Recent developments in deep learning have been focused on how to gather ever-larger amounts of data. This is great, because we’re living in an era where almost every product or service has a computer attached to it that can process large quantities of information quickly.
By having computers perform more complex tasks, they achieve better accuracy and performance than previous techniques. For instance, with the right set up, computers are now able to do advanced image recognition and classification!
This article will talk about some of the strategies that AI researchers use to give their algorithms as much raw data as possible. We’ll look at what kind of datasets different areas uses, and why it’s important to have lots of examples.
Once you get past this initial stage, there are many free sources of data online that anyone can access. Some make the data accessible through software, while others offer a paid account where you have full control over everything.
Data sets come in various forms too – numbers, images, videos, and even text documents are all suitable. In fact, most companies these days need very little context to understand what they are looking at before giving away the information.
Data sets with no pre-existing understanding are sometimes referred to as ‘raw’ data. These are often called unlabelled data since you don’t necessarily have to apply meaning to them yet.
Types of deep learning
Recent developments in neural network architecture have led to what some call “super-deep” networks. These super-deep networks use more neurons than ever before, which allows them to perform even higher level tasks.
The most popular type of these super-deep networks are called convolutional neural networks (ConvNets). A ConvNet uses many neuron layers organized into small groups of features that it can combine together to form new patterns.
These groups of feature combining neurons are referred to as kernels or filters. By having large numbers of these, the system is able to find increasingly complex structures within the data.
A major benefit of this structure is that you don’t need lots of data to train the network. Because the system finds its own internal representations of the data, only a few examples are needed!
This article will discuss how much training data is required to effectively use ConvNets for image classification. But first, let us review some other types of NNs.
Neural network
A neural network is an algorithm that has come to be called a deep learning algorithm. Neural networks are so-called because they simulate how neurons work in your brain!
In fact, some people even refer to it as artificial neural networking or ANN. At their most basic level, neural networks can learn information by looking at examples of data and then applying what she learns to new instances of data.
That’s really all there is to it!
So why are they such a powerful tool?
Well, it comes down to two things: firstly, they can handle very large amounts of data, making them great for big datasets; secondly, due to the way they organize information, they become increasingly intelligent as they gather more and more data.
Data sets with lots of examples usually take longer for a neural net to “forget” about a piece of knowledge, while data set with less examples may not teach it properly. By having access to ever growing stores of data, neural nets are always one step ahead.
There are many types of neural network, but the ones we will focus on here use numerical values (like numbers or equations) as inputs, which are then transformed into other numeric values.
These transformed values are referred to as activations, and each layer uses its own activation function to determine if a part of the model should respond positively or negatively to an input value.
Convolutional neural network
A convolutional neural network (ConvNet) is a deep learning algorithm that has seen dramatic success in computer vision applications, such as object recognition and image classification.
A ConvNet typically begins with an input layer that receives raw data from your source material (e.g., pictures of cats), followed by several sequential layers that process this information according to pre-defined rules.
The last layer of the net processes all the information it received through the earlier layers using simple mathematical functions (i.e., calculations). These functions combine local patterns into larger structures called [*features*]{}. Finally, these features are classified into one of several possible categories depending on their shape and position.
There’s no single right number of neurons in each layer of a ConvNet – it depends on how much training data you have! This makes sense because if there were a fixed amount of information in the early layers, then adding more pixels to the picture would just saturate those layers and prevent them from functioning properly.
However, as we mentioned before, having too few neurons can result in poor performance, so most good networks have at least partially redundant architecture.
That said, some research groups have experimented with very thin architectures where every neuron does its work directly influencing the final output, but this comes with costlier computational resources and longer inference times.
Recurrent neural network
A recurrent neural network (RNN) is an artificial neuron network that has connections that go both backwards and forwards in time. This allows it to learn how information flows through past experiences, creating predictive patterns.
Traditional feed-forward networks can be used to take input data and produce output data, but they do not have connections that remember information beyond the next instant. An example of this would be predicting whether or not someone will like something based solely off their facial expressions and body language at the moment you pose the question.
