deep learning
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Understanding Backpropagation: A Comprehensive Guide to Neural Network Learning
Introduction Backpropagation is a fundamental algorithm in the training of neural networks, enabling these complex systems to learn from data and improve their performance over time. This guide aims to demystify the backpropagation process, explaining its core concepts and significance in machine learning. The Objective of Neural Networks The primary goal of a neural network Continue reading
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ML | About Lora adapters : An Efficient Approach to Fine-Tuning Language Models
Introduction Low-Rank Adaptation (LoRA) is an innovative technique in machine learning, particularly useful for fine-tuning large language models. This post will explore how LoRA adapters work, why they’re efficient, and their practical applications in model adaptation. What are LoRA Adapters? LoRA adapters are a method to efficiently fine-tune large neural networks with significantly fewer parameters. Continue reading
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Understanding Self-Attention Mechanism in AI: A Deep Dive with PyTorch
In the world of artificial intelligence and deep learning, the self-attention mechanism has revolutionized how we process sequential data, particularly in natural language processing tasks. This blog post will explore the self-attention mechanism, implement it using PyTorch, and discuss its applications and benefits 📘 What is Self-Attention? Self-attention, also known as intra-attention, is a mechanism Continue reading
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FLOAT32, FLOAT16, AND BFLOAT16: PRECISION’S ROLE IN DEEP LEARNING
(continued from previous section) 🟧 Impact on Deep Learning Processes The choice of floating-point format significantly affects various aspects of deep learning, including training speed, memory usage, and computational accuracy. Let’s explore these impacts in detail. Training Speed The precision format directly influences the speed of neural network training: Many deep learning frameworks now support Continue reading
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Float32, Float16, and BFloat16: Precision’s Role in Deep Learning
Introduction: The Importance of Numerical Precision In the world of deep learning, the way numbers are represented can have a significant impact on model performance, training speed, and hardware requirements. At the heart of this lies floating-point arithmetic, a method computers use to handle real numbers with finite precision. Three floating-point formats have become particularly Continue reading
Think like a butterfly 