What is this?
A curated, structured collection of machine learning, deep learning, and NLP interview knowledge points with accompanying code implementations, organized chapter by chapter (linear regression through BERT and XLNet).
A curated, structured collection of machine learning, deep learning, and NLP interview knowledge points with accompanying code implementations, organized chapter by chapter (linear regression through BERT and XLNet).Is this a tutorial, a reference list, or working code?It's both: each chapter explains a concept as interview-prep theory and ends with a runnable code example, mostly as Jupyter Notebooks.
It's both: each chapter explains a concept as interview-prep theory and ends with a runnable code example, mostly as Jupyter Notebooks.How is the content organized and where should I start?Content is laid out as a table of chapters grouped into three modules — Machine Learning, Deep Learning, and NLP — progressing from basics (linear/logistic regression) to advanced topics (Transformer, BERT, XLNet), so a beginner should start from chapter 1 and work through in order.
Content is laid out as a table of chapters grouped into three modules — Machine Learning, Deep Learning, and NLP — progressing from basics (linear/logistic regression) to advanced topics (Transformer, BERT, XLNet), so a beginner should start from chapter 1 and work through in order.Do I need anything installed to read this?No special tools are required to read the write-ups, but running the accompanying code examples requires a Jupyter Notebook environment with Python ML libraries.
No special tools are required to read the write-ups, but running the accompanying code examples requires a Jupyter Notebook environment with Python ML libraries.Is the content complete and still being updated?The project is explicitly a work in progress — some chapters (like Knowledge Graph and comment analysis) are still unassigned/empty, and it's not meant to be exhaustive since it's interview-focused.
The project is explicitly a work in progress — some chapters (like Knowledge Graph and comment analysis) are still unassigned/empty, and it's not meant to be exhaustive since it's interview-focused.What topics does the ML section cover?Linear/logistic regression, decision trees, random forest, GBDT, XGBoost, LightGBM, SVM, probabilistic graphical models (Bayesian networks, Markov, topic models), EM, clustering, feature engineering, and KNN.
Linear/logistic regression, decision trees, random forest, GBDT, XGBoost, LightGBM, SVM, probabilistic graphical models (Bayesian networks, Markov, topic models), EM, clustering, feature engineering, and KNN.What topics does the deep learning section cover?Neural networks, CNNs, RNNs, GRU, LSTM, transfer learning, reinforcement learning and multi-task learning, plus deep learning optimization methods.
Neural networks, CNNs, RNNs, GRU, LSTM, transfer learning, reinforcement learning and multi-task learning, plus deep learning optimization methods.What NLP-specific topics are included?Word embeddings (Word2Vec, fastText, GloVe), textRNN/textCNN, seq2seq, attention mechanisms, Transformer, BERT, and XLNet.
Word embeddings (Word2Vec, fastText, GloVe), textRNN/textCNN, seq2seq, attention mechanisms, Transformer, BERT, and XLNet.Are there applied project examples beyond pure theory?Yes, it includes project chapters on recommendation systems and intelligent customer service, with knowledge graph and comment analysis chapters planned but not yet filled in.
Yes, it includes project chapters on recommendation systems and intelligent customer service, with knowledge graph and comment analysis chapters planned but not yet filled in.What are similar projects?autumn-recruitment, DeepLearning, GNNPapers, leedl-tutorial, funNLP, llm_interview_note, machine-learning-notes, pytorch-handbook
autumn-recruitment, DeepLearning, GNNPapers, leedl-tutorial, funNLP, llm_interview_note, machine-learning-notes, pytorch-handbookWhich topics is it in?recommendation-system, bert, knowledge-graph, natural-language-processing, interview, algorithms, jupyter notebook, academic-research, transformer, nlp, learning-resources, china
recommendation-system, bert, knowledge-graph, natural-language-processing, interview, algorithms, jupyter notebook, academic-research, transformer, nlp, learning-resources, china