How to Evaluate if Deep Learning Is Right For You?
Deep learning is one of the hottest trends in artificial intelligence right now. It's also one of the most difficult to understand, so you must know what deep learning can do for your business before you start implementing it.
This blog post will discuss how to evaluate when deep learning is right for you and what benefits it may provide.
Deep learning is also known as deep neural networks. That's because it mimics the neural networks in your brain that you use to think and learn, which can be considered layers or sections stacked on top of each other. There may be several hidden layers between the input data (e.g., an image) and the output (e.g., an object or face) with deep learning.
Deep learning is especially good at sifting through tons of data to find patterns. This makes it great for tasks like image or voice recognition, where you have hundreds of thousands if not millions of examples.
Traditional models do have their merits.
Deep learning isn't the ideal solution in all cases. Sometimes, traditional models perform better than deep learning because they're more interpretable.
In a traditional model, you can see what features fed into the decision and affect performance. In comparison, deep learning may be challenging to interpret because it's not always clear where your data is going or how it's being processed before arriving at an answer. That opacity means it might take longer for others to trust its results, especially for tasks that require approvals.
Traditional methods perform better for small datasets with fewer features. They also learn significantly faster than deep learning models.
That's not to say traditional models always outperform deep learning, but it does happen sometimes! So don't count out your old machine learning techniques just yet if you're stuck on what approach is best for a project or business problem you face.
You can also try using feature selection and extraction tools like SVM that help you sift through lots of data to find just what your business needs. By teaching it about the features and requirements, SVM can build models with less data than deep learning requires while providing accurate results.
This has been tested in several studies too. Here's one that says SVM models had better results than Convolutional Neural Networks (CNN.)
How can you pick the right machine learning approach?
The number one strategy is to try comparing both approaches head-to-head, so you know which one will work best for your problem.
For example, you could split the data into two parts and build a traditional model with one group of examples and a deep learning model on the other. Then compare their performance to see which produces better results for your business problem or use case.
If you have a ton of features, deep learning might be the right choice for you.
There are often more features than data points to work within real-world problems, so it's not always possible to see which ones are important. That means machine learning algorithms need to sift through all the data before choosing the best features to work with.
Deep learning helps find which feature is the most important, like identifying the face in an image even if it's turned away or partially obstructed. That means less time tuning traditional machine learning algorithms to choose the right ones before training for better performance overall.
What does deep learning actually do?
With deep learning, you feed data into the model, and it learns how to recognize patterns on its own. For instance, if there's a pile of sand and another pile that contains only rocks, the input layer might see just that: two piles of sand and rock. Each deeper layer in the network can learn more complex concepts from those simple ones, like adding more rocks or mixing in mud. And that information moves through the layers all at once.
Deep learning works best for problems like image and voice recognition, but it also requires a lot of data. For example, we know that deep learning can do well with 100,000 examples or more to learn from. So if you only have 1,000 examples (i.e., images), then traditional models are probably the way to go.
One common challenge is the computational resources required for deep learning. It can take days or even weeks to train a deep learning model, so you need your data and workflows in place before you start.
You also want to be sure that your problem can be solved with deep learning. Deep learning excels at tasks like image, voice, and text recognition, so be sure to check out the list of popular deep learning use cases below.
If your business does happen to face a problem that you can solve with deep learning, don't give up on the other approaches listed above just yet! For example, if you need an interpretable model to help others trust its output, then traditional methods like SVM may be a better bet.
Where does deep learning perform well?
The possibilities for deep learning are endless when it comes to computer vision. It's used in autonomous cars, image recognition (e.g., Facebook can tag your photos for you), medical imaging like radiology and pathology reports, drones that map terrain or scan the ground below them, security systems like facial recognition at airports or even smartphones apps like Prism.
Deeper neural networks with many hidden layers and more neurons can also help computers detect speech patterns, which is how Apple's Siri and Amazon Echo work so well at understanding what you're saying. Google Voice Search is another excellent example of this technology in action. It uses deep learning models to capture noise from up to 20 feet away while filtering background sounds.
Deep learning has also made it easier to build more engaging chatbots. These computer programs can hold conversations with users, which is why you might have used one recently if you've ever had a conversation using Facebook Messenger's M assistant or chatted with Internet-based customer service bots like Poncho.
Deep learning models help these assistants understand what you're saying and provide helpful responses.
In summary, deep learning is a branch of machine learning that uses deep neural networks that can learn abstract features like sounds, images, and language. Deep learning has several advantages over other machine learning methods, including better performance in vision, voice, or text recognition tasks.
However, it requires more computational resources than traditional models. Also, if there is not much data, traditional methods perform better since they need less training. If you choose to use deep learning, make sure your problem is suited for this type of model. That's about it! Hope you enjoyed reading…
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