From Anna Karenina to DeepFM: a Chinese DL tutorial archive

What it does

This repository holds fourteen Jupyter notebooks that implement classic deep learning demos—text generation, word embeddings, GANs, neural style transfer, sequence-to-sequence translation, and CTR models—written to accompany a Chinese-language Zhihu column. The projects span TensorFlow 1.6, Keras, and TensorFlow 2.0, serving as a practical reference for foundational architectures rather than a unified library. Most folders link to a specific article explaining the underlying theory, making the collection a curated curriculum of mid-2010s “get your hands dirty” deep learning.

The interesting bit

The repo is essentially a time capsule of TensorFlow’s evolution, frozen in tutorial form; you can watch the framework age from raw 1.6 sessions to TF 2.0 eager execution across adjacent projects. Its real value lies in the pairing of runnable code with detailed Chinese prose, turning a scattered set of blog posts into a persistent, star-gathering archive.

Key highlights

• Fourteen distinct modules, from a character-level LSTM trained on Anna Karenina to implementations of DeepFM, Deep&Cross, xDeepFM, and AutoInt.
• Side-by-side model comparisons, including KNN versus CNN on CIFAR and DNN versus LSTM versus Text-CNN for sentiment analysis.
• A Keras-based translation notebook that adds attention visualization and BiRNN to a Seq2Seq baseline, reporting a BLEU score near 0.9 on training data.
• Low-level TensorFlow implementations of skip-gram and batch normalization, showing the math beneath the API.
• A neural style transfer section that includes animated GIFs with filenames referencing Marvel characters and famous paintings such as Starry Night, The Scream, and Guernica.

Caveats

• The README and project context are entirely in Chinese, so non-Mandarin readers will need a translation tool or tolerance for context-free code.
• Several notebooks rely on TensorFlow 1.6, which is obsolete and unlikely to run on modern Python environments without significant archaeology.
• The generate_lyrics module lacks a linked article or detailed description in the README, so its exact scope is unclear.

Verdict

Worth bookmarking if you read Chinese and want to see how foundational deep-learning architectures were once implemented in plain TensorFlow. Everyone looking for maintained, production-grade packages should keep scrolling.