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

张晓宇

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

 

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

专业户

To get started with deep learning computer vision (CV), you can follow these steps:

1. Learn the basics :

   • Master the Python programming language and basic mathematical knowledge, including linear algebra, calculus, and probability and statistics.

2. Learn the basics of computer vision :

   • Learn the basic concepts of computer vision, such as image processing, feature extraction, image classification, object detection, etc.

3. Learn the basics of deep learning :

   • Understand the basic principles of deep learning, including the structure of neural networks, forward propagation and back propagation, etc.

4. Choose the right learning resources :

   • Learn about deep learning computer vision by choosing the right online courses, textbooks, blog posts, video tutorials, etc.

5. Master the deep learning framework :

   • Learn and master commonly used deep learning frameworks, such as TensorFlow, PyTorch, etc., as well as related computer vision libraries.

6. Completed practical projects :

   • Consolidate what you have learned by completing actual computer vision projects. You can choose some classic tasks such as image classification and object detection.

7. Learn advanced techniques :

   • Learn some advanced deep learning computer vision techniques, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), etc.

8. Participate in open source communities and discussions :

   • Participate in open source communities and forums in the field of computer vision and deep learning to communicate and discuss with other learners and professionals.

9. Continuous learning and practice :

   • Continuous learning and practice are the key to improving your deep learning computer vision capabilities. With continuous practice and experience accumulation, your skills will continue to improve.

By following the above steps, you can gradually build up your understanding and skills in deep learning computer vision and continue to improve yourself in practice. I wish you good luck in your studies!

dashui

To learn more about deep learning applications in computer vision (CV), you can follow these steps:

1. Learn the basics: Before delving into deep learning, it is recommended that you first master the basics of computer vision, including basic concepts and algorithms in image processing, feature extraction, image recognition, etc.

2. Learn the basics of deep learning: Deep learning is widely used in the field of computer vision, so you need to learn the basics of deep learning first, including basic concepts and common models such as neural networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

3. Choose appropriate learning resources: Choose some high-quality deep learning computer vision courses, textbooks or teaching videos, such as the "Deep Learning Special Course" on Coursera, Stanford University's CS231n course, Andrew Ng's Deep Learning Special Course, etc., which can help you systematically learn the application of deep learning in the field of computer vision.

4. Hands-on projects: It is very important to practice in time during the learning process. You can choose some classic computer vision projects, such as image classification, object detection, semantic segmentation, etc., and use deep learning models to solve practical computer vision problems. This will help you consolidate what you have learned and improve your practical ability.

5. Read related papers: Deep learning is developing rapidly in the field of computer vision. Every year, a large number of research papers are published on cutting-edge technologies and methods in the field of computer vision. You can read some classic computer vision papers to learn about the latest research results and technological advances.

6. Participate in relevant communities and discussions: Join some online communities and forums for deep learning and computer vision, such as GitHub, Stack Overflow, Kaggle, etc., to exchange experiences, share problems and solutions with other peers, which can help you learn and improve better.

7. Continuous learning and practice: The field of deep learning and computer vision is developing very rapidly. You need to continue learning and practicing to keep up with the latest technologies and methods in order to achieve better results in this field.

The above are the general steps to get started with deep learning in computer vision. I hope it helps you. I wish you good luck with your studies!

Addddd

Understanding the application of deep learning in the field of Computer Vision (CV) is the first step to get started. Here are some suggestions:

1. Learn basic concepts: Understand the basic concepts of computer vision, including image processing, feature extraction, object detection, image classification, semantic segmentation, etc. You can acquire these basic knowledge by reading entry-level books or online tutorials.

2. Learn deep learning models: Understand the application of some commonly used deep learning models in the field of computer vision, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), etc.

3. Choose the right learning resources: Choose some introductory textbooks, online courses or teaching videos for deep learning computer vision for beginners. Some well-known online learning platforms such as Coursera, Udacity, edX, etc. provide a wealth of relevant courses. You can choose according to your interests and needs.

4. Learn deep learning tools and frameworks: Understand the application of some common deep learning tools and frameworks in the field of computer vision, such as TensorFlow, PyTorch, Keras, etc. These tools and frameworks provide rich computer vision models and algorithm implementations, and provide easy-to-use APIs and tool sets.

5. Hands-on projects: By practicing some computer vision projects, you can deepen your understanding of theoretical knowledge and improve your ability to solve practical problems. You can choose some simple computer vision projects to practice, such as image classification, object detection, face recognition, etc.

6. Participate in communities and forums: Join computer vision-related communities and forums to communicate and discuss with other learners and experts, share experiences and resources, and get feedback and suggestions. This will help you learn and grow faster.

By following the above steps, you can gradually build up your understanding and skills in deep learning in the field of computer vision and become a qualified computer vision engineer or researcher. I wish you progress in your studies!