Over the past few years, there have been significant developments in computer technology that allow for increasingly complex systems to perform specific tasks. These technologies are referred to as artificial intelligence or AI.
One area of AI that has seen some growth is called deep learning. As the name suggests, this is an advanced algorithm that uses layers (think sheets of paper with lots of information printed on them) to teach computers how to achieve certain goals.
By using repeated cycles of data input and experimentation, the system can learn new behaviors or patterns. Once it learns these patterns, the software takes over the task of completing the given project.
There are two major components used in most cases of deep learning. The first is what is called neural networks. Neural networks work by having several “layers” of nodes connected together. Each layer gives off different signals based on the inputs from the previous layer(s).
The second component is the use of mathematical functions or rules known as activation functions. These activate each node in a particular way depending on the input from either side of the node. For example, if the node receives input that the next adjacent node should be completely blocked, then it will switch itself on so that the block occurs.
Deep neural networks
Neural networks are an incredibly powerful tool for computer science. They have seen incredible success in applications such as image recognition, speech processing, and machine learning algorithms.
One type of neural network that has become very popular is called deep neural networks or DNNs. These are neural networks with more than two layers- usually at least three!
By having many different layers, DNNs can learn complex functions that go beyond what computers had access to just a few years ago.
They also work well when there are “channels” of information going into the system, which DNNs are able to use. For example, photographs contain lots of channels (color, shape, texture), while voice contains less frequency data and text contains even less. By using all these types of information, DNNs can perform well.
There are several ways to make DNNs work effectively though. One way is through how many layers you have and what kind of layer you are adding onto the network.
Convolutional neural networks
CNNs are one of the most important architectures in modern-day machine learning. They consist of several layers that perform different functions to process input data.
The first layer is referred to as the feature extraction layer because it looks at small parts of the image or sound file to find patterns and features. For example, it may look for lines or shapes, or determine how much noise there is in the signal.
The next layer is the convolution layer, which multiplies each element of the previous layer by various weights and then sums them together. This multiplication mimics how neurons work in our brains, where you have lots of little “neurons” connected to other neurons via synapses. The weighting factors used here mimic the strength of the connection between two neurons.
After this comes what’s called the pooling layer, which removes some information about the item being analyzed. Some common examples of poolings include taking the average, picking out the biggest chunk, or even looking at the edge of the thing being analyzed and using that value as the final output!
Lastly comes the fully connected (or dense) layer, which takes the outputs from the preceding layer and merges them all into one large blob of numbers. This is similar to how humans think when they connect with others – everything gets mixed together and interpreted as one idea.
Recurrent neural networks
Neural network algorithms are categorized into two main groups, non-recursive and recursive. Non-recursive algorithms do not have an internal layer that is repeated to form the output, instead they take input from all layers before computing their final result.
Convolutional neural networks (convnets) fall under this category. These work by taking inputs at each position in the image and applying learned functions or kernels to produce the results.
Recurrent neural networks (RNNs), such as long short term memory (LSTM) networks, are another type of non-recursive algorithm. Like convnets, RNNs also apply internal learnable functions to get their result, but these functions can remember information over time, allowing for longer sequences to be analyzed.
Deep learning algorithms combine both types of neural networks to achieve powerful performance! This article will go more in depth about how different architectures use recurrent techniques to function.
Long-short term memory networks
Neural network architectures are usually categorized into two main types, which are referred to as convolutional neural networks (CNNs) and non-convolutional neural networks (Non-CNNs). CNNs include what have become known as layer types such as Convolutional Layer, Pooling Layer, Fully Connected Layer, and Activation Function. Non-CNNs typically use fully connected layers that are much simpler than the more advanced CNN layers.
However, this does not make them any less powerful! This is because every layer in a deep learning algorithm has its own set of learnable parameters that can be tuned during training. By stacking many such layers together, you get very complex algorithms that work really well. What’s interesting about these algorithms is that they will automatically figure out how best to combine all of these layers to solve the problem at hand.
This process is called back propagation or error correction. Back propagation happens when each layer outputs a result that is altered by the input it receives from a lower level neuron/layer.
Support vector machines
Another fundamental algorithm in machine learning is the support vector machine (SVM). A lot of applications use SVMs, including speech recognition, image classification, and natural language processing.
In fact, many applications of deep neural networks are actually just very well-designed SVMs!
By now, you’ve probably noticed that most of the layers in modern AI algorithms are some kind of feature extraction or transformation.
These features are then input into an SVM to classify the data. If there were no additional transformations applied to the data, then an ordinary binary classifier like an SVM would work!
It’s true: almost every layer in state-of-the-art artificial intelligence systems is an SVM! That’s why they’re called “deep” SVMs — because they have lots of hidden layers beyond the softmax.
We can even rephrase the first part of this article’s title as: Why not use an SVM instead of a traditional logistic regression model? Because it’s effectively the same thing, with the addition of more advanced optimization techniques.
Probabilistic neural networks
Neural networks have become increasingly popular in recent years, with every major company developing their own variation. There are three main types of neural network that we will be talking about in this article!
The first type is feed-forward networks, which contain multiple layers where information is passed along different neurons or brain cells. These layers can be either convolutional (where each layer convolutes input data by looking at small windows of the image or piece of text), recurrent (where the output depends not only on the current content but also what comes before it), or some combination of both.
Feed-forward nets with convolutions were one of the most successful architectures for images due to how they combine spatial and non-spatial features. Recurrent nets like long short term memory (LSTM) work similarly except that they remember past events as well.
Probabilistic neural networks add an extra level of complexity to the system. They create a probability distribution over all possible outputs instead of picking just one like traditional net classes do. This way, the algorithm learns more nuanced representations of the data rather than relying on simple classification.
There are many ways to use probabilistic models in deep learning. Some applications include computer vision tasks such as object recognition and segmentation, natural language processing (NLP), and sequential prediction problems like speech recognition.
Deep belief networks
A deep neural network is just like any other network, except it has more layers than an ordinary net. These additional layers are referred to as hidden layers or internal layers.
The number of internal (hidden) layers you have in your DNN determines how many “layers” there are in your model. More layers mean longer to train, but eventually you will get better accuracy since the network can go deeper into the data.
Convolutional nets and recurrent nets also use lots of internal layers to function properly- convnets use several kernels that shift across parts of the input while looking for patterns, and recurrent nets learn long term dependencies within the data.
In fact, some state-of-the-art models contain over 100 million parameters! That means there are enough weights so that every bit of information in the training set gets amplified by the algorithm.
This article will talk about how to implement dropout in TensorFlow, what is dropout, and why you would want to use it with CNNs and RNNs.
A slightly different way to define deep learning is using it as transfer learning. This term was first coined in 2017 by Alexey Dosovskiy, a research scientist at Google. He defined it as “training with one algorithm and then applying that knowledge to another related task or domain”.
A good example of this would be if you needed to learn how to write an article but didn’t have any content to work from. You could take some pre-written articles and edit them to make them yours!
This concept can easily be applied to neural networks too. By taking a trained network already designed for other tasks and settings, we are able to apply it to new problems where the model has been adapted to perform well.
That is what makes these algorithms so powerful – you don’t need to start completely fresh! Instead, you can use parts of the algorithm that have worked before to solve your current problem.
There are many applications of transfer learning in computer science and engineering. Neural networks have become very popular due to their effectiveness when used correctly.