This article will take you into the wonderful world of deep learning, and discuss the core principles, application examples, and future trends of this cutting-edge technology. Through the in-depth analysis of the content of this article, we hope to guide readers to understand how deep learning is changing the field of data analysis and machine learning, and I hope it will be helpful to you.
In 2016, Google's DeepMind company developed AlphaGo, which shocked many by defeating Go world champion Lee Sedol and later Ke Jie, who was ranked No. 1 in the world at the time, and ignited everyone's enthusiasm for artificial intelligence.
In 2022, OpenAI released ChatGPT, and since then, various large language models have flocked to the market, and generative AI has flourished.
Looking back at the history of the development of human society, once a major breakthrough in science and technology is achieved, people's lives will undergo earth-shaking changes.
Behind applications like AlphaGo and ChatGPT, there's one key technology behind it, and that's deep learning.
Here is the 75th of 100 analytical thinking models: deep learning, which can help us better understand the underlying laws of how the world works.
1. Why learn deep learning?
Deep learning is a cutting-edge technology in the field of artificial intelligence, which has been integrated into our daily work, life and learning.
Today, people in all walks of life who are using deep learning have significantly improved their productivity.
At the same time, a large number of simple and repetitive tasks will be replaced by machines, and many people will be at risk of losing their jobs. With the rapid development of deep learning technology, almost all activities engaged in by humans in the future may be affected by it, including science, medicine, manufacturing, energy, transportation, agriculture, art, etc.
Maybe one day, a deep learning app will become your right-hand man, or even your close friend, it may know you better than anyone else, be able to answer your questions, help you educate your children, and take care of your health.
2. What is deep learning?
Deep learning is a branch of machine learning, and ChatGPT is one of the applications of deep learning.
The origin of deep learning can be traced back to 1943, when neurophysiologist Warren McCulloch collaborated with mathematician Walter Pitts to publish a paper entitled "Logical Calculation of the Inner Mind of Neural Activity", in which a neuronal model was proposed to implement a simple circuit to simulate the behavior of neurons in the brain, proving that the operation mechanism of neural networks could be described in the way of logical calculus, laying the foundation for deep learning.
The essence of learning is to improve performance. The famous scholar Herbert Simon said: "If a system can improve its performance by executing a process, then the process is learning." ”
Deep learning is the complete automation of feature engineering by transmitting information through the connection of "neural networks", thus making problem solving more efficient and simpler.
The rise of the internet has made it simple and feasible to collect and distribute data. If deep learning is the steam engine of the industrial revolution, then data, like coal and oil, drives the rapid development of deep learning.
The process of deep learning conforms to the law of the DIKW model, that is, extracting useful information from data, accumulating information into knowledge, and knowledge evolving into wisdom.
Deep learning is based on mathematics, computer science, and neuroscience, learning from data. As the learning level progresses, the machine will become more and more intelligent.
3. How to use deep learning?
Deep learning has a wide range of application scenarios, including image classification, speech recognition, machine translation, text-to-speech conversion, digital assistants, data searching, assisted programming, assisted writing, assisted office, art creation, health diagnosis, autonomous driving, marketing campaigns, playing Go, playing games, answering questions in natural language......
As computers process faster and more data, any business or even individual can use deep learning to improve themselves.
For example, with apps like ChatGPT, everyone can do deep learning by chatting with a machine.
However, learning and applying deep learning is a long journey, and there is so much to learn that one book can't cover it all, and there are countless books on deep learning on the market, with a rough estimate of no fewer than 100, and the number is growing.
If you are not a practitioner in the field of artificial intelligence, then you don't need to delve into the code and formula, you just need to combine your actual situation and learn and apply relevant knowledge in a targeted manner, because a person's time, energy and ability are limited.
I pay more attention to the use of deep learning in personal growth, and I hope that I can use deep learning to analyze the data recorded in the past and the written materials, so that it can become my right-hand man, and then understand myself better and become a better version of myself.
There are three main steps to using deep learning: defining the task, developing the model, and deploying the model.
(1) Define the task
Learning usually starts with clarifying goals, defining tasks, and asking questions, understanding the business logic behind the questions, and starting to collect data. For example, how can data be used to empower growth, what data is currently available, and is there a need to collect more data?
(2) Develop a model
Once you have your data ready, you can try to select a suitable model, then tune and test it to continuously improve the performance of the model.
(3) Deploy the model
Deploy the optimized model to the target environment, monitor the performance of the model in the real environment, and continue to collect more data to form a closed loop of deep learning.
The steps of deep learning are somewhat similar to the process of "data analysis", starting from collecting data, improving data, then analyzing data and solving problems, and then collecting more data in turn to form an augmentation loop to solve practical problems with data.
IV. Final Words
In the future, it is possible that each of us will use deep learning, which is as widespread as today's Internet technology, which will greatly liberate human productivity and significantly improve people's cognitive ability. Although deep learning has a wide range of applications, it is important to note that deep learning is not a panacea key, and some problems are not suitable for deep learning methods, either because there is not enough data or because there are other better methods.
When you only have a hammer in your hand, everything looks like a nail. But if you have a hammer, a screwdriver, a screwdriver, a wrench, etc., then you can choose the right tool according to the situation, so that the problem will be more efficient.
Therefore, we need to embrace the concept of lifelong learning, learn more analytical thinking models, and apply them in real work and life, so as to open up a better future.
Official account: Lin Ji, author of "Data Analysis".
This article was originally published by @林骥 on Everyone is a Product Manager. Reproduction without the permission of the author is prohibited.
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