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Tag: pytorch

  • How AMD GPUs Enable Deep Learning – Detailed

    How AMD GPUs Enable Deep Learning (for Novices) – Detailed Imagine training a computer to recognize patterns in vast amounts of data, like identifying diseases from medical images or understanding the sentiment behind millions of social media posts. Deep learning, a powerful subset of artificial intelligence, makes this possible. However, the sheer volume of calculations… Read more

  • Agentic AI Applications Architecture to Reduce Power Costs

    Designing Energy-Efficient Agentic AI Applications Designing the architecture of agentic AI applications with a focus on reducing power costs is a multifaceted challenge that requires careful consideration of various components, from the underlying hardware to the algorithms employed and the overall system design. Here’s a breakdown of key architectural considerations and strategies: 1. Efficient Hardware… Read more

  • Python Libraries for Video Motion Detection – Real-Life Use Cases

    Python Libraries for Video Motion Detection – Real-Life Use Cases Python libraries for video motion detection are employed in a wide array of real-world applications, leveraging their capabilities for various purposes. Here are some prominent examples, categorized by the libraries often used: OpenCV (cv2) – Use Cases OpenCV’s efficiency and versatility make it suitable for… Read more

  • Python Libraries for Image Object Identification

    Python Libraries for Image Object Identification Here’s a breakdown of popular Python libraries used for analyzing image object identification: High-Level Libraries (Easy to Use, Often with Pre-trained Models): TensorFlow Object Detection API (with Keras) A robust framework built on TensorFlow for constructing, training, and deploying object detection models. Keras simplifies building neural networks and offers… Read more

  • Data Structure of Trained ML Models

    Data Structure of Trained ML Models Once a machine learning model is trained, its “knowledge” is stored in a specific data structure that allows it to make predictions on new, unseen data. The exact structure varies depending on the type of model and the library used for training. However, the core idea is to save… Read more

  • Use cases: Driving Efficiency and Innovation Across Industries with Data Science

    Driving Efficiency and Innovation Across Industries with Data Science Data science is at the forefront of driving efficiency gains and fostering innovation across diverse industries. This article highlights ten compelling use cases that demonstrate this transformative power. 11. Price Optimization Domain: Retail, E-commerce, Hospitality Determining the optimal pricing strategy for products or services to maximize… Read more

  • Use Cases: Enhancing Customer Experience and Business Operations with Data Science

    Enhancing Customer Experience and Business Operations with Data Science Enhancing Customer Experience and Business Operations with Data Science Data science provides powerful tools to understand customers better, personalize their experiences, and optimize core business operations. This article explores ten key use cases in these areas. 1. Customer Churn Prediction Domain: Customer Relationship Management (CRM), Telecommunications,… Read more

  • Using local LLM for Document Extraction

    Non-Cloud LLM for Document Extraction This guide explains how to use a non-cloud version of a pretrained Large Language Model (LLM) for document extraction, focusing on open-source models and local execution. Phase 1: Setting Up Your Local Environment 1. Hardware Requirements Ensure your system meets the following recommendations: CPU/GPU: An NVIDIA GPU with sufficient VRAM… Read more

  • Automating PDF to JSON Extraction with AI/ML

    Automating PDF to JSON Extraction with AI/ML 1. Understanding the Problem and Defining Key Values for AI/ML When leveraging AI/ML for PDF to JSON extraction, the initial problem definition remains crucial, but with a focus on how AI/ML can address challenges posed by unstructured or highly variable documents. Identify the Key Values: As before, define… Read more

  • Detailed Explanation: Training and Inference Times in Machine Learning

    Detailed Explanation: Training and Inference Times in Machine Learning Training Time in Machine Learning: A Detailed Look Definition: Training time is the computational duration required for a machine learning model to learn the underlying patterns and relationships within a training dataset. This process involves iteratively adjusting the model’s internal parameters (weights and biases) to minimize… Read more

  • Tensor Reduction (Sum) with PyTorch and CUDA

    Tensor Reduction (Sum) with PyTorch and CUDA Tensor Reduction operations involve aggregating the values in a tensor across one or more dimensions to produce a tensor with a smaller number of dimensions (or a scalar). The sum reduction operation computes the sum of all elements (or elements along specified dimensions) of a tensor. CUDA significantly… Read more

  • Tensor Reshaping with PyTorch and CUDA

    Tensor Reshaping with PyTorch and CUDA Tensor Reshaping involves changing the shape of a tensor without altering its underlying data. This operation is frequently used to prepare tensors for different operations in neural networks and other numerical computations. While the reshaping operation itself is typically not computationally intensive, performing it on a GPU using CUDA… Read more

  • Matrix Multiplication with PyTorch and CUDA

    Matrix Multiplication with PyTorch and CUDA Matrix Multiplication is a fundamental operation in linear algebra and is crucial in many machine learning algorithms, especially in the layers of neural networks. CUDA significantly accelerates this operation by parallelizing the numerous multiply-accumulate operations involved. Code Example with PyTorch and CUDA import torch # Check if CUDA is… Read more

  • Accelerating Image Classification with CUDA

    Image Classification using CUDA CUDA (Compute Unified Device Architecture) significantly accelerates image classification tasks by leveraging the parallel processing power of NVIDIA GPUs. Deep learning models, which are commonly used for image classification, involve numerous matrix operations that are highly parallelizable and thus benefit greatly from GPU acceleration via CUDA. How CUDA Accelerates Image Classification… Read more

  • Exploring CUDA (Compute Unified Device Architecture)

    Exploring CUDA CUDA is a parallel computing platform and programming model developed by NVIDIA for use with their GPUs. It allows software developers to leverage the massive parallel processing power of NVIDIA GPUs for general-purpose computing tasks, significantly accelerating applications beyond traditional CPU-bound processing. 1. CUDA Architecture: The Hardware Foundation NVIDIA GPUs are designed with… Read more

  • Can AMD GPUs Train LLMs?

    Can AMD GPUs Train LLMs? AMD GPUs can be used to train Large Language Models (LLMs). While NVIDIA GPUs, particularly those with CUDA architecture, have historically dominated the LLM training landscape, AMD has been making significant strides in this area with its ROCm (Radeon Open Compute) platform. 1. ROCm Platform ROCm is AMD’s open-source software… Read more

  • AMD GPUs vs. NVIDIA GPUs for LLM Training

    AMD GPUs vs. NVIDIA GPUs for LLM Training Here we dive into how AMD GPUs can be used for LLM training, and compare them directly with the dominant player in this field: NVIDIA GPUs. Comparison: AMD vs. NVIDIA GPUs for LLM Training Feature NVIDIA GPUs AMD GPUs Dominant Architecture/Platform CUDA (Compute Unified Device Architecture) –… Read more

  • Building a Personalized Healthcare Recommendations AI Agent on GCP: A Comprehensive Guide

    Building a Personalized Healthcare Recommendations AI Agent on GCP: A Comprehensive Guide This article provides a detailed guide to building a Personalized Healthcare Recommendations AI Agent on Google Cloud Platform (GCP). We will explore the necessary GCP services, a comprehensive architecture, sample training data, the implementation of model training using Vertex AI, and the creation… Read more

  • Real-time Recommendation Engine AI Agent on AWS

    Real-time Recommendation Engine AI Agent on AWS Implementing a real-time recommendation engine AI agent on AWS requires a robust and scalable architecture. Here are implementation examples for key services in the tech stack: 1. Real-time Data Ingestion (Amazon Kinesis Data Streams): You would use the AWS SDK (Boto3 in Python) in your application backend to… Read more