I want to get started with neural networks from scratch, what should I do?

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I want to get started with neural networks from scratch, what should I do? [Copy link]

 

I want to get started with neural networks from scratch, what should I do?

chenxin

For beginners of neural networks, here are some suggested steps:

1. Understand the basic concepts :

   • Before you start learning neural networks, you should first understand some basic concepts, such as neurons, weights, biases, activation functions, etc. These concepts are the basis for understanding the principles of neural networks.

2. Learn basic math knowledge :

   • Neural networks involve some math, especially linear algebra and calculus. Knowing this math will help you understand how neural networks work.

3. Learn the basics of programming :

   • Choose a programming language and learn its basics. Python is one of the commonly used programming languages in the field of deep learning. Learning Python's basic syntax and common libraries (such as NumPy, Pandas, etc.) will help you understand the implementation of neural networks.

4. Read the textbook or online resources :

   • Find some introductory textbooks or online resources, such as Neural Networks and Deep Learning (Michael Nielsen), Coursera's Neural Networks and Deep Learning course, etc. Through these resources, you can understand the basic principles and implementation methods of neural networks.

5. Hands :

   • Practice what you have learned by writing code. You can start by implementing the simplest neural network, such as a perceptron, and then gradually expand to more complex neural network structures.

6. Using Deep Learning Frameworks :

   • Master a popular deep learning framework, such as TensorFlow or PyTorch. These frameworks provide rich tools and interfaces that can help you build and train neural network models more easily.

7. Join an online course or community :

   • Take some online neural network courses, such as those offered by Coursera, edX, Udacity, etc., or join some neural network-related communities to exchange experiences with other learners.

8. Keep practicing and practicing :

   • Learning neural networks is a gradual process that requires constant practice and experimentation. Try using different datasets and model architectures, and constantly optimize your models to improve your skills.

I hope the above suggestions can help you get started in the field of neural networks! Good luck with your studies!

Morpho

You can follow these steps to get started with neural networks:

1. Understand basic concepts: Before you begin, understand the basic concepts of neural networks, including neurons, weights, biases, activation functions, forward propagation, back propagation, etc.

2. Learn the basics of mathematics: Neural networks involve a lot of mathematical knowledge, including linear algebra, calculus, etc. It is recommended that you first learn the basics of these mathematical knowledge in order to better understand and apply neural network algorithms.

3. Choose learning resources: Choose a neural network textbook, course, or online resource suitable for zero-based learning. Some well-known online learning platforms, such as Coursera, edX, and Udacity, provide a wealth of neural network courses. You can choose a suitable course based on your interests and learning style.

4. Learn programming skills: Mastering a programming language, such as Python, is an important prerequisite for learning neural networks. Python has simple and easy-to-learn syntax and rich machine learning libraries, such as NumPy and Pandas, which are suitable for beginners.

5. Understand the basic neural network structure: Learn to understand the basic neural network structure, such as single-layer perceptron, multi-layer perceptron (MLP), convolutional neural network (CNN), recurrent neural network (RNN), etc., as well as their application scenarios and characteristics.

6. Practical project exercises: Reinforce your knowledge through practical projects, such as using public datasets for tasks such as handwritten digit recognition, image classification, text classification, or participating in neural network competitions or challenges.

7. Continuous learning and practice: Neural networks are a field that is constantly evolving and progressing, so continuous learning and practice are very important. Follow the latest developments and research results in the industry and continuously improve your skills and abilities.

Through the above steps, you can gradually master the basic knowledge and skills of neural networks, start from scratch and gradually deepen your learning and practice. I wish you a smooth study!

数字化

Understanding the introductory path to neural networks from scratch is a good start. Here are some suggestions:

1. Understand basic concepts: Before you begin, it is very important to understand the basic concepts of neural networks. Learn the basics about neurons, weights, biases, activation functions, etc.

2. Learn the math basics: Neural networks involve a lot of math concepts, especially linear algebra and calculus. If you don’t have the background, you can learn these math basics through online courses, textbooks, or videos.

3. Choose a programming language: Neural networks are usually implemented using programming languages such as Python, R, etc. Choose a popular programming language and master its basics.

4. Learn the basics of programming: If you are not familiar with programming, it is recommended that you first learn the basics of programming, including variables, data types, conditional statements, loop statements, etc.

5. Master the working principles of neural networks: Learn the working principles of neural networks, including the forward propagation and backpropagation processes, as well as common neural network architectures such as multi-layer perceptrons, convolutional neural networks, and recurrent neural networks.

6. Learn deep learning tools and libraries: Learn to use common tools and libraries for deep learning, such as TensorFlow, PyTorch, etc. These tools and libraries provide a wealth of neural network models and algorithms that can help you quickly implement your own projects.

7. Complete an introductory course: Take some introductory neural network courses or tutorials for beginners. These courses usually introduce the basic concepts of neural networks, common models, and practical techniques to help you build the foundation.

8. Read relevant books and documents: Books and documents about neural networks will provide more systematic and in-depth theoretical knowledge and practical experience, which can help you better understand the principles and applications of neural networks.

9. Practice projects: The most important thing is to deepen your understanding and mastery of neural networks through practice. Try to complete some simple neural network projects, and constantly debug and optimize them to accumulate experience and skills.

10. Participate in communities and discussions: Join communities and forums related to neural networks to exchange experiences, share problems and solutions with other learners and experts, and obtain more learning resources and support.

By following the steps above, you can get started with neural networks from scratch and gradually build up your skills and experience. Good luck with your studies!