Recent developments in artificial intelligence (AI) have caused quite a stir. Companies that were once considered competitors are now being overshadowed by AI systems that can outperform humans in various domains, from games to language understanding to autonomous vehicles.
Many of these advanced algorithms use what’s been coined as deep learning. This term refers to algorithms that have more layers than earlier machine learning methods.
By having many different functions nested within each other, neural networks can achieve impressive results. They also require less data to be effective compared to older techniques.
That’s why most companies using AI incorporate it into their technology. It may even replace some of the older models where possible. However, there is one major disadvantage of this approach- they aren’t very transparent!
There isn’t much you, the average person, can do to understand how an AI system works. Even if you’re highly experienced in computer science, it’s difficult to apply your knowledge to complex software.
This article will take a closer look at the inner workings of two popular classes of deep learning algorithm – convolutional neural networks (CNNs) and recurrent neural networks (RNNs).
The number of neurons in a hidden layer affects how a network processes data
A common way to improve the performance of neural networks is by adding more layers. More layers mean that the algorithm has greater capacity to process information, which can help networks make better predictions for the same amount of training data.
The size of each individual neuron’s dendrite (the thing where it receives messages from other parts of the neuron) or axon (the place where the message is sent) directly impacts the accuracy of the model.
If a neuron is very short then it cannot receive as many messages, and thus only gets partial info about what happened in the past. This makes the neuron unable to generalize and learn important patterns that predict future events.
On the other hand, if a neuron is too long it won’t be able to send out strong signals quickly, because it will need time to gather up enough electricity to actually fire off an action potential.
The number of input neurons affects how a network processes data
Recent advances in artificial intelligence are made using deep learning, which is typically categorized into one of two categories: convolutional neural networks (CNNs) or recurrent neural networks (RNNs).
A CNN has several layers that each contain many neurons, where each neuron receives inputs from nearby neurons and applies an activation function to produce an output.
These layers can be repeated multiple times depending on what kind of pattern the AI wants to learn. For instance, say you wanted to recognize cats. You would have a layer for individual cat features such as whiskers, fur texture, etc., then a layer for shapes such as squares, circles, triangles, and so forth, and then a final layer to determine if it had enough information to classify the image as a “cat”.
An RNN works similarly to a human mind, in that it remembers past events and uses those memories to make predictions about future occurrences.
However, instead of having separate layers for different types of patterns, it forms internal connections between itself and earlier time steps. This allows it to find relationships that occur over time rather than just at a single point in time.
So why does this matter? It all comes down to how many parameters your algorithm has! More layers mean longer sequences of numbers are able to influence the outcome, giving it more opportunity to pick up on trends.
The number of output neurons affects how a network processes data
Having more layers means that your neural net can process more input information and find increasingly complex patterns in this information to produce new outputs. More layers also mean longer training times as there are additional parameters to optimize for during learning.
However, having too many layers is not always useful. Too many layers increase the chance of overfitting which happens when the model learns overly complicated functions of the inputs. Overfitted networks do not generalize well beyond their training set because they have learned specific features that only apply to the dataset used to teach them.
Deep neural nets with three or fewer hidden layers usually perform best since they are less likely to suffer from overfitting. On the other hand, deep nets with lots of hidden layers may be better at avoiding such problems but may not improve performance much due to diminishing returns.
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Deep neural networks are difficult to implement
Creating deep learning algorithms is not as simple as creating other types of machine learning algorithms. This is because they contain multiple layers that connect together, requiring careful planning for how many layers you have, what type of layer each one is, and where to get your data needed to train it.
The more layers you have in your network, the better the network will learn, but only if everything else is correct!
Too few layers will result in underfitting (learning modes that do not include all important information) while too many can lead to overfitting (testing examples fitting the model instead of helping separate new instances from knowledge already stored in the algorithm).
Algorithm creators must be very careful about how many layers their net has since there are mathematical limits to the number of layers you can add before things break down.
Deep learning systems with lots of layers require incredible amounts of training data and computational power to work properly, which makes producing quality software tough.
