MindMap Gallery Artificial Intelligence Learning Outline
Employment in the field of artificial intelligence, recommended learning sequence and content outline
Edited at 2020-03-25 09:31:02This is a mind map about bacteria, and its main contents include: overview, morphology, types, structure, reproduction, distribution, application, and expansion. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about plant asexual reproduction, and its main contents include: concept, spore reproduction, vegetative reproduction, tissue culture, and buds. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about the reproductive development of animals, and its main contents include: insects, frogs, birds, sexual reproduction, and asexual reproduction. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about bacteria, and its main contents include: overview, morphology, types, structure, reproduction, distribution, application, and expansion. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about plant asexual reproduction, and its main contents include: concept, spore reproduction, vegetative reproduction, tissue culture, and buds. The summary is comprehensive and meticulous, suitable as review materials.
This is a mind map about the reproductive development of animals, and its main contents include: insects, frogs, birds, sexual reproduction, and asexual reproduction. The summary is comprehensive and meticulous, suitable as review materials.
Artificial Intelligence Learning Outline
basic mathematical knowledge
Must know advanced mathematics
constant e
Derivative
gradient
Taylor
gini coefficient
Information entropy and combination number
gradient descent
Newton's method
linear algebra
Linear spaces and linear transformations
Basic concepts of matrices
state transition matrix
Feature vector
Correlated multiplication of matrices
QR decomposition of matrix
Symmetric matrix, orthogonal matrix, positive definite matrix
SVD decomposition of matrices
Derivatives of matrices
Matrix mapping/projection
probability theory
Calculus and Approximation Theory
Basic concepts of limit, differential and integral
Use the idea of approximation to understand differential calculus, and use integral methods to understand probability.
Basics of Probability Theory
classical model
Common probability distributions
Theorem of large numbers and central limit theorem
Covariance (matrix) and correlation coefficient
Maximum likelihood estimation and maximum a posteriori estimation
Convex optimization
Basic concepts of convex optimization
Convex set
convex function
Standard form of convex optimization problem
Lagerange dualization of convex optimization
Programming phthon practice
Python introductory and practical courses
Quick Start with Python
Scientific computing library Numpy
Data analysis and processing library Numpy
Visualization library Matplotlib
Easier visualization with Seaborn
Python project
machine learning
Basic introduction
Algorithm explanation
linear regression algorithm
Gradient descent principle
Logistic regression algorithm
Case practice: Python implements logistic regression
Case study: Comparing different gradient descent strategies
Case study: Python analysis of Kobe Bryant’s career data
Case Study: Credit Card Fraud Detection
Decision tree construction principle
Case practice: Decision tree construction example
Random forest and ensemble algorithm
Case study: Titanic rescue prediction
Bayesian algorithm derivation
Case practice: news classification task
Kmeans clustering and visual display
DBSCAN clustering and its visual display
Case practice: clustering practice
Dimensionality Reduction Algorithm: Linear Discriminant Analysis
Case practice: Python implements linear discriminant analysis
Dimensionality reduction algorithm: PCA principal component analysis
Case practice: Python implements PCA algorithm
Advanced promotion
Project walkthrough
Derivation of EM algorithm principle
GMM clustering practice
Recommended system
Case study: Python practical recommendation system
Support vector machine principle derivation
Case practice: SVM instance
Time Series ARIMA Model
Case practice: time series prediction task
Xgbooost boosting algorithm
Case practice: Xgboost parameter adjustment practice
Computer Vision Challenge
Necessary basics for neural networks
Overall architecture of neural network
Case study: CIDAR image classification task
language model
Natural language processing-word2vec
Artificial Intelligence Comprehensive Project Practice
Voice recognition, face recognition
E-commerce website data mining and recommendation algorithm
Only investment consultant on financial P2P platform
Autonomous driving technology
Disease diagnosis and monitoring in the medical industry
Intelligent learning system for education industry
deep learning
Basic introduction
Computer Vision-Convolutional Neural Network
Third generation object detection framework
Basic principles of convolutional neural networks
Detailed explanation of convolution parameters
Case practice CNN network
Network model training skills
Classic network architecture and object detection tasks
Basic operations of deep learning framework Tensorflow
Tensorflow framework constructs regression model
Tensorflow neural network model
Tensorflow builds CNN network
Tensorflow builds RNN network
Tensorflow loads the trained model
Deep learning project practice-verification code recognition
Deep learning framework Caffe network configuration
Caffe production data source
Caffe framework tips
Common tools of Caffe framework
Deep learning project walkthrough
Project walkthrough: Face detection data source production and network training (based on Caffe)
Project walkthrough: Implementing face detection (based on Caffe)
Project walkthrough: first stage network training for key point detection (based on Caffe)
Project walkthrough: second stage model training for key point detection (based on Caffe)
Project Walkthrough: Adversarial Generative Network (based on Tensorflow)
Project walkthrough: LSTM sentiment analysis (based on Tensorflow)
Project walkthrough: Robot writes Tang poetry (based on Tensorflow)
Project walkthrough: text classification task interpretation and environment configuration
Project walkthrough: text classification practice (based on Tensorflow)
Project walkthrough: Reinforcement learning basics (based on Tensorflow)
Project walkthrough: DQN lets AI play games by itself (based on Tensorflow)
study materials
Teacher Ng Enda’s course http://zh.coursera.org/learn/machine-learning
"Machine Learning in Practice"
"Machine Learning"
"Statistical Learning Methods"
Liao Xuefeng's courses
"Fluent Python"
"Using Python for Data Analysis"
"Deep Learning"
Employment prospects and trends
The current development status of artificial intelligence is in the growth stage. Due to the relatively small number of relevant talents, the artificial intelligence talent market is vacant and demand exceeds supply. In addition, the country has issued relevant policies to promote the development of artificial intelligence; some provinces also attach great importance to artificial intelligence. development, so the employment prospects for artificial intelligence majors are still bright.