Post-Training Quantization Explained: How to Make Deep Learning Models Faster and Smaller
Large deep learning models are powerful but often too bulky and slow for real-world deployment. Their size, computational demands, and […]
Neural Network Fundamentals
Neural Network Fundamentals
Quantization-Aware Training: The Best of Both Worlds
Imagine you are a master artist, renowned for creating breathtaking paintings with an infinite palette of colors. Your paintings are
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Understanding PEFT: A Deep Dive into LoRA, Adapters, and Prompt Tuning
Imagine you’re trying to teach a world-class chef a new recipe. Instead of retraining them from scratch, you just show
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What is Batch Normalization and Why is it Important?
Batch normalization was introduced in 2015. By normalizing layer inputs, batch normalization helps to stabilize and accelerate the training process,
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Knowledge Distillation: Principles And Algorithms
The sheer size and computational demands of large ML models, like LLMs, pose significant challenges in terms of deployment, accessibility,
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Gradient Scaling: Improve Neural Network Training Stability
Gradient scaling is a technique aimed at managing gradient magnitudes, primarily in the context of mixed-precision training. It involves adjusting the scale of gradients to prevent underflow or overflow during floating-point computations.
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Gradient Clipping: A Key To Stable Neural Networks
Gradient clipping emerges as a pivotal technique to mitigate gradient explosion and gradient vanishing, ensuring that gradients remain within a manageable range and thereby fostering stable and efficient learning.
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Residual Connections in Machine Learning
One of the critical issues in neural networks is the problem of vanishing and exploding gradients as the depth of
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How to Initialize Weights in Neural Networks: A Deep Dive
Weight initialization in neural networks significantly influences the efficiency and performance of training algorithms. Proper initialization strategies can prevent issues
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The Vanishing and Exploding Gradient Problem in Neural Networks: How to Overcome It
Two critical issues that often arise in training deep neural networks are vanishing gradients and exploding gradients. These issues can
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How to Choose the Best Learning Rate Decay Schedule for Your Model
The training process involves optimizing a model’s parameters to minimize the loss function. One crucial aspect of this optimization is
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Understanding the Bias-Variance Tradeoff: How to Optimize Your Models
In ML and statistical modeling, the concept of bias-variance trade-off is fundamental to model performance. It serves as a guiding
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