Sandeep97 articles in Deep Learning — page 2 of 5. Practical, up-to-date guides written to be found, answered, and cited.
GraphSAGE vs GAT: Comparing Graph Neural Network Architectures — a practical 2026 guide to graphsage vs gat: comparing graph, for developers and founders.
Getting Started with Federated Learning Using Flower — a practical 2026 guide to getting started, core concepts, best practices, real data and FAQs.
Reinforcement Learning from Human Feedback: A Practical Walkthrough — a practical 2026 guide to reinforcement learning, for developers and founders.
What Is Direct Preference Optimization and How Does It Work — a practical 2026 guide to direct preference optimization, for developers and founders.
How Graph Neural Networks Power Fraud Detection at Scale — a practical 2026 guide to fraud detection, core concepts, best practices, real data and FAQs.
Flower vs NVIDIA FLARE: Choosing a Federated Learning Framework — a practical 2026 guide to flower vs nvidia flare: choosing, for developers and founders.
Is RLHF Worth It, or Has DPO Made It Obsolete — a practical 2026 guide to RLHF worth it,, core concepts, best practices, real data and FAQs, updated for 2026.
Best Synthetic Data Generation Tools for 2026 — a practical 2026 guide to synthetic data generation tools, core concepts, best practices, real data and FAQs.
When Should You Use Active Learning Instead of Labeling Everything — a practical 2026 guide to active learning instead of labeling, updated for 2026.
Self-Supervised Learning Explained: A Complete Guide — a practical 2026 guide to self supervised learning explained:, for developers and founders.
How to Build a Graph Neural Network with PyTorch Geometric — a practical 2026 guide to graph neural network, for developers and founders, updated for 2026.
PyTorch Geometric vs DGL: Which GNN Library Wins in 2026 — a practical 2026 guide to pytorch geometric vs dgl:, for developers and founders, updated for 2026.
Reinforcement Learning vs Supervised Learning: Key Differences — a practical 2026 guide to reinforcement learning vs supervised learning:, updated for 2026.
How Does RLHF Actually Shape Large Language Models — a practical 2026 guide to RLHF actually shape large language, for developers and founders.
What Is Federated Learning and Why It Matters in 2026 — a practical 2026 guide to federated learning, core concepts, best practices, real data and FAQs.
Transformer Architecture Trends to Watch Through 2026 — a practical 2026 guide to transformer architecture trends to watch, for developers and founders.
What Are Hyena Operators and Can They Replace Attention — a practical 2026 guide to hyena operators, core concepts, best practices, real data and FAQs.
How Does the Attention Mechanism Work Under the Hood — a practical 2026 guide to under the hood, core concepts, best practices, real data and FAQs.
Rotary vs Absolute Positional Encodings: Which Should You Use — a practical 2026 guide to rotary vs absolute positional encodings:, updated for 2026.
How to Compress Neural Networks With Pruning and Distillation — a practical 2026 guide to compress neural networks, for developers and founders.
What Is Flow Matching and Why Are Labs Adopting It — a practical 2026 guide to flow matching, core concepts, best practices, real data and FAQs.
Best Open-Source Transformer Libraries for Researchers in 2026 — a practical 2026 guide to open source transformer libraries, for developers and founders.
How Do Vision-Language Models Fuse Image and Text Embeddings — a practical 2026 guide to vision language models fuse image, for developers and founders.
Linear Attention Explained: Trading Softmax for Speed at Scale — a practical 2026 guide to linear attention explained: trading softmax, updated for 2026.