That said, there are some strategies you can use when trying to create your own net structure. By thinking about ways to reduce the effect of over- or underfit, you’ll find yourself using thinner nets than you would otherwise.
There are many different types of neural networks
Neural network architectures come in several varieties, with some being better than others for certain tasks. Some use convolutions to look at small chunks of the image or input layer to find patterns that determine what is going on in the rest of the network.
Convolutional neural nets (convnets) were the first type of deep learning algorithm and have been used to solve a wide range of problems. They work by looking at small patches of an input image and seeing if there are repeating pattern features you can identify.
Recurrent neural networks (RNNs) work similarly to convnets, but instead of just patching together parts of the picture, they remember past instances of the pattern and combine them into one complex idea of what’s happening in the image.
State-of-the-art computer vision applications such as self-driving cars make heavy use of both RNNs and CNNs. Researchers test out their algorithms on large datasets of pictures and videos and then tweak the settings until the system works well.
Another kind of net uses what’s called backpropagation to adjust its layers depending on how well it is performing its task. This way, the earlier layers learn from the later ones and vice versa, which helps prevent the algorithm from getting “stuck” on incorrect assumptions.
Some examples of neural networks include: a) Multilayer perceptron
A multilayer perceptron (MLP) is one type of feed-forward network that has several layers of nodes connected in a way designed to process information.
The term “perceptron” comes from early computers, which used weighted electrical impulses to recognize patterns. For instance, we are pattern recognition machines!
In MLPs, there are an overall number of neurons or processing units called the layer size. Then, within each neuron, there are also an overall amount of input connections (or weights) and output connections (or thresholds).
These can be modified during training depending on whether they are increased for some or all classes, or decreased for others. This is how the algorithm learns which categories are associated with each other and what category any given example belongs to.
Overall, bigger numbers mean that the algorithm will learn more complicated functions and concepts. It becomes able to do things like classify pictures and speech. However, these larger numbers may not work as well in practice because the computer will overfit the data it is learning from.
Deep learning algorithms such as convolutional neural nets and recurrent neural nets have become increasingly popular due to this reason. Systems using deep learning often suffer less from overfitting than those using traditional machine learning methods.
Some advantages of a multilayer perceptron are that it performs well with both linear and nonlinear relationships
One major advantage of using a MLP is how many layers you have to build your network. Because there is an inner layer, middle layer, and outer layer, you can start with any size input and expand or reduce the number of layers depending on what your data looks like.
A one-layer neural net will only contain one internal layer, which makes training much more difficult because your computer needs to figure out how to combine all three components (inputs, middle layer, output) at once.
There are some algorithms that use just a single neuron in their middle layer, but they’re very hard to train due to lack of convergence. A multi-layer perceptron uses several neurons in its middle layer, making it easier to get good accuracy.
However, this comes with another cost – the extra layers take up additional space within the algorithm, reducing overall efficiency. It is dependent on the problem domain whether or not this is a deal breaker for you.
Some disadvantages of a multilayer perceptron are that it can get stuck in local minima
A deep learning algorithm with too many layers is sometimes described as having “overfitting” or getting “locked into” a pattern instead of finding the best-fit solution. This happens when the model learns patterns from the training data which do not translate to testing samples correctly.
When this occurs, you may notice some test results that look very similar to those during training, making it hard to determine whether the sample was accurately classified as the correct class or if it was just learned similarly.
This effect is less common with shallow networks (networks with one layer) than ones with multiple, but larger numbers of layers.
With deeper networks, there can be issues where the network does not improve its performance even after a lot of attempts to train, indicating that it has ‘hit a wall’ and found no better way to fit the data. This also means that the model will likely take longer to converge and potentially fail to converge at all!
So how do we avoid overfitting?
Using more examples for training is always a good start, but there are other ways to ensure your model doesn’t pick up on spurious correlations in the data. One such method is called regularization. Simply put, adding a small amount of noise to the system helps prevent it from fitting noisy information.
There are several types of regularization including weight decay, dropout, and early stopping, but perhaps the most straightforward is batch normalization.
