Perception and Perception (Level 6)

learning and memory

concepts and thinking

language and communication

cognitive control and executive functions

Programming skills: Master at least one mainstream programming language, such as Python, Java, C, etc., be familiar with data structures and algorithms, and be able to implement them in code.

Basic mathematics: including basic mathematical knowledge such as linear algebra, probability theory, statistics, calculus, etc. This knowledge has applications in fields such as machine learning and deep learning.

Machine learning algorithms: Master common machine learning algorithms, such as regression, decision trees, support vector machines, random forests, etc., as well as their implementation methods and optimization techniques.

Deep learning algorithms: Master deep learning algorithms, such as convolutional neural networks, recurrent neural networks, attention mechanisms, etc., as well as their implementation methods and optimization techniques.

Data processing: Familiar with data processing technologies such as data preprocessing, data cleaning, and data conversion, and be able to perform corresponding data processing for different data types and formats.

Computer Vision: Understand the basic principles and methods of computer vision, such as image processing, feature extraction, target detection, semantic segmentation, etc.

Natural language processing: Understand the basic principles and methods of natural language processing, such as text processing, word vector representation, sentiment analysis, machine translation, etc.

Reinforcement learning: Understand the basic principles and methods of reinforcement learning, such as Markov decision process, Q-learning, policy gradient, etc.

Model deployment and tuning: Understand the basic principles and methods of model deployment and tuning, including model compression, model acceleration, model optimization, etc.

Programming skills: Have an in-depth mastery of at least one mainstream programming language, such as Python, Java, C, etc., be familiar with high-performance computing and distributed computing technology, and be able to implement complex algorithms and models in code.

Mathematics basics: In-depth mastery of basic mathematical knowledge such as linear algebra, probability theory, statistics, calculus, etc., and the ability to use advanced mathematical knowledge to solve practical problems.

Machine learning algorithms: In-depth mastery of various machine learning algorithms, including traditional machine learning algorithms such as support vector machines, decision trees, ensemble learning, etc. and deep learning algorithms such as convolutional neural networks, recurrent neural networks, attention mechanisms, etc., and be able to use Select and tune appropriate algorithms for different task requirements.

Reinforcement learning algorithms: In-depth mastery of the theory and practice of reinforcement learning algorithms, including methods based on value functions, methods based on policy gradients, methods based on evolutionary algorithms, etc., as well as their applications in actual scenarios.

Unsupervised learning algorithms: Have an in-depth understanding of various unsupervised learning algorithms, including clustering, dimensionality reduction, generative models, etc., be able to select appropriate unsupervised learning algorithms based on different data types and scenarios, and be able to conduct effective data exploration and analysis.

Natural language processing: Have an in-depth grasp of the basic theories and methods of natural language processing, including text processing, language models, sequence-to-sequence models, semantic understanding, etc., and be able to apply natural language processing technology to solve practical problems, such as intelligent customer service, intelligent translation, automatic Abstract etc.

Computer Vision: Deeply master the basic theories and methods of computer vision, including image processing, feature extraction, target detection, image segmentation, etc., and be able to apply computer vision technology to solve practical problems, such as face recognition, object recognition, image generation, etc.

Autonomous driving: Deeply master the basic theories and technologies of autonomous driving, including sensor fusion, environment perception, path planning, etc., and be able to apply autonomous driving technology to solve practical problems, such as the development and implementation of autonomous vehicles

Big data technology: Master big data technology, including data storage, data mining, distributed computing, etc., and be able to process large-scale data sets and extract useful information.

High-performance computing technology: Understand high-performance computing technology, including GPU acceleration, distributed computing, cloud computing, etc., and be able to use these technologies to improve the computing speed and efficiency of algorithms.

Software Engineering: Be familiar with the basic theories and methods of software engineering, including code management, version control, testing and debugging, etc., and be able to write high-quality, maintainable code and conduct effective testing and debugging.

Management and leadership: Have management and leadership skills, be able to take the lead in the team, effectively organize and coordinate the work of team members, and promote the progress and realization of the project

Including linear algebra, calculus, probability theory, statistics, discrete mathematics, algorithms and data structures, etc.

Such as Python, C, Java, MATLAB, R, etc., as well as related development tools and libraries, such as TensorFlow, PyTorch, Keras, scikit-learn, NumPy, etc.

Including supervised learning, unsupervised learning, reinforcement learning, neural network, convolutional neural network, recurrent neural network, autoencoder, etc.

Such as logistic regression, support vector machine, random forest, deep neural network, convolutional neural network, etc., and master how to design, train and optimize these models

Including data cleaning, feature extraction, data visualization, data analysis and data mining, etc., master common data processing and analysis tools and libraries, such as Pandas, NumPy, Scikit-learn, etc.

Be familiar with the application of deep learning in computer vision, natural language processing, speech recognition and other fields, understand commonly used deep learning models and algorithms, such as target detection, image segmentation, text classification, speech recognition, etc., and master how to apply these models to solve practical problems

Understand commonly used optimization algorithms and acceleration technologies, such as gradient descent algorithms, adaptive learning rate algorithms, batch normalization, convolution calculations, etc., and how to improve the efficiency and accuracy of deep learning algorithms in practical applications

Master how to apply AI technology to actual scenarios, how to evaluate and optimize model performance, how to collaborate with team members to solve problems, etc.

Fisher's formula (MV=PQ)

Valuation method

Financial Report Analysis Methods