Introduction
Machine learning uses a lot of things, but what is machine learning? The answer to this question is that machine learning is the use of algorithms to teach computers how to learn from data. But what does it mean to “teach” or “learn”? In this article we’ll cover some of the most basic concepts in machine learning, including:
‘A’ is for Autoencoder
Autoencoders are a type of neural network that learns to compress data. They can be used for dimensionality reduction and feature extraction, as well as generating new data from existing data. Autoencoders are often used in unsupervised learning, but they can also be applied to supervised learning problems by adding labels to the input layer (this is called “supervised autoencoder”).
‘B’ is for Backpropagation
Backpropagation is a method for training neural networks. It’s a technique for calculating the gradient of a loss function with respect to the network parameters, allowing you to figure out how much each individual parameter affects your overall performance. The backpropagation algorithm itself is pretty complex, so if this is something that interests you, I’d recommend reading up on it elsewhere before continuing!
‘C’ is for Convolutional Neural Networks (CNN’s)
CNN’s are a type of neural network that can be trained to detect patterns in images. CNN’s are useful for image recognition, object detection, and classification.
CNN’s have been around since the 1980’s but they weren’t widely used until 2012 when Alex Krizhevsky published his first paper on them at the International Conference on Machine Learning (ICML). In this paper he showed that you can use convolutional layers to classify an image into one of 1000 classes with over 90{6f258d09c8f40db517fd593714b0f1e1849617172a4381e4955c3e4e87edc1af} accuracy.
‘D’ is for Decision Trees
Decision trees are a way of breaking down a problem into smaller subproblems. The basic idea is that you start with the most general question, then break it down into more specific ones until you get to the point where you can make a decision.
A decision tree is represented as a tree structure with each node representing an attribute and each branch representing possible values for that attribute. Each leaf node represents some kind of class or category–for example, if we have data about different types of fruits (apples, oranges and bananas), then our leaf nodes might be “apple” or “orange” or “banana.”
When learning from data using decision trees there are two main steps:
- Grow your tree – this consists of growing branches from each internal node until they reach terminal nodes (leafs). At each step along the way we compare our current set against several possible splits until one produces better results than others; this step continues until all terminal nodes contain only one instance apiece
‘E’ is for Embedding, the process of taking the input and mapping it to a number between 0 and 1.
E is for Embedding, the process of taking the input and mapping it to a number between 0 and 1.
It can be done by creating an n-dimensional vector for each word in the vocabulary and then calculating the cosine similarity between these vectors.
‘F’ is for F1 Measurement, an evaluation metric that assesses how well an algorithm performs at making predictions relative to the correct answer.
F1 measure is a weighted average of precision and recall. It is used in machine learning to evaluate the performance of an algorithm, where F1 is calculated as follows:
- P(a) = True Positive Rate
- R(b) = False Negative Rate + True Negative Rate / 2 (if there are no false positives)
- P(c) + R(d) / 2 (if there are no true negatives).
‘G’ is for Gradient Descent, an algorithm that finds local minima and maxima by adjusting variables in directions opposite to the gradient of a function.
In machine learning, a gradient descent is an optimization algorithm that finds local minima and maxima by adjusting variables in directions opposite to the gradient of a function. It’s a common algorithm used in machine learning, especially for optimization problems.
It’s an iterative method that converges to a local minimum; however, there are no guarantees about how long it will take to get there (or if it will ever arrive).
‘H’ is for Hierarchical Methods, which use multiple layers of neurons with different sizes to classify data. They’re useful when you have large amounts of data that you want to process quickly.
Hierarchical Methods are a type of machine learning algorithm that uses multiple layers of neurons with different sizes to classify data. They’re useful when you have large amounts of data that you want to process quickly, such as images or video.
For example, let’s say we want to teach our computer how to recognize cats in images by showing it thousands upon thousands of pictures with cats in them and thousands upon thousands without cats in them (and hopefully nothing else). The problem is that there are so many images out there–so many possible combinations–that even a supercomputer would take forever just trying out all the possibilities! But what if instead we break down each photo into smaller chunks? Say I break down one image into four squares: top left corner, bottom left corner etc., then feed those squares into four separate neural networks; each network will only look at one square at a time instead of having all its attention focused on just one big picture at once. Then if any two networks agree about whether an object is present within their respective squares’ boundaries then we know for sure whether or not there’s anything worth checking out further down towards center axis where most objects tend reside anyway…
Machine Learning uses A-Z
Machine Learning uses A-Z. The alphabet is composed of letters, which are used to create words and sentences. In the same way, Machine learning has its own set of building blocks that can be used to create sophisticated solutions for problems like image recognition or text classification. These building blocks are called algorithms, and each one has its own purpose within ML (for example: CNN’s are good at processing images).
Conclusion
We hope you enjoyed learning about the alphabet of machine learning. If you want to learn more about how to use these algorithms in your own projects, check out our tutorials on the topic!