Deep learning is a term that many of us working in the technology space would most definitely have come across at some point in time. An elevator pitch on deep learning: deep learning is an aspect of Artificial Intelligence (AI) that is emulating the learning approach that humans use to learn.
The term ‘deep’ is coined by the number of layers by which data should be processed. In deep learning, each deep learning algorithm is stacked in a hierarchy of increasing complexity, while traditional Machine Learning (ML) algorithms are linear. Deep learning applies a non-linear transformation on its input and uses what it learns, to create a statistical model as output. In traditional machine learning, the learning process has to be supervised where the programmer instructs the computer for the type of things it should be identifying and seeking. This process is laborious and the success is heavily dependent on the programmer to describe the feature.
The advantage of deep learning is that the program builds a feature set by itself, defined by a predictive model without supervision, leading to increased accuracy. Deep learning algorithms can create statistical models directly from their own iterative process from large quantities of unstructured data. With each iteration, the program becomes more complex and iterations are continued till output has reached a considerable level of accuracy. This behavior mimics human neurons hence the name deep neural learning or deep learning.
Deep learning algorithms today have access to loads of training data and processing power, which were unavailable before the era of cloud computing and big data. This means that today, deep learning is the fulcrum of everything in technology using AI, as IOT continues to become all pervasive generating huge quantities of unstructured, unlabeled data.
A neural network is a kind of advanced machine learning algorithm. Neural networks come in several different forms like recurrent neural networks, convolutional neural networks, artificial neural networks and feed forward neural networks. Each has their benefit for specific use cases. However, they all function in somewhat similar ways, essentially by