Fingerprint unlocking, facial recognition, speech-to-text conversion, robots diagnosing illnesses, AlphaGo······ We have deeply felt that artificial intelligence is changing the way we work and perceive the world.
According to a report by SAS on the readiness of enterprises for artificial intelligence, most companies believe that AI is still in its early stages, “Currently, many application scenarios we are deploying include AI components.” It is evident that we must learn new skills to keep pace with the development of AI, and the future belongs to those who realize this and start developing these skills early.
Choosing the Right Direction in the AI Field Is Important
Looking at the investment data in the AI field for 2017, the most investment events occurred in the computer vision sector, followed by natural language processing, intelligent robotics, and autonomous driving.
The ability to secure such significant investment proves that computer vision is a field with immense potential for development.
So, how should one learn this hot topic of computer vision?
1. You can start by reading books. There are many books on computer vision that will help you grasp the basic terminology and concepts of computer vision, and you can also practice hands-on with the code and examples provided in the books as you read.
2. Engage in in-depth practice. This requires you to have a certain level of knowledge in computer vision. You can choose to work on actual projects in a lab or a company, preferably focusing deeply on the current project direction. During the practice, you can communicate with mentors and superiors at any time.
3. Take systematic and professional courses. The courses mentioned here are not university major courses but rather a compilation of key research issues, industry development trends, and practical cases in the field of computer vision, condensed into essential content for concentrated teaching that allows you to make a qualitative leap.
I recommend the course from Little Elephant Academy:
“Deep Learning Practice in Computer Vision”
Professor Ye Zi from Shanghai Jiao Tong University, with over 10 years of experience in AI research and development, shares his knowledge:
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Key research issues in the field of computer vision, explained from basic to advanced, covering topics such as digital image storage, preprocessing, feature extraction, and the achievements made in the field of computer vision before the rise of deep learning;
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Introduction to the fundamental theories of deep learning, including the basic principles of neural networks and the key improvements deep learning brings to traditional neural networks;
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Focus on the application of deep learning models in the field of computer vision, specifically how convolutional neural networks, region neural networks, fully convolutional networks, recurrent neural networks, long short-term memory units, and generative adversarial networks are used to solve challenges in image applications;
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The course will use languages such as Python and deep learning frameworks like TensorFlow and Keras for practical case teaching;
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