Writing in the front

I have been in formal contact with AI for nearly two years. With continuous learning, experiments and model tuning, my understanding of AI has been constantly improved. I plan to write a series of introductory courses on artificial intelligence, which will help students who are interested in artificial intelligence.

I mainly focus on information retrieval. So I came up with the following three series.

Fundamentals of Python machine learning

The main goal of this part is to have a basic understanding of machine learning and be able to complete some simple regression tasks and classification tasks. And to image processing and natural language processing has a certain foundation.

Fundamentals of Python deep learning

The main objective of this section is to gain an understanding of basic multilayer perceptrons, convolutional neural networks, and cyclic neural networks, and to introduce the application of these networks to image processing and natural language processing.

Python intelligent search engine

This section will introduce the principles of search engines and introduce some methods of introducing deep learning into search engines.

Below is the table of contents, which should be updated gradually. The code will be placed at github.com/nladuo/pyth… In the water.

directory

Fundamentals of Python machine learning

  • 1. Introduction to artificial intelligence, machine learning and deep learning
  • 2. Python Scientific computing and Machine learning library
  • 3. Introduction to machine learning algorithm: KNN algorithm
  • 4. Image processing basics
  • Linear regression and optimization algorithms
  • Logistic regression algorithm
  • Fundamentals of natural language processing

Fundamentals of Python deep learning

  • 1. Perceptron and neural network
  • 2. Use TF.keras to realize handwritten character recognition
  • 3. Convolutional neural network
  • 4. Read SVHN paper and implement it
  • CNN and natural language processing
  • 6. Transfer learning
  • 7. Word vector: Word2Vec
  • 8. Recurrent neural network
  • 9. RNN and Natural Language Processing

Python intelligent search engine

  • 1. Boolean search
  • 2. Vector space model and TF-IDF
  • 3. Evaluation of information retrieval
  • 4. Probabilistic retrieval model
  • 5. Build the search engine using ElasticSearch
  • 6. Reordering in information retrieval: Sorting learning
  • 7. Text matching and retrieval
  • Vector search engines
  • 9. Image retrieval