A Comprehensive Learning Path for NLP Algorithms

A Comprehensive Learning Path for NLP Algorithms

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As 2021 is about to end, looking back at the recent autumn recruitment for algorithm positions, it can be described as going from ashes to hell on earth. The trend has shifted: those considering a career change are starting to change careers, and those switching majors are starting to switch majors.

Many people want to transition to NLP because the technology has developed rapidly in recent years, with technologies like BERT, GPT-3, Graph Neural Networks, Knowledge Graphs being widely applied in project practices. This has also driven the continuous implementation of NLP in the industry and the demand for related talents.
However, recently a follower messaged me saying that NLP is hard to learn, and they wonder if they can stick to this path.
A Comprehensive Learning Path for NLP Algorithms

In response to this friend’s question, I would like to answer from two aspects.

NLP is Not Easy to Learn
A Comprehensive Learning Path for NLP Algorithms
01

Most students who wish to work in NLP-related fields often learn through self-study. However, the obvious issue is:

1. Although they have learned many algorithm models, their understanding of the technical depth and breadth is relatively weak, mostly remaining at the stage of calling existing tools like BERT, XLNet, etc. This leads to being easily recognized as novices by “old hands” in the NLP field during interviews or when actually working in the industry.
2. A lack of deep understanding of algorithm principles can lead to poor performance in practical applications. Furthermore, during interviews with major company interviewers, they can only watch helplessly as they lose offers.
How Well Do You Need to Learn to Get a Good Job?
A Comprehensive Learning Path for NLP Algorithms
02
Human energy is definitely limited. Since being overly eager for success is not advisable, how well do we need to learn to be considered “out of the mountain” and capable of navigating the world?
For interviews in the NLP industry, one must prove two things to the interviewer:
  • I know how to do it.
  • I have done it.
Indeed, what companies value most in this industry is project experience, but beginners often find it challenging to engage with industrial projects. What to do?
Don’t worry, I have prepared it for you.

To truly cultivate NLP talents comprehensively and systematically, the Greedy Academy has launched the “Natural Language Processing Lifelong Upgraded Version” course, covering all necessary technologies from classic machine learning, text processing techniques, sequence models, deep learning, pre-trained models, knowledge graphs, to graph neural networks. It includes practical industrial-level projects, with experienced NLP leaders providing live explanations to help you master the concepts and easily secure offers.

NLP Algorithm Engineer Training Program

Helping You Become theTOP 10% of Engineers

Students interested in the course

Scan the QR code for consultation

A Comprehensive Learning Path for NLP Algorithms

01 Course Outline
Part One: Basics of Machine Learning

Chapter 1: Overview of Natural Language Processing

  • Current Status and Prospects of Natural Language Processing

  • Applications of Natural Language Processing

  • Classic Tasks of Natural Language Processing

Chapter 2: Basics of Data Structures and Algorithms

  • Time Complexity, Space Complexity

  • Dynamic Programming

  • Greedy Algorithm

  • Various Sorting Algorithms

Chapter 3: Classification and Logistic Regression

  • Logistic Regression

  • Maximum Likelihood Estimation

  • Optimization and Gradient Descent

  • Stochastic Gradient Descent

Chapter 4: Model Generalization and Hyperparameter Tuning

  • Understanding Overfitting and Preventing Overfitting

  • L1 and L2 Regularization

  • Cross-Validation

  • Regularization and MAP Estimation

Part Two: Text Processing
Chapter 5: Text Preprocessing and Representation

  • Various Tokenization Algorithms

  • Word Normalization

  • Spelling Correction, Stop Words

  • One-Hot Encoding Representation

  • TF-IDF and Similarity

  • Distributed Representation and Word Vectors

  • Word Vector Visualization and Evaluation

Chapter 6: Word Vector Techniques

  • Advantages and Disadvantages of One-Hot Encoding

  • Advantages of Distributed Representation

  • Static and Dynamic Word Vectors

  • SkipGram and CBOW

  • Detailed Explanation of SkipGram

  • Negative Sampling

Chapter 7: Language Models

  • Role of Language Models

  • Markov Assumption

  • UniGram, BiGram, NGram Models

  • Evaluation of Language Models

  • Smoothing Techniques for Language Models

Part Three: Sequence Models
Chapter 8: Hidden Markov Models

  • Applications of HMM

  • HMM Inference

  • Viterbi Algorithm

  • Forward and Backward Algorithms

  • Detailed Explanation of HMM Parameter Estimation

Chapter 9: Linear Conditional Random Fields

  • Directed and Undirected Graphs

  • Generative and Discriminative Models

  • From HMM and MEMM

  • Label Bias in MEMM

  • Introduction to Log-Linear Models

  • From Log-Linear to LinearCRF

  • Parameter Estimation of LinearCRF

Part Four: Deep Learning and Pre-training
Chapter 10: Basics of Deep Learning

  • Understanding Neural Networks

  • Various Common Activation Functions

  • Backpropagation Algorithm

  • Comparison of Shallow and Deep Models

  • Hierarchical Representation in Deep Learning

  • Overfitting in Deep Learning

Chapter 11: RNN and LSTM

  • From HMM to RNN Models

  • Gradient Issues in RNN

  • Gradient Vanishing and LSTM

  • LSTM to GRU

  • Bidirectional LSTM

  • Bidirectional Deep LSTM

Chapter 12: Seq2Seq Models and Attention Mechanism

  • Seq2Seq Models

  • Greedy Decoding

  • Beam Search

  • Problems with Long Dependencies

  • Implementation of Attention Mechanism

Chapter 13: Dynamic Word Vectors and ELMo Technology

  • Contextual Word Vector Technology

  • Hierarchical Representation in Image Recognition

  • Hierarchical Representation in Text Domains

  • ELMo Model

  • Pre-training and Testing of ELMo

  • Advantages and Disadvantages of ELMo

Chapter 14: Self-Attention Mechanism and Transformer

  • Disadvantages of LSTM Models

  • Overview of Transformer

  • Understanding Self-Attention Mechanism

  • Encoding Positional Information

  • Understanding the Difference between Encoder and Decoder

  • Understanding Training and Prediction of Transformer

  • Disadvantages of Transformer

Chapter 15: BERT and ALBERT

  • Introduction to Autoencoders

  • Transformer Encoder

  • Masked Language Model

  • BERT Model

  • Different Training Methods for BERT

  • ALBERT

Chapter 16: Other Variants of BERT

  • RoBERTa Model

  • SpanBERT Model

  • FinBERT Model

  • Incorporating Prior Knowledge

  • K-BERT

  • KG-BERT

Chapter 17: GPT and XLNet

  • Review of Transformer Encoder

  • GPT-1, GPT-2, GPT-3

  • Disadvantages of ELMo

  • Considering Context under Language Models

  • Permutation LM

  • Dual-Stream Self-Attention Mechanism

Part Five: Information Extraction and Knowledge Graphs
Chapter 18: Named Entity Recognition and Entity Disambiguation

  • Applications and Key Technologies of Information Extraction

  • Named Entity Recognition

  • Common Techniques for NER Recognition

  • Entity Unification Techniques

  • Entity Disambiguation Techniques

  • Coreference Resolution

Chapter 19: Relation Extraction

  • Applications of Relation Extraction

  • Rule-Based Methods

  • Supervised Learning Methods

  • Bootstrap Methods

  • Distant Supervision Methods

Chapter 20: Syntactic Analysis

  • Applications of Syntactic Analysis

  • Introduction to CFG

  • From CFG to PCFG

  • Evaluating Parse Trees

  • Finding the Best Parse Tree

  • CKY Algorithm

Chapter 21: Dependency Grammar Parsing

  • From Syntactic Analysis to Dependency Grammar Parsing

  • Applications of Dependency Grammar Parsing

  • Dependency Grammar Parsing Based on Graph Algorithms

  • Transition-Based Dependency Grammar Parsing

  • Case Studies of Dependency Grammar

Chapter 22: Knowledge Graphs

  • Importance of Knowledge Graphs

  • Entities and Relationships in Knowledge Graphs

  • Unstructured Data and Constructing Knowledge Graphs

  • Designing Knowledge Graphs

  • Applications of Graph Algorithms

Part Six: Model Compression and Graph Neural Networks
Chapter 23: Model Compression

  • Importance of Model Compression

  • Overview of Common Model Compression Techniques

  • Matrix Factorization-Based Compression Techniques

  • Distillation-Based Compression Techniques

  • Bayesian Model-Based Compression Techniques

  • Model Quantization

Chapter 24: Graph-Based Learning

  • Graph Representation

  • Graphs and Knowledge Graphs

  • Common Algorithms on Graphs

  • Deepwalk and Node2vec

  • TransE Graph Embedding Algorithm

  • DSNE Graph Embedding Algorithm

Chapter 25: Graph Neural Networks

  • Review of Convolutional Neural Networks

  • Designing Convolution Operations in Graphs

  • Information Propagation in Graphs

  • Graph Convolutional Networks

  • Classic Applications of Graph Convolutional Networks

Chapter 26: GraphSage and GAT

  • From GCN to GraphSAGE

  • Regression of Attention Mechanism

  • Detailed Explanation of GAT Model

  • Comparison of GAT and GCN

  • Handling Heterogeneous Data

Chapter 27: Other Applications of Graph Neural Networks

  • Node Classification

  • Graph Classification

  • Link Prediction

  • Community Mining

  • Recommendation Systems

  • The Future Development of Graph Neural Networks

NLP Algorithm Engineer Training Program

Helping You Become theTOP 10% of Engineers

Students interested in the course

Scan the QR code for consultation

A Comprehensive Learning Path for NLP Algorithms

05 Who is the Course Suitable For?
College Students

  • Undergraduate/Master’s/Doctoral students in related science and engineering majors who wish to pursue NLP work after graduation

  • Those who want to delve into the AI field, preparing for research or studying abroad

  • Those who wish to systematically learn knowledge in the NLP field

Working Professionals

  • Currently working in IT-related jobs, wishing to work on NLP-related projects in the future

  • Currently working in AI-related jobs, hoping to keep up with the times and deepen their understanding of technology

  • Those who wish to keep up with cutting-edge technologies

A Comprehensive Learning Path for NLP Algorithms
06 Registration Notes
1. This session only recruits 50 people, and the remaining spots are limited.
2. Quality assurance! After the course officially starts, you can get a full refund without conditions within 7 days.
3. A certain foundation in machine learning is required to study this course.
NLP Algorithm Engineer Training Program

Helping You Become theTOP 10% of Engineers

Students interested in the course

Scan the QR code for consultation

A Comprehensive Learning Path for NLP Algorithms

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