Fingerprint unlocking, facial recognition, speech-to-text, robots diagnosing diseases, Alphago······ We have profoundly felt that artificial intelligence is changing our work methods and perceptions.
According to the report on enterprise AI readiness by SAS, most companies believe that artificial intelligence is still in its early stages, “Currently, many application scenarios we are deploying contain 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 recognize this and start developing these skills early.
Choosing the Right Direction in AI is Crucial
Based on the investment data from various tracks in the AI field in 2017, the most investment events occurred in the computer vision sector, followed by natural language processing, intelligent robotics, and autonomous driving.
The significant investment in this field proves that computer vision is a direction with enormous growth potential.
So, how should one learn about this hot field of computer vision?
1. You can start by reading books. There are many books on computer vision that help you grasp the basic terminology and concepts, and you can practice hands-on with the code and cases provided in the books while you study.
2. Engage in deep 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 company, preferably focusing on a current project direction. During the practice, you can communicate with mentors and superiors at any time.
3. Systematic professional course learning. The courses mentioned here are not university major courses but rather a summary of key research problems, industry development trends, and practical cases in the field of computer vision condensed into essential content for concentrated teaching, allowing you to achieve 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 AI research and development experience, shares his insights:
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Key research problems in the field of computer vision, explained from basic to advanced, covering digital image storage, preprocessing, feature extraction, and achievements in the field of computer vision before the rise of deep learning;
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A comprehensive introduction to the fundamental theoretical knowledge of deep learning, including the basic principles of neural networks and key improvements to traditional neural networks;
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Focusing on the application of deep learning models in computer vision, specifically how convolutional neural networks, regional neural networks, fully convolutional networks, recurrent neural networks, long short-term memory units, and generative adversarial networks can 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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