Machine Learning Optimization

The main goal of machine learning is to build a model that works well. It makes good predictions in a certain set of situations. Machine learning optimization is the process of changing the hyperparameters so that the cost function is the least. One of the optimization techniques used to do this. It is important to try to keep the cost function as low as possible. Because it shows how far off the true value of the estimated parameter is from what the model thinks it should be.

Here, we will talk about the main types of ML optimization techniques.

Parameters and Hyperparameters of the Model

It’s important to know the difference between parameters and hyperparameters before we go any further. These two words are easy to mix up, but we should not.

Before you start to train the model, you need to set the hyperparameters. They have many clusters, a learning rate, and more. Hyperparameters show how the model put together.

The parameters of the model, but, learned during the training process itself. Getting them now is not possible. Examples of weights and biases for neural networks show here. In this case, this data is inside the model and changes based on what the model see.

To change the model, we need to change the hyperparameters. By finding the best way to combine their values, we can cut down on the error and build the most accurate model.

How hyperparameter tuning works

As we said, the hyperparameters are set up before we start to train. For example, you can’t know in advance which learning rate (big or small) is best in a given case. You can’t know this in advance. To improve the model’s performance, hyperparameters need to change.

After each iteration, you compare the output to what you expected. Look at how accurate it is, and change the hyperparameters if necessary. People do this all the time. You can do that on your own. Even you can use one of the many optimization techniques that come in handy when you work with a lot of data.

Top Optimization Techniques in Machine Learning

You can now talk about how you can change the hyperparameters of your model to make it work better.

When looking for the best hyperparameters, a process called exhaustive search or brute-force search. Use to look for the best one by looking at how well each candidate fits. You do the same thing when you forget the code for your bike’s lock and try all the possible ways to get in. If you want to learn how to use machine learning, you have a lot of options.

A simple way to do an exhaustive search is to use this method. A k-means algorithm is one example. You’ll have to look for the right number of groups. But, if you have to choose from hundreds or even thousands of options, it gets very heavy and slow. Because of this, a brute-force search is not very efficient in most real-world situations.

Gradient descent

Gradient descent is the most common method for reducing the amount of error in a model. When you want to do gradient descent, you have to go back and forth over the training dataset while changing the model.

Your goal is to make the cost function as small as possible, which will make the model more accurate.

It is over when you can’t make any more progress (make your error less). You have found a local minimum.

Classical gradient descent won’t work well if there are a lot of local minima, so it won’t work very well. When you find your first minimum, you won’t keep looking because the algorithm only finds a nearby one. It’s not meant to find the world’s best.

So, you have to be very careful when you choose the learning rate to use. If it’s done right, gradient descent can be a very efficient way to improve models.

Genetic algorithms

Another way to improve ML is to use genetic algorithms. The idea behind these algorithms is to use the theory of evolution to help machines learn.

In the evolution theory, only the organisms that can best adapt to their environment are able to live and reproduce. To figure out which specimens are good or bad for machine learning models, you need to know how to figure it out.

Imagine that you have a lot of different algorithms. This is how many people you’ll have. Some of the many models with predefined hyperparameters are better adjusted than the rest. Let’s look for them! Then, you figure out how accurate each model is. Then, you only keep the ones that worked out best. Now you can make some descendants with the same hyperparameters as the best models. Then you can make a second generation of models with the same hyperparameters as the best models.

Optimization of Deep Learning Models

You need to use good, cutting-edge algorithms instead of generic ones because it takes so much computing power to train.

It makes for deep learning optimization. Stochastic gradient descent with momentum, RMSProp, and Adam Optimizer are algorithms that make for this purpose.

Stochastic Gradient Descent

Stochastic gradient descent (SGD) proposes to solve the problem. It measures how much time it takes to run each iteration for big data.

There’s a process called “back-propagation” that takes the values. It changes them over and over again based on different parameters in order to reduce the loss function.

In this method, one sample chose at random each time to update the gradient (theta) instead of directly figuring out the exact value of the gradient. The stochastic gradient is a good guess at the real gradient. This optimization method cuts down on the time. It takes to update when dealing with a lot of different samples. It also removes some of the computational redundancy.

RMSProp

RMSProp is good for normalizing the gradient because it evens out the step size, which makes the gradient look the same. It can even work with small amounts.

Adam Optimizer

Adam Optimizer can handle noise. It can also work with very large datasets and many different parameters.

Conclusion

Gradient Descent and stochastic gradient descent are two types of optimization algorithms that we talked about in this article. It is very important in Machine Learning to use SGD to find a good way to solve a problem. Most of the time, it uses in Logistic Regression and Linear Regression, but it is also used in other types of regression. Its uses in Deep Learning as Adam and Adagrad.