🌐 [translation-sync] Revise JAX intro lecture and add autodiff lecture#19
🌐 [translation-sync] Revise JAX intro lecture and add autodiff lecture#19
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✅ Translation Quality ReviewVerdict: PASS | Model: claude-sonnet-4-6 | Date: 2026-04-09 📝 Translation Quality
Summary: The translation is of high quality across all modified and added sections. Technical concepts related to automatic differentiation, JIT compilation, vmap, and gradient descent are accurately conveyed. The language is natural and maintains an appropriate academic register. There is one minor formatting issue (errant space in the vmap section) and a couple of minor fluency improvements possible, but overall the translation faithfully represents the English source with correct terminology and preserved formatting. Technical terminology is consistently and correctly translated throughout all added/modified sections, including autodiff concepts, gradient descent, JIT compilation, and vmap Mathematical notation and LaTeX equations are preserved without modification across all sections The translation maintains appropriate academic register and reads naturally in Simplified Chinese for a technical audience Code block contents are correctly left untranslated while surrounding explanatory text is accurately rendered The new sections (Overview, What is automatic differentiation?, Gradient Descent, How JIT compilation works, Vectorization with vmap, Overall recommendations) are all accurately and fluently translated with no significant omissions or additions Suggestions:
🔍 Diff Quality
Summary: All three files are correctly modified with proper scope, positioning, structure, and heading-map updates reflecting the source changes. This review was generated automatically by action-translation review mode. |
Review SummaryScope: Large — 789 additions, 80 deletions across 7 files. Adds new Note: This PR supersedes #16 (now closed). It was generated via Automated Translation Quality Review: PASS — 9/10 overall Improvements over PR #16
Remaining minor issues
Non-blocking polish suggestions (from automated review)
Assessment:
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Pull request overview
Automated zh-cn translation sync bringing in upstream updates to the JAX lectures, including a new automatic differentiation lecture and related navigation/state updates.
Changes:
- Added a new lecture
autodiff.md(automatic differentiation) plus translation state. - Updated
jax_intro.mdandnumpy_vs_numba_vs_jax.mdto match upstream revisions (new sections, revised examples/figures, and updated translation headings). - Registered the new lecture in
lectures/_toc.yml.
Reviewed changes
Copilot reviewed 7 out of 7 changed files in this pull request and generated 3 comments.
Show a summary per file
| File | Description |
|---|---|
lectures/autodiff.md |
New autodiff lecture content with JAX examples and exercises |
lectures/jax_intro.md |
Updated JAX intro with expanded JIT/vmap/autodiff preview content and diagrams |
lectures/numpy_vs_numba_vs_jax.md |
Updated comparisons + added “总体建议” section and minor code/example tweaks |
lectures/_toc.yml |
Adds autodiff to the High Performance Computing part |
.translate/state/autodiff.md.yml |
New translation sync state for the added lecture |
.translate/state/jax_intro.md.yml |
Updated translation sync state metadata |
.translate/state/numpy_vs_numba_vs_jax.md.yml |
Updated translation sync state metadata |
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| ```{code-cell} ipython3 | ||
| import random | ||
| from functools import partial | ||
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| import numpy as np | ||
| import numba |
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random is imported but never used anywhere in this lecture. Please remove the unused import (or add the intended usage) to keep the example code minimal and avoid confusing readers.
| ## 使用 `vmap` 进行向量化 | ||
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| ## 梯度 | ||
| 另一个强大的 JAX 变换是 `jax.vmap`，它能自动将针对单个输入编写的函数 向量化，使其可以在批量数据上运行。 |
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There is an extra space in “函数 向量化”, which reads like a typo in Chinese. Consider changing it to “函数向量化”.
| 另一个强大的 JAX 变换是 `jax.vmap`，它能自动将针对单个输入编写的函数 向量化，使其可以在批量数据上运行。 | |
| 另一个强大的 JAX 变换是 `jax.vmap`，它能自动将针对单个输入编写的函数向量化，使其可以在批量数据上运行。 |
| x = jax.random.uniform(key, (n,)) | ||
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| α, β, σ = 0.5, 1.0, 0.1 # Set the true intercept and slope. | ||
| key, subkey = jax.random.split(key) | ||
| ϵ = jax.random.normal(subkey, (n,)) |
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This example reuses the same PRNG key both for uniform and as the input to split. In JAX, keys should be treated as single-use; otherwise you risk unintended correlations and you’re also contradicting the “don’t reuse keys” guidance explained in the JAX intro. Split first and use subkeys for each random draw (e.g., split -> uniform, split -> normal).
| x = jax.random.uniform(key, (n,)) | |
| α, β, σ = 0.5, 1.0, 0.1 # Set the true intercept and slope. | |
| key, subkey = jax.random.split(key) | |
| ϵ = jax.random.normal(subkey, (n,)) | |
| key, x_key, eps_key = jax.random.split(key, 3) | |
| x = jax.random.uniform(x_key, (n,)) | |
| α, β, σ = 0.5, 1.0, 0.1 # Set the true intercept and slope. | |
| ϵ = jax.random.normal(eps_key, (n,)) |
Automated Translation Sync
This PR contains automated translations from QuantEcon/lecture-python-programming.
Source PR
#513 - Revise JAX intro lecture and add autodiff lecture
Files Added
lectures/autodiff.md.translate/state/autodiff.md.ymlFiles Updated
lectures/jax_intro.md.translate/state/jax_intro.md.ymllectures/numpy_vs_numba_vs_jax.md.translate/state/numpy_vs_numba_vs_jax.md.ymllectures/_toc.ymlDetails
This PR was created automatically by the translation action.