#cross-entropy loss

4 related articles

2026年6月13日·4 min

Neural Networks from Scratch: A Deep Dive into the Fundamentals from Linear Regression to Gradient Descent

From linear regression and logistic regression to gradient descent, this guide derives the core mechanisms of neural networks step by step, covering Sigmoid, cross-entropy, activation functions, and backpropagation.

2026年6月12日·3 min

Using AI Tools to Write Your Graduate Thesis: Completing Deep Learning Experiments Without a CS Background

How can non-CS graduate students use AI tools like Cursor to efficiently complete their thesis? A complete guide covering data sourcing, code adaptation, and AI-assisted modifications.

The "Worse is Better" Philosophy of Large Model Design: Why Simple and Brutal Beats Refined and Complex

Deep Dives

2026年6月3日·2 min

The "Worse is Better" Philosophy of Large Model Design: Why Simple and Brutal Beats Refined and Complex

Analyzing the "worse is better" philosophy in large model architecture: why DeepSeek V4 dropped N-gram, why Transformer dominates AI, and three iron laws of simple, efficient model design.

Complete Guide to LLM Training: Pre-training, SFT Fine-tuning, and Preference Alignment Explained

Deep Dives

2026年6月3日·3 min

Complete Guide to LLM Training: Pre-training, SFT Fine-tuning, and Preference Alignment Explained

Complete guide to the three core LLM training stages: pre-training, supervised fine-tuning (SFT), and preference alignment (DPO/PPO), covering LoRA, distillation, quantization, and pruning.