MindMap Gallery Machine learning algorithm engineer
Machine learning algorithm engineer, including a summary of basic concepts and classifications of machine learning, classic machine learning models, deep learning models, business and applications, engineering capabilities, mathematical foundations, etc.
Edited at 2023-03-12 11:16:18Avatar 3 centers on the Sully family, showcasing the internal rift caused by the sacrifice of their eldest son, and their alliance with other tribes on Pandora against the external conflict of the Ashbringers, who adhere to the philosophy of fire and are allied with humans. It explores the grand themes of family, faith, and survival.
This article discusses the Easter eggs and homages in Zootopia 2 that you may have discovered. The main content includes: character and archetype Easter eggs, cinematic universe crossover Easter eggs, animal ecology and behavior references, symbol and metaphor Easter eggs, social satire and brand allusions, and emotional storylines and sequel foreshadowing.
[Zootopia Character Relationship Chart] The idealistic rabbit police officer Judy and the cynical fox conman Nick form a charmingly contrasting duo, rising from street hustlers to become Zootopia police officers!
Avatar 3 centers on the Sully family, showcasing the internal rift caused by the sacrifice of their eldest son, and their alliance with other tribes on Pandora against the external conflict of the Ashbringers, who adhere to the philosophy of fire and are allied with humans. It explores the grand themes of family, faith, and survival.
This article discusses the Easter eggs and homages in Zootopia 2 that you may have discovered. The main content includes: character and archetype Easter eggs, cinematic universe crossover Easter eggs, animal ecology and behavior references, symbol and metaphor Easter eggs, social satire and brand allusions, and emotional storylines and sequel foreshadowing.
[Zootopia Character Relationship Chart] The idealistic rabbit police officer Judy and the cynical fox conman Nick form a charmingly contrasting duo, rising from street hustlers to become Zootopia police officers!
Machine learning algorithm engineer
Feature Engineering (Chapter 1)
Feature discretization and normalization
Feature combination
Feature selection
word embedding representation
Model Evaluation (Chapter 2)
Evaluation index
A/B testing
Overfitting and underfitting
Hyperparameter selection
Optimization Algorithms (Chapter 7)
loss function
Regularization
EM algorithm
gradient descent
stochastic gradient descent
Backpropagation (Chapter 9, Section 3)
Gradient verification
Momentum
AdaGrad
Adam
Mathematical basis
probability theory
Commonly used probability distributions
Theorem of large numbers and central limit theorem
hypothetical test
Bayesian theory
linear algebra
calculus
Convex optimization
information theory
Engineering capabilities
Data Structures and Algorithms
Trees and Related Algorithms
Graphs and Related Algorithms
Hash table
Matrix operations and optimization
big data processing
MapRuduce
Spark
HiveQL
Storm
Machine learning platform
TensorFlow
Torch
Theano
parallel computing
Databases and data warehouses
System service architecture
Business and Application
computer vision
natural language processing
Recommended system
Calculated advertising
smart games
deep learning model
Forward Neural Networks (Chapter 9)
multilayer perceptron
convolutional neural network
Deep Residual Network
Self-organizing map neural network (Chapter 5, Section 3)
Restricted Boltzmann Machine
Recurrent Neural Networks (Chapter 10)
recurrent neural network
long short term memory model
attention mechanism
Seq2Seq
Deep learning optimization techniques
batch normalization
Dropout
activation function
Sigmoid
Softmax
Tanh
ReLU
Reinforcement Learning (Chapter 11)
Generative Adversarial Networks (Chapter 13)
Classic machine learning model
Supervised learning model
Classical Algorithms (Chapter 3)
Support Vector Machines
logistic regression
decision tree
Probabilistic graphical models (Chapter 6)
Naive Bayes
maximum entropy model
Hidden Markov Model
conditional random field
Unsupervised Learning (Chapter 5)
hierarchical clustering
K-means clustering
Gaussian Mixture Model
Topic Model (Chapter 6, Section 5)
Ensemble Learning (Chapter 12)
Bagging
Boosting
random forest
GBDT
Dimensionality Reduction Algorithms (Chapter 4)
Sampling (Chapter 8)
Reinforcement Learning (Chapter 11)
Basic concepts and classification of machine learning
basic concept
hypothesis space
training/test data
mark
loss function
Sort by data
Classification
return
sequence annotation
Classification by supervision
supervised learning
unsupervised learning
reinforcement learning
Classified by model
generative model
discriminant model