I want to get started with AI deep learning, what should I do?

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I want to get started with AI deep learning, what should I do? [Copy link]

 

I want to get started with AI deep learning, what should I do?

层林尽染

Learning Artificial Intelligence (AI) and Deep Learning is a challenging but also very interesting task. Here are some steps to help you get started with Deep Learning:

1. Build a foundation in mathematics and statistics :

   • Understand basic linear algebra, calculus, probability and statistics. These knowledge are essential for understanding deep learning models and algorithms.

2. Learn programming skills :

   • Master a programming language, such as Python. Python is widely used in the field of deep learning, and there are rich deep learning libraries available, such as TensorFlow, PyTorch, etc.

3. Learn the basics of deep learning :

   • Learn the basic concepts and principles of deep learning, including neural networks, convolutional neural networks (CNNs), recurrent neural networks (RNNs), etc. Understand the structure and working principles of deep learning models.

4. Master deep learning tools and libraries :

   • Be familiar with some commonly used deep learning tools and libraries, such as TensorFlow, PyTorch, Keras, etc. These tools and libraries provide a wealth of deep learning models and algorithms, and provide convenient interfaces and functions to help you quickly implement and apply deep learning models.

5. Completed Deep Learning Project :

   • Participate in some deep learning projects and practice them. You can choose some classic deep learning problems or problems that interest you, collect data, select appropriate models and algorithms, and conduct experiments and evaluations. Through practical projects, you can better understand the applications and technologies of deep learning.

6. Take online courses and training :

   • Take some online deep learning courses and training, such as relevant courses on Coursera, edX, Udacity, etc. These courses are usually taught by senior deep learning experts and can help you systematically learn and master deep learning knowledge.

7. Read related books and papers :

   • Read some classic deep learning books and papers, such as "Deep Learning" (Ian Goodfellow, etc.), "Neural Networks and Deep Learning" (Michael Nielsen), etc. These books and papers can help you deeply understand the principles and methods of deep learning.

8. Participate in deep learning communities and forums :

   • Participate in deep learning communities and forums, such as GitHub, Kaggle, Stack Overflow, etc. On these platforms, you can exchange experiences with other deep learning enthusiasts, share learning resources, and get feedback and suggestions from the community.

Through the above steps, you can gradually master the basic knowledge and skills of deep learning and continuously improve your abilities in practice. I wish you a smooth study!

偷得浮生半日闲

You already have a basic understanding of AI and deep learning. Here are some suggestions to help you get started:

1. Strengthen programming basics :

   • Make sure you have a good foundation in programming, especially Python. The field of deep learning mainly uses Python programming, so proficiency in Python is essential.

2. Learn the basics of mathematics :

   • Deep learning involves a lot of mathematical knowledge, including linear algebra, calculus, probability theory, etc. It is recommended that you review these mathematical knowledge to better understand the principles of deep learning algorithms.

3. Dive into the theory of deep learning :

   • Learn the basic theories and principles of deep learning, including neural network structure, back propagation algorithm, optimizer, etc. You can learn this knowledge by reading classic deep learning textbooks or online courses.

4. Master the deep learning framework :

   • Be familiar with and master some commonly used deep learning frameworks, such as TensorFlow, PyTorch, etc. These frameworks provide rich APIs and tools to help you build and train deep learning models more easily.

5. Practical projects :

   • Practice what you have learned by participating in some deep learning projects, such as image classification, object detection, speech recognition, etc. Through practical projects, you can have a deeper understanding of deep learning algorithms and improve your practical application capabilities.

6. Participate in research and development :

   • If you are interested in in-depth research and development of deep learning algorithms, you can consider participating in related research projects or development work. By participating in research and development, you can be exposed to the latest deep learning technologies and methods and expand your technical horizons.

7. Continuous learning and follow-up :

   • The technologies and algorithms in the field of deep learning are changing with each passing day. You must keep learning, pay attention to the latest research results and technological advances, and participate in relevant conferences, seminars and training courses.

Through the above steps, you can gradually master the basic principles and skills of deep learning, become a qualified AI engineer, and apply deep learning technology in actual projects to solve practical problems.

caiyao

As an electronic engineer who wants to get started with AI deep learning, you can follow these steps:

1. Learn basic concepts: Understand the basic concepts of artificial intelligence and deep learning, including neural networks, deep learning models, forward propagation, backpropagation, etc.

2. Learn mathematics: Deep learning involves some mathematics, including linear algebra, probability statistics, and calculus. It is recommended to learn these basic mathematics first to lay the foundation for a deeper understanding of deep learning.

3. Learn programming languages and tools: Master programming languages (such as Python) and deep learning frameworks and tools (such as TensorFlow, PyTorch, etc.), so that you can better implement and apply deep learning models.

4. Learn deep learning algorithms and models: Understand common deep learning algorithms and models, such as convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory networks (LSTM), generative adversarial networks (GAN), etc., as well as their principles, advantages and disadvantages, and application scenarios.

5. Practical projects: Choose some simple deep learning projects for practice, such as image classification, object detection, speech recognition, etc. Through practical projects, you can have a deeper understanding of the working principles and application methods of deep learning.

6. Read relevant books and tutorials: There are some excellent books and tutorials that can help you learn deep learning systematically, such as "Deep Learning", "Neural Networks and Deep Learning", etc.

7. Participate in online courses and training: By participating in some online courses and training courses, you can systematically learn the theoretical knowledge and practical skills of deep learning, and communicate and learn with other learners.

8. Continuous learning and practice: Deep learning is a rapidly developing field. You need to continue to learn the latest research results and technological advances to continuously improve your abilities and level.

Through the above steps, you can gradually master the basic knowledge and skills of deep learning, and constantly improve yourself in practice to become an excellent AI engineer.