1. Practice, practice, practice — Gather large volumes of data.

“Practice isn’t the thing you do once you’re good. It’s the thing you do that makes you good.” — Malcolm Gladwell

This one is pretty simple. The more you practice, the better you’ll get.

We all have heard the buzzwords Big data. But few know what this truly means. According to Oxford Dictionaries, Big Data is: Extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.

Big data is having a massive amount of examples for a model to train.

According to an Oxford study on Neural Mechanisms of Skill Learning, when we start learning something new, there’s a lot of neural activity involved. But as we practice the task more and more, the energy required to perform the same task decreases. The more we practice our skills, the less effort it takes for our brain to complete the task.

When building a Machine Learning model, this is one of the core things that’s going to determine your algorithm’s performance. You need many examples in order to achieve good models. Imagine showing the alien just a couple of pictures versus showing him thousands. He’ll learn better if you show him a greater amount of examples.

“But invariably, simple models and a lot of data trump more elaborate models based on less data.” — Alon Halevy, Peter Norvig, and Fernando Pereira, Google. The unreasonable effectiveness of data.

People tend to hesitate a lot before actually starting to practice. We spend too many hours reading about the topic, watching videos, asking people for advice, etc. Even though these activities are useful, we often get stuck.

The only thing that’s going to make you improve is getting your hands dirty.

2. Don’t memorize — Avoid Overfit

Source: hercampus.com

Back in school, did you ever memorize exactly how to solve a math problem, only to find that on test day, the problem was slightly different and seemingly impossible to solve? You recognized that it was the same concept, but that minor change completely threw off your approach.

This is what happens when we memorize a process, we lose the ability to generalize and adapt to situations that we’ve never faced before.

In machine learning this is known as Overfit. It happens when you over train your model to the point that it learns all your training data by memory. This is a really dangerous thing, because if we test it with this data it’ll probably have a performance of almost 100%, but the truth is that it’ll actually perform worse on unseen problems.

Under fit, Good fit, and Overfit. Source: shapeofdata.wordpress.com

In a world where we have the knowledge we need just one click away, it doesn’t make sense to memorize.

That’s why most people fail to learn programming. They become demoralized because there’s too much to remember. But, that’s exactly their problem — I don’t know a single good programmer who’s not also a master at Googling.

According to Eric Mazur, professor of Physics at Harvard University and author of Peer Instruction, an interactive methodology of learning, students end up understanding nearly three times more when the focus is on understanding and not on rote learning.

The lesson is simple: Force yourself to learn without guides. After trying to figure something out by yourself, you can go back to your resources and review the process. This way, you avoid memorizing the guide without truly understanding.

3. Vary your training — Have variated, representative data.

We tend to think that doing the same thing over and over will eventually make us an expert at the subject. Even though we will make progress, there’s a much faster way. It’s not just about practice, but the way you practice.

The key is to vary your learning.

The best guitarists don’t play just one genre — those rock star didn’t become the masters they are just by playing rock. To reach their level of expertise, they had to explore and experiment with many different musical styles. Each of these styles contributed their unique qualities, resulting in a well-rounded and masterful guitarist.

“If you don’t know the blues… there’s no point in picking up the guitar and playing rock and roll or any other form of popular music.” — Keith Richards

To become good at something, you can’t keep doing the same stuff over and over. You have to vary the tasks you do, so you can have a more general and adaptable ability

When training a ML model, one of the most important things is using data that represents many different scenarios.

Source: Bibliocad.com

If you wanted to teach your alien friend what chairs look like, you would want to make sure to give him pictures of different styles and views of chairs.

The next time you want to pick up a new skill, try approaching it from as many different angles as possible.

4. Don’t reinvent the wheel — Transfer learning.

Source: brandsalsa.com

In Silicon Valley, you get to meet a lot of career switchers who end up doing really well in their new area. There are Architects who’ve turned into Designers, Lawyers into Sales reps, Engineers into Marketers, etc. The interesting thing is that they all find applications of their old knowledge to their new careers.

There’s a technique used on Artificial Neural Networks (mainly for Image Recognition) called Transfer Learning. Instead of training a whole new model from scratch, you can take one that was previously trained for a similar task and apply part of that older knowledge to the new task. Understandably, this results in improved performance and saves weeks of training time compared to a new model.

Source: Transfer Defect Learning deck by Sunghun Kim.

Continuing with the guitar player example; if you already knew how to play piano, it would probably be much easier to learn guitar. It’s a very different instrument, but all the knowledge of keys, timing and scales would be pretty similar.

The key is taking advantage of brain connections you have already developed, and applying them to a different skill.

Skateboard skills are applicable on snowboarding too. Source : ilivextreme.com

By utilizing what you already know, you can approach new subjects with a head start.