Gradient

I compiled some of my favorite gradient makers. I will provide you with the direct links, give you descriptions, and include screenshot of the tools.

Every Web Designer Should Know These 13 Beautiful Gradient Tools 💯👍

In this article I will be giving a <a href="https://hackernoon.com/tagged/mathematical" target="_blank">mathematical</a> intuition of gradient descent and the reason behind moving in the direction opposite to the gradient.

The reason behind moving in the direction opposite to the Gradient

Proposes BSGAL, a gradient-based method for effective screening and utilization of generated data to improve downstream perception tasks.

BSGAL: Gradient-Based Screening for Long-Tailed Perception Tasks

The ideal contribution of generated data is approximated using a first-order Taylor expansion based on the gradient dot product. 

Ideal Contribution Estimation: Taylor Expansion and Gradient Cache

Proposes BSGAL, a Generative Active Learning algorithm that uses gradient cache to filter unlimited synthetic data for long-tailed instance segmentation.

Enhancing Long-Tailed Segmentation with Gradient Cache and BSGAL

This article outlines the implementation details for RECKONING, which uses a GPT-2-base model and runs on NVIDIA A100 GPUs.

Technical Setup for RECKONING: Inner Loop Gradient Steps, Learning Rates, and Hardware Specification

Learn why TensorFlow gradients return None, the common causes behind it, and how to fix unconnected or missing gradients in your model.

Debugging Disconnected Gradients in TensorFlow Step by Step

Compute gradients in TensorFlow with GradientTape—watching, models, non-scalars, control flow, and performance tips, all with clear, runnable examples.

Why GradientTape Is the Most Underrated Feature in TensorFlow

Optimizing neural networks with gradient descent and domain decomposition for faster, scalable, and more accurate AI training.

Why Gradient Descent Converges (and Sometimes Doesn’t) in Neural Networks

This article introduces G‑LSM, a sparse Hermite polynomial LSM variant using gradient info for efficient, accurate high‑dimensional American option pricing.

Efficient Price and Greeks Computation for American Options via Gradient-Enhanced LSM

Learn advanced gradient control in TensorFlow with tf.GradientTape, including custom gradients, Jacobians, and higher-order derivatives.

Optimizing Machine Learning Models with Precise Gradient Management in TensorFlow

Build low-level custom training loops in Keras using GradientTape and tf.function. Learn with hands-on GAN examples, metrics, and performance tips.

How to Write Custom Training Loops in Keras with GradientTape

SG methods scale ML but struggle with correlated spatial data. Authors extend SGLD using Vecchia approximation for unbiased GP gradient estimates.

Breaking the iid Barrier: How Stochastic Gradients Are Going Spatial

SGMCMC + Vecchia approximation enables scalable Bayesian GP inference for large spatial datasets, achieving linear time complexity and competitive performance.

Stochastic Gradient MCMC for Large-Scale Gaussian Process Spatial Modeling

Learn how to compute gradients using TensorFlow's GradientTape API for training models with automatic differentiation and eager execution.

Getting Started with Gradients and Automatic Differentiation in TensorFlow

Gradient-Free Training vs. Gradient Descent, How Gradient-Free Training Could Decentralize AI 

How Gradient-Free Training Could Decentralize AI

Discover gradient descent and its variants, including Adam optimizer. Learn how Adam simplifies machine learning optimization with efficiency and adaptability!

Extending Stochastic Gradient Optimization with ADAM

This article investigates the implications of the gradient of a scalar field in the context of local dark matter (DM) velocity.

Exploring the Gradient of a Scalar in Dark Matter Velocity Analysis

Learn how the gradient for the DPO objective under the Plackett-Luce model is derived. 

Deriving the Gradient of the DPO Objective

Could we create a meta-computational space, for machine learning to sort concepts, and artificial intelligence to navigate them?

Visualizing Colors: The Gradient Oscillation Hyperspace

Techniques like Stochastic Gradient Descent (SGD) are designed to improve the calculation performance but at the cost of convergence accuracy.

Understanding Stochastic Average Gradient

zkFL leverages zero-knowledge proofs (ZKPs) to tackle the issue of a malicious aggregator during the training model aggregation process. 

Zero Knowledge Proof based Gradient Aggregation for Federated Learning: Conclusion & References

Zero Knowledge Proof based Gradient Aggregation for Federated Learning: Results

Zero Knowledge Proof based Gradient Aggregation for Federated Learning: Theoretical Analysis

The Gradient is a high efficiency heat pump that customers can install in their homes without any special tools or skills

GRADIENT

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Gradient's stories on HackerNoon

Every Web Designer Should Know These 13 Beautiful Gradient Tools 💯👍

The reason behind moving in the direction opposite to the Gradient

BSGAL: Gradient-Based Screening for Long-Tailed Perception Tasks

Ideal Contribution Estimation: Taylor Expansion and Gradient Cache

Enhancing Long-Tailed Segmentation with Gradient Cache and BSGAL

Technical Setup for RECKONING: Inner Loop Gradient Steps, Learning Rates, and Hardware Specification

Debugging Disconnected Gradients in TensorFlow Step by Step

Why GradientTape Is the Most Underrated Feature in TensorFlow

Why Gradient Descent Converges (and Sometimes Doesn’t) in Neural Networks

Efficient Price and Greeks Computation for American Options via Gradient-Enhanced LSM

Optimizing Machine Learning Models with Precise Gradient Management in TensorFlow

How to Write Custom Training Loops in Keras with GradientTape

Breaking the iid Barrier: How Stochastic Gradients Are Going Spatial

Stochastic Gradient MCMC for Large-Scale Gaussian Process Spatial Modeling

Getting Started with Gradients and Automatic Differentiation in TensorFlow

How Gradient-Free Training Could Decentralize AI

Extending Stochastic Gradient Optimization with ADAM

Exploring the Gradient of a Scalar in Dark Matter Velocity Analysis

Deriving the Gradient of the DPO Objective

Visualizing Colors: The Gradient Oscillation Hyperspace

Understanding Stochastic Average Gradient

Zero Knowledge Proof based Gradient Aggregation for Federated Learning: Conclusion & References

Zero Knowledge Proof based Gradient Aggregation for Federated Learning: Results

Zero Knowledge Proof based Gradient Aggregation for Federated Learning: Theoretical Analysis

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