If we’re just starting out in machine learning, we might be wondering where to begin. However, according to one of the greatest teachers of machine learning AI ever, it’s not so much about what we do, but how much we do it.
Focusing on Quantity over Quality: Why 10,000 Hours Matter
The idea of putting in 10,000 hours of work to become an expert comes from a concept called “deliberate practice.” This means that to become truly proficient in something, we need to engage in focused and intentional practice for an extended period of time. For machine learning, this means spending time every day working on the subject, regardless of what we’re working on.
We believe that beginners often get too caught up in what to do, rather than how much to do. We encourage beginners to spend 10,000 hours working on anything related to machine learning. It doesn’t matter if we’re working on a specific project or just practicing our coding skills. What matters is that we’re spending time every day on the subject.
Comparing Yourself to Your Past Self
Another piece of advice that we give is to avoid comparing ourselves to others. This is because it’s easy to become discouraged when we see others who are farther along in their machine learning journey than we are. Instead, we advise comparing ourselves to our past selves. Are we better at machine learning now than we were a year ago? If so, then we’re making progress.
The Importance of Scar Tissue
We also stress the importance of making mistakes. We believe that mistakes are not dead work, but rather a way to accumulate scar tissue. Scar tissue, in this context, refers to the knowledge we gain from our mistakes. By making mistakes, we learn what doesn’t work and what to avoid in the future. Over time, this scar tissue builds up and makes us stronger and more proficient in machine learning.
The Power of Teaching
We also discuss the importance of teaching in our own learning process. We believe that teaching strengthens our own understanding of machine learning and helps us identify gaps in our knowledge. By teaching others, we are also able to help others learn and become better at machine learning.
Finally, we discuss the difficulty of creating educational content. We explain that it takes a lot of iteration and thought to create something that has true educational value. However, we also note that going back to the basics and teaching others about concepts like backpropagation and loss functions can help strengthen our own understanding of these concepts.
In conclusion, if we’re just starting out in machine learning, the most important thing we can do is to put in the time and effort. Focus on spending 10,000 hours practicing and learning, and don’t worry too much about what we’re working on. By comparing ourselves to our past selves and embracing our mistakes, we’ll be well on our way to becoming an expert in machine learning.
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