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Hello everyone, this article is a written form of a tutorial I conducted two weeks ago with Neurons Lab. If you prefer a narrative walkthrough, you can find the YouTube video here: As always, you can find the code on GitHub, and here are separate Colab Notebooks: Planning and reasoningDifferent types of memoriesVarious types of toolsBuilding complete agentsIntroduction to the agents Illustration b
The Need For AI PatternsWe all anchor to some tried and tested methods, approaches and patterns when building something new. This statement is very true for those in software engineering, however for generative AI and artificial intelligence itself this may not be the case. With emerging technologies such as generative AI we lack well documented patterns to ground our solution's. Here I share a ha
Figure 1: Root Cause Workflows for LLM RAG Applications (flowchart created by author) If you have been experimenting with large language models (LLMs) for search and retrieval tasks, you have likely come across retrieval augmented generation (RAG) as a technique to add relevant contextual information to LLM generated responses. By connecting an LLM to private data, RAG can enable a better response
This article covers the following “hyperparameters” sorted by their relevant stage. In the ingestion stage of a RAG pipeline, you can achieve performance improvements by: Data cleaningChunkingEmbedding modelsMetadataMulti-indexingIndexing algorithmsAnd in the inferencing stage (retrieval and generation), you can tune: Query transformationsRetrieval parametersAdvanced retrieval strategiesRe-ranking
The Wonderful World of RAG Fusion. Illustration by author.Having explored search technologies for almost a decade, I can honestly say nothing has been as disruptive as the recent rise of Retrieval Augmented Generation (RAG). This system is revolutionising search and information retrieval using vector search with generative AI to produce direct answers based on trusted data. In my search projects,
Image by authorPrologueAs the wave of interest in Large Language Models (LLMs) surges, many developers and organisations are busy building applications harnessing their power. However, when the pre-trained LLMs out of the box don’t perform as expected or hoped, the question on how to improve the performance of the LLM application. And eventually we get to the point of where we ask ourselves: Shoul
Let’s see a brief description of the columns of our dataset: age (numeric)job : type of job (categorical: “admin.” ,”unknown”,”unemployed”, ”management”, ”housemaid”, ”entrepreneur”, ”student”, “blue-collar”, ”self-employed”, ”retired”, ”technician”, ”services”)marital : marital status (categorical: “married”,”divorced”,”single”; note: “divorced” means divorced or widowed)education (categorical: “
The Quick-start Guide Isn’t Enough“Retrieval augmented generation is the process of supplementing a user’s input to a large language model (LLM) like ChatGPT with additional information that you (the system) have retrieved from somewhere else. The LLM can then use that information to augment the response that it generates.” — Cory Zue LLMs are an amazing invention, prone to one key issue. They mak
Image created by the authors.How can we test applications built with LLMs? In this post we look at the concept of testing applications (or prompts) built with language models, in order to better understand their capabilities and limitations. We focus entirely on testing in this article, but if you are interested in tips for writing better prompts, check out our Art of Prompt Design series (ongoing
Image created by the author.OpenAI stunned the world when it dropped ChatGPT in late 2022. The new generative language model is expected to totally transform entire industries, including media, education, law, and tech. In short, ChatGPT threatens to disrupt just about everything. And even before we had time to truly envision a post-ChatGPT world, OpenAI dropped GPT-4. In recent months, the speed
This gentle introduction to the machine learning models that power ChatGPT, will start at the introduction of Large Language Models, dive into the revolutionary self-attention mechanism that enabled GPT-3 to be trained…
It appears that every sophisticated ML team has built a feature store for their ML platform. Uber built Palette. Airbnb built Zipline. Netflix built Time Travel. Google Cloud worked with our customer GoJek to build Feast. Fortunately, you no longer need to build or manage your own. Google Cloud Vertex AI offers a fully managed feature store as does Sagemaker. There are even companies like tecton.a
Class imbalance is not a problem. Debunking one of the most widespread misconceptions in the ML community.
