Recent developments in computer science have led to another revolution that has quickly become mainstream- deep learning. Technically speaking, this is not quite AI as we know it, but it is definitely using neural networks to learn tasks for computers.
Deep learning involves creating an algorithm or network of algorithms that process large amounts of data to achieve results with accuracy. Technology companies use these algorithms to create software and applications that require them to work effectively.
For example, Google uses convolutional neural networks (CNNs) when teaching its automated systems how to recognize objects in images. These CNNs are very efficient at finding patterns within lots of information so they’re often called pattern recognition tools.
By having machines automatically do some of the thinking for us, it reduces workload and makes things faster and more accurate. Companies now rely heavily on AI technology to perform various functions such as answering questions via chatbots, identifying disease symptoms, and even helping doctors make diagnoses and suggestions.
Because these technologies are becoming increasingly common, there are many cases where someone can suffer performance loss due to lack of knowledge about how to validate and assess DL models.
Use a variety of validation metrics
There are many different ways to validate the performance of a deep learning model. While some methods have fallen out of favor, there is no one definitive way to evaluate models. What’s important is finding what works for you!
One of the most popular approaches is using accuracy as the metric. This means calculating the percentage of examples that the model correctly classifies. However, this only tells part of the story when evaluating predictive analytics tools.
Other more detailed metrics look into whether the model makes correct predictions in certain categories instead of just if it predicts the right category or not. These additional metrics help mitigate false positives where the tool does not require an update but also gives poor results.
A common set of these metrics are called precision-recall curves. A precision-recall curve plots both recall (the proportion of positive instances identified) against precision(the ratio of true positives over total predicted). At each point on the graph, you can determine which value is more important by looking at the position along the x axis, and which is more important by looking at the position on the y axis.
In general, higher values of precision and/or recall are considered better indicators of strong modeling. When both are high, it suggests that the model is doing a good job of identifying all relevant features needed to make a prediction while ignoring irrelevant ones.
Use different training and validation configurations
It is very important to use appropriate settings for your model during both training and evaluation. Settings that affect performance of the network include: batch size, number of epochs, learning rate, regularization factors like dropout or weight decay, and how you choose to measure accuracy.
When developing your deep neural networks, it is common to start with a large batch size and small number of epochs (the number of times through the dataset used for training) before reducing these numbers in order to achieve better results. Reducing these initial parameters can take many attempts before achieving good test accuracies, so be prepared to spend some time doing this!
After the models are trained, people often report using the same hyperparameters across all datasets and tasks, which may not work well for every situation. Testing several different configurations can help determine if there’s a setting that works particularly well for yours.
Run training and validation checks regularly
While there are many ways to check the performance of your model, one of the most important is regular use of the trained model. This includes using it for both new data and older data that it has already been trained on.
The best way to do this is to create test sets that contain similar data as what the model was originally trained on. These can be called out datasets or different sources of content used in the same genre as the original dataset used to train the model.
By doing this consistently, you ensure that your model works well on newer material and/or material it has seen before. You also reduce chance of overfitting, which would cause the model to perform poorly on unseen data because it thinks it knows how every piece fits together.
Revisit training and validation processes
It is very important to revisit your training process and methodology for deep learning models. This includes changing how many layers you have, what activation functions you use, and even altering the batch size or number of epochs.
There are several reasons why having an in-depth understanding of these settings changes is so essential.
For one, different initializations can result in slightly different optimal settings being found. While not necessarily bad, this does mean that depending on which setting worked best before may not work as well now.
Another reason is that when people say they used X depth neural network architecture, they usually do not specify any other parts of the model like layer types, activation functions, etc.
By taking the time to evaluate whether those things matter to you, you can determine if there is value in investing more into this area.
If yes, then it’s worth looking at how to implement them more thoroughly.
Use an ensemble of models
An increasingly popular approach in deep learning is using what’s called an ensemble method. This involves training several different neural networks as separate models, and then combining their predictions into one final result.
The easiest way to think about this is like having multiple people vote for the winner of a contest. All of the voters can have their own opinion, but they must agree on who wins to make sense!
By doing this with lots of candidates, you get a better overall picture of how well each model performs compared to others. It also gives you some leeway when individuals fail or are wrong. By averaging out these errors, you strengthen the overall predictive power of the model.
Ensemble methods are not new, but there are a few variants that work particularly well with AI applications. The two types most commonly used in NN research are stacking and hybridization. Let us look at both of them in detail.
In stacking, we train individual models and then combine their outputs by taking a certain weighted average. For example, say we trained three logistic regression classifiers (a type of ML algorithm). Each would take input features and output a probability that the object was either positive (like an image containing a dog) or negative (no dog).
We could then create a third classification layer which took all three probabilities and produced a final decision.
Calculate the accuracy of your model
The most fundamental way to evaluate the performance of any ML algorithm is by calculating its accuracy. Accuracy is defined as the proportion of cases when the model makes correct predictions out of all the instances that it was tested with.
The accuracy of a classification model can be determined in two ways, internal or external accuracy. Internal accuracy calculates how well the model predicts outcomes for samples that it has already seen, while external accuracy measures whether the model’s prediction matches what we actually know about the outcome.
For example, if our model predicted that every person is not only likely to go shopping but also guaranteed to buy at least one item then it would have very high internal accuracy because it accurately identified the negative outcome (no shopping) and the positive outcome (purchase). However, this wouldn’t tell us much about the model’s predictive ability since it assumes that everyone goes shopping at least once!
External accuracy comes into play here, if we look across many different models then we can compare their accuracies according to whether they predict an activity like going shopping in people who do or don’t usually perform such activities. This way we get a more accurate picture of the model’s generalization capability.
Calculating internal and external accuracy
Internal accuracy is relatively straightforward and just requires knowing the number of times each class was present in the dataset used to train the model.
Consider using a hybrid model
A recent trend in deep learning is using two different architectures for your model, one that can be considered shallow and another that is deeper. These are referred to as “shallow” models and “deep” models, respectively.
A common example of this is VGGNet. This network has three main layers – convolutional, fully connected and pooling. The first two make up the shallow part while the third is the deep part. More complex features are able to extract higher level information by combining lower level details.
The problem with having only a shallow or a deep layer attached to the rest of the network is that it does not provide enough context for the layers that follow it. If you have a very high number of categories then this may not matter too much, but if there are fewer than say ten then telling what category an image belongs to becomes more difficult.
Hybrid networks combine both types of architecture into a single network. By adding in some sort of connecting middleware, they are able to achieve similar results to having separate shallow and deep models.
Ensure that you have strong post-hoc explanations for your predictions
Recent developments in AI technology have led to the emergence of deep learning systems. These are computer programs that use neural networks to achieve predictive outcomes.
Neural networks work by taking large datasets of examples and teaching the system how to apply those lessons to new data sets. For instance, if there is a dataset containing images and labels describing each image, then a trained network could learn what types of pictures contain text and what kinds do not.
By looking at lots of such examples it becomes able to predict whether or not an unknown picture contains textual content. This process is referred to as classification. On its own, however, this type of algorithm would be very limited because it wouldn’t know why someone wanted to include text in their picture or why they didn’t.
Thus, additional features must be used to determine why a certain outcome was achieved. These features can come from the image itself (shape, color, texture), the subject matter (is it about sports? Animals?), or something related to the person who took the picture (anniversary gift). Using both kind and feature theories will allow us to make more accurate predictions.
Given all these elements, most people agree that developing models using these principles is a good way to go about improving accuracy. However, just like with any other tool, there are some things we need to pay attention to when utilizing them.