natural language processing. #Transformers #MachineLearning #AIExplained #DeepLearning #NLPVisualisation

How GPT3 Works - Visualizations and Animations Jay Alammar’s post offers a detailed and accessible explanation of GPT-3, a state-of-the-art language processing AI. It covers its training process, architecture, and internal mechanisms with visual aids and animations, making it easier to grasp the complexities of this advanced AI model. #GPT3 #AIExplained #MachineLearning #DeepLearning #TechVisualisation

The Illustrated Retrieval Transformer Jay Alammar’s post delves into the RETRO (Retrieval-Enhanced Transformer) model, a more size-efficient alternative to large language models like GPT-3. It explains how RETRO integrates a retrieval database, allowing it to perform comparably with significantly fewer parameters by accessing external information, and provides a detailed breakdown of its architecture and processing methods. #RETRO #AI #MachineLearning #LanguageModels #TechInnovation

The Illustrated Stable Diffusion by Jay Alammar provides a thorough and visually engaging explanation of the Stable Diffusion model, detailing its components, such as the text encoder, UNet neural network, and image decoder. The post explains how Stable Diffusion generates images from textual descriptions by converting text to numeric representations, creating images in latent space, and then decoding these into visual outputs, making it a great resource for understanding this advanced AI image generation technique. #StableDiffusion #AIImaging #MachineLearning #DeepLearning #TechVisualisation

D2L.ai Chapter 11 Attention Mechanisms and Transformers This section from the “Dive into Deep Learning” book provides a detailed exploration of attention mechanisms and transformers in deep learning. It covers topics such as queries, keys, values, attention pooling, scoring functions, and transformer architecture, offering a thorough understanding of these concepts with theoretical explanations and practical examples. #DeepLearning #Transformers #AttentionMechanisms #MachineLearning #AIResearch

MiniGPT-4: Enhancing Vision-language Understanding with Advanced Large Language Models The MiniGPT-4 project explores the integration of a visual encoder and a large language model (Vicuna) to enhance vision-language understanding. By training just a single projection layer, MiniGPT-4 demonstrates advanced multi-modal capabilities like generating detailed image descriptions and creating websites from handwritten text, similar to GPT-4, but with a focus on computational efficiency and improved generation reliability through curated datasets. #MiniGPT4 #VisionLanguageAI #AIResearch #MachineLearning #DeepLearningInnovation

What is Attention. in Language Models? This video provides an educational overview of the attention mechanism in language models. It explains how attention helps in focusing on specific parts of input data, enhancing the model’s ability to understand and generate language, making it a useful resource for those interested in the technical aspects of natural language processing and AI. #machine #learning #howto #code #video - #LanguageModels #AttentionMechanism #AI #MachineLearning #TechEducation

The Narrated Transformer Language Model This video provides an in-depth explanation of the Transformer model in language processing. It narrates the key concepts and workings of the Transformer, including its architecture, self-attention mechanism, and its role in modern natural language processing tasks, making it a valuable resource for learners and enthusiasts in AI and machine learning. #machine #learning #howto #code #video #Transformers #LanguageProcessing #AI #DeepLearning #TechEducation

Building LLM applications for production This article discusses the challenges and strategies in deploying large language models (LLMs) for production. It covers various aspects, including the ambiguity of natural languages in prompt engineering, integrating control flows and tools for complex tasks, and exploring promising use cases for LLM applications. The post also addresses the importance of systematic prompt engineering and the balance between cost, latency, and performance in LLM deployment. #machine #learning #howto #code #LLMEngineering #AIProduction #PromptEngineering #MachineLearning #AIChallenges

What are Transformer Models and How Do They Work? This article offers a beginner-friendly explanation of transformer models in machine learning. It discusses their architecture and functionality, illustrating how transformers effectively maintain context in language processing. The post is designed to demystify these complex models, making them more approachable for those new to the field. #machine #learning #howto #code #Transformers #MachineLearning #AI #TechEducation #DeepLearning

Elixir Nx Scholar is a machine learning toolkit built atop Nx in Elixir. It includes algorithms for various tasks like classification, regression, clustering, and preprocessing. The toolkit emphasizes traditional machine learning methods, and for deep learning, it refers users to Axon. Example notebooks are available under the “Guides” section, demonstrating Scholar’s practical applications. #nx #machine #learning #howto #code #MachineLearning #ScholarToolkit #ElixirProgramming #DataScience #AIAlgorithms

Originally published by M@ on Medium.