Join me on an exciting journey exploring the world of Artificial Intelligence, Machine Learning, and Data Science. Whether you’re just starting or have some experience, let’s break down complex topics and learn together. My mission is to make AI and ML approachable and practical for everyone.
Using GitHub Actions and CML for CI & CD. Machine learning workflows are complex and time-consuming, involving tasks like data processing, model training, and evaluation. Integrating Continuous Integration (CI) and Continuous Deployment (CD) practices can automate these tasks, saving time, reducing errors, and improving collaboration.
SAM 2 (Segment Anything Model 2) is an advanced machine learning model designed for comprehensive object segmentation in both static images and dynamic videos. Developed by Meta AI Research, SAM 2 represents a significant leap forward in computer vision capabilities, offering real-time performance and zero-shot generalization. This tutorial will explore the key features of SAM 2, its architecture, and how to get started with using this powerful tool.
In today’s data-rich world, being able to retrieve relevant information across different modalities (text, images, audio) has become increasingly important. This post will guide you through creating a multi-modal retrieval system that combines text embeddings from BGE (Bidirectional Generative Encoder) and image embeddings from CLIP (Contrastive Language-Instrumental Pre-training) to index and query Wikipedia articles.
Ollama empowers you to leverage powerful large language models (LLMs) like Llama2,Llama3,Phi3 etc. without needing a powerful local machine. Google Colab’s free tier provides a cloud environment perfectly suited for running these resource-intensive models. This tutorial details setting up and running Ollama on the free version of Google Colab, allowing you to explore the capabilities of LLMs without significant upfront costs.
This chapter will be your comprehensive guide to navigating the fascinating world of LLMs. We’ll delve into their core concepts, exploring different types like autoregressive models and encoder-decoder models. You’ll discover the magic behind self-attention, a mechanism that allows LLMs to focus on relevant information, and delve into the pre-training strategies that give them their vast knowledge. Finally, we’ll showcase the real-world applications of LLMs, from powering chatbots and generating realistic dialogue to creating marketing copy and summarizing complex topics.
Reading_time: 5 min Tags: [LLM, GenAI, AI ,MachineLearning, NLP, ComputerVision, MLOps DeepLearning, DataScience, TechInnovation,FutureTech ]
Dive into the World of NLP.This article introduces the basics and scope of NLP, explaining what NLP is and why it matters in today’s tech landscape. It explores real-world applications and provides historical context, showcasing the evolution and milestones of NLP. You’ll also learn about the inner workings of NLP, including techniques like tokenization, stemming, and tagging. The article addresses the challenges and limitations in developing NLP systems, highlighting the technical hurdles and complexities. Additionally, it offers further resources, such as popular datasets, libraries, and online courses, to deepen your understanding.
yolosegment2labelme - a Python package that allows you to convert YOLO segmentation prediction results to LabelMe and anylabeling JSON format. This tool facilitates the annotation easy.
Reading_time: 5 min Tags: [YOLO, ,Annotation, LabelMe, AnyLbaeling, ComputerVision, Json]
I recently wrapped a tutorial on summarization techniques in LangChain. This article covers the basic usage of document summarization techniques and provides insights into various summarization methods. Additionally, to learn more and to explore how to validate intermediate results from the output of each of these techniques.
![2-Introduction to LLM — [AIMagazine]](/assets/img/blog/AIMagazine.png)