By including these “memory” cells into your model, RNNs are able to use all of the previous information to make more accurate predictions.
There are two main types of RNN architectures: sequence-to-sequence models and convolutional recurrent models. Sequence-to-sequence models connect one set of inputs to one another sequentially, while convolutional recurrent models look across time within sequences.
Deep neural network
A deep neural net is an advanced machine learning technique that has been exploding in popularity over the past few years. Machines with this technology are referred to as “deep” because of how many layers they have, and how much data they process through each layer.
Deep neural nets learn by example. Rather than giving it instructions like most algorithms do, a DNN learns concepts by looking at examples of these concepts and applying what it learns to similar situations. This makes them very powerful tools.
With the right amount of training material, a DNN can perform almost any task you give it. They have already been used to accomplish things such as producing quality pictures, writing effective articles, translating languages, and even playing some games really well!
Because they work so well when there is enough training data, people have started using DNNs to solve new problems. By designing systems or apps using DNNs, we could potentially achieve our goals more efficiently.
Transfer learning
A popular technique in deep neural networks is called transfer learning. This is when you use an existing network to learn new tasks or domains. For example, let’s say your goal is to predict if a movie is good or bad. You could take a pre-trained net that predicts if something like “Harry Potter” is fiction (good) or nonfiction (bad), and then teach it to determine if any other book, film, or series of notes is good or not!
This is what most AI systems do these days. Take someone else’s work and tweak it slightly so it does your job better than it did before.
A key part of this process is using pretrained models — sets of software instructions that have done some specific task already. These pretrained models save time because you don’t need to spend hours training a totally new set of algorithms!
There are many different ways to use pretrained nets, but one of the more common ones is what we call fine-tuning. With fine-tuning, you start with a well-working model and you test how closely it matches our definition of a successful outcome. Then, you slowly add layers and tweaks to make the model match our definitions even closer.
Here’s an example: Let’s say your current model doesn’t include the word ‘success��’ in its vocabulary.
Applications of deep learning
Recent developments in artificial intelligence (AI) have focused on what are known as “deep neural networks” or DNNs. Developed by researchers at Google, these AI systems use multiple layers to teach themselves how to perform specific tasks.
For example, let’s say your goal is to predict whether someone will go on a shopping spree after watching a movie about buying expensive things. YouTube has millions of videos, so you could train an AI system to look through that data to determine if people who have watched such a video went on a spending frenzy afterwards.
A trained machine learning algorithm would be able to tell you from past behavior that people who watch action movies tend to buy lots of products afterward. So it would then cross-reference this with information on online purchases to make its prediction.
Google uses this kind of AI technology to suggest related searches while you do research or to recommend books or films to read or watch. This applies not just to entertainment, but also to practical applications like helping companies understand their customers better or improving computer vision — being able to identify objects using software programs.
Financial markets
Another area where deep learning has found applications is in financial analytics. This includes analyzing market data, predicting future price movements or outcomes, and understanding how different factors influence the market.
Trading strategies are often dependent on large quantities of historical data and accurate predictions of this data depend on powerful computational engines like those used for deep learning.
Deep neural networks can be trained to learn complex patterns in datasets that contain large amounts of information. These patterns can then be applied to make conclusions about new data sets that have not been seen before!
There are many areas within finance that could benefit from applying deep learning to gain insights into the economy. For example, researchers have applied it to predict stock market swings and determine the riskiest assets to invest in.
Another application of DL in finance is credit scoring. By looking at your past income statements, spending habits, payment histories, etc., computers can assess whether you will spend money responsibly or if you are likely to run out quickly.
This is done by calculating what variables correlate with how people behave during times when they have access to money. The average person who goes through debt troubles usually changes their behavior around money so there are lots of statistical correlations to consider.
Researchers use these numbers to create predictive models that calculate an individual’s likelihood of going bankrupt.