Photo by Kai Dahms on UnsplashI love creating software libraries. Two months ago, I started porting one of our Python packages into a Rust crate. This new Rust crate matches the Python package’s ease of use and expressiveness. Along the way, I learned nine rules that can help you create beautiful libraries in Rust. The rules are: Create examples that don’t embarrass you.Accept all kinds of strings
Tuning deep learning pipelines is like finding the right gear combination (Image by Tim Mossholder on Unsplash)Why should you read this post?The training/inference processes of deep learning models are involved lots of steps. The faster each experiment iteration is, the more we can optimize the whole model prediction performance given limited time and resources. I collected and organized several P
On 20th May 2021 Google held its developer conference I/O and announced a new algorithm for their search engine: MUM, a Multitask Unified Model [1]. For the last two years, BERT was the underlying model for their search engine. BERT was a breathtaking release and was state-of-the-art until now, until MUM came. The algorithm BERT changed a lot in the field of NLP and was applied in thousands or eve
if you are not careful your shortcuts will cost you a lot afterwardsAirflow permissive approach will let you schedule any custom code (jobs) but you will create a spaghetti stack if you do not follow very strict SEPARATION OF CONCERN design between the airflow dags and your jobs. Airflow allow you to run your jobs without isolation with the framework itselfAt the origin Airflow was sort of a “supe
Image: ShutterstockThis post was co-authored with Petar Veličković. See also my last year’s prediction, Michael Galkin’s excellent post on the current state of affairs in Graph ML, a deeper dive into subgraph GNNs, techniques inspired by PDEs and differential geometry and algebraic topology, and how the concepts of symmetry and invariance form the picture of modern deep learning. Summing up impres
Image by author.Table of contentsIntroductionAutomated Exploratory Data Analysis packages 2.1 DataExplorer 2.2 GGally 2.3 SmartEDA 2.4 tableoneConclusionsReferences1. IntroductionExploratory Data Analysis (EDA) aims at performing an initial investigation on the data by summarizing their characteristics through statistical and visualization techniques, and it is a critical early step in any Data Sc
Data Science is the current buzzword in the market. Every company at the moment is looking to hire Data Science Professionals to solve some Data problem that they themselves are not aware of currently. Machine Learning has taken over the industry by storm and we have a bunch of self taught Data Scientists in the market. Since this Data Science word is an altogether different universe, it is very d
It’s been quite a year for Graph ML — thousands of papers, numerous conferences and workshops… How do we catch up with so many cool things happening around? Well, we are puzzled as well and decided to present a structured look at Graph ML highlighting 🔥 trends and major advancements. The image was generated by ruDALL-E with a prompt “graphs floating in space”.Whether you are working on a narrower
(Image by Author) PyCaret’s New Time Series Module🚪 IntroductionPyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive. In comparison with the other open-source machine learning libraries, PyCaret
IntroductionIt was in January of 2021 that OpenAI announced two new models: DALL-E and CLIP, both multi-modality models connecting texts and images in some way. In this article we are going to implement CLIP model from scratch in PyTorch. OpenAI has open-sourced some of the code relating to CLIP model but I found it intimidating and it was far from something short and simple. I also came across a
Image made by the authorAs a data scientist, I daily use Python to build applications that rely on credentials and sensitive settings. Here are some examples of those, off the top of my head: API keys to access third-party services
Transformer word clouds generated with Python codes. Image by authorTransformers — Hello and we’re meeting again. We have a date, aren’t we, RoBERTa? If you have read and followed through with my earlier post on Transformers, can you rate the…
Image by authorBuilding a transformer model from scratch can often be the only option for many more specific use cases. Although BERT and other transformer models have been pre-trained for many languages and domains, they do not cover everything.
One of FLAML’s algorithms CFO tuning the # of leaves and the # of trees for XGBoost. The two heatmaps show the loss and cost distribution of all configurations. The black dots are the points evaluated in CFO. Black dots connected by lines are points that yield better loss performance when evaluated (image by authors).Authors: Qingyun Wu, Chi Wang, Antoni Baum, Richard Liaw and Michael Galarnyk FLA
IntroRecently I accidentally came across the new book of Bill Inmon and Francesco Puppini called “Unified Star Schema” (will refer to it USS downstream). Having a new book in 2020 from the father of data warehousing definitely grabbed my attention, I bought it and read it in the…
Photo by Circe Denyer on PublicDomainPictures.netUsually, when I see BatchNorm and Dropout layers in a neural network, I don’t pay them much attention. I tend to think of them as simple means to speed up training and improve generalization with no side effects when the network is in inference mode. In this post, I will show why this notion is not always correct, and may cause the neural network to
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