Original link: https://www.mbcao.com/ai/
When becoming a top artificial intelligence algorithm development engineer, you need to learn deeply and master knowledge in multiple fields. The following is a detailed knowledge map, covering content from basic knowledge to top knowledge. Note that this is just an overview, you can do in-depth study and research according to your own interests and needs. Fundamentals of Programming Fundamentals: Choose a programming language such as Python or Java. Learn basic concepts such as language syntax, data types, conditional statements, and loop structures. Understand the ideas of object-oriented programming and functional programming, and be able to apply them to practical programming tasks. Familiarity with common programming tools and development environments, such as IDEs (Integrated Development Environments) and version control systems (such as Git). Mathematical Foundations: Learn the basic concepts of linear algebra, such as vectors, matrices, linear equations, etc. Learn about key concepts such as matrix operations, eigenvalues, and eigenvectors. Master the basics of probability theory and statistics, including probability distribution, expectation, variance, hypothesis testing, etc. Learn how to apply statistical methods to analyze data. Learn the basic concepts of calculus, including derivatives, integrals, and differential equations. Understand the principles and applications of optimization algorithms. Machine Learning Fundamentals: Understand the basic concepts of machine learning, including supervised, unsupervised, and semi-supervised learning. Learn about common learning tasks such as classification, regression, and clustering. Learn common machine learning algorithms such as linear regression, logistic regression, decision trees, support vector machines, and more. Understand their principles, advantages and disadvantages, and applicable scenarios. Deep Learning Fundamentals: Understand the basic structure and working principles of neural networks, including forward propagation and back propagation. Learn the network structures commonly used in deep learning, such as convolutional neural network (CNN), recurrent neural network (RNN) and transformer (Transformer). Learn about their application in different tasks. Intermediate Knowledge Dive into Machine Learning and Deep Learning: Learn more advanced machine learning algorithms such as support vector machines, random forests, ensemble learning, and more. Learn about their principles, optimization methods and tuning techniques. Master advanced concepts of deep learning, such as regularization, batch normalization, dropout, etc. Learn how to deal with overfitting and underfitting. Learn deep learning frameworks such as TensorFlow, PyTorch, etc. Understand their basic usage and common APIs. Natural Language Processing (NLP): Understand the basic tasks of natural language processing, such as text classification, named entity recognition, machine translation, etc. Learn about natural language representations and language models. Learn common NLP models, such as word embedding, recurrent neural network (RNN), long short-term memory network (LSTM), attention mechanism, etc. Learn about their application in different tasks. Computer Vision (CV): Understand the basic tasks of computer vision, such as image classification, object detection, image segmentation, etc. Learn about image representation and feature extraction methods. Learn common CV models, such as convolutional neural network (CNN), regional convolutional neural network (R-CNN), YOLO, etc. Learn about their application in different tasks. Reinforcement Learning: Understand the basic concepts of reinforcement learning, such as Markov decision process (MDP), value function, policy gradient, etc. Understand the basic pipeline of reinforcement learning. Learn reinforcement learning algorithms such as Q-learning, deep reinforcement learning, policy gradient methods, etc. Learn how to apply reinforcement learning to solve different problems. Advanced Knowledge Deep Reinforcement Learning: Learn advanced algorithms of deep reinforcement learning, such as Deep Q Network (DQN), Monte Carlo Tree Search (MCTS), inverse reinforcement learning, etc. Understand their principles and application scenarios. Master techniques for dealing with continuous action spaces and partially observable Markov decision processes (POMDPs), such as deterministic policy gradients, deep decision networks, etc. Generative Models: Learn the basic concepts of generative models, such as variational autoencoders (VAEs), generative adversarial networks (GANs), and more. Learn how they work and how to generate samples. Master the application of generative models in the fields of image generation, language generation, etc., such as image generation, text generation, video generation, etc. Transfer learning and multi-task learning: Study the theory and method of transfer learning and multi-task learning to solve the problem of data shortage and correlation between tasks. Learn about techniques such as domain adaptation and parameter sharing. Explore how to share knowledge and models in different domains and tasks to improve the generalization ability and effect of the model. Explainable AI and Fairness: Learn about the importance and challenges of explainable AI and fairness. Research methods and evaluation metrics for explainable AI and fair AI. Explore how to make machine learning and deep learning models more interpretable, such as feature importance analysis, visualization, and more. Understand the definition and measurement of fairness, and study the design and implementation of fair AI. High Performance Computing and Distributed Training: Learn how to optimize the performance and training speed of deep learning models. Learn about techniques such as parallel computing, model compression, and quantization. Master technologies such as distributed training and GPU acceleration, such as distributed frameworks (such as TensorFlow distributed) and GPU programming (such as CUDA). This advanced knowledge map covers the learning path from basic knowledge to advanced knowledge. Please note that this is only a rough guide, and you can further study and explore according to your personal interests and goals. In the process of becoming a top artificial intelligence algorithm development engineer, it is important to practice, conduct in-depth research, and communicate and cooperate with experts in other fields. I wish you great success in the field of artificial intelligence! Note that this article was generated by ChatGPT, not by this blog. × close alert
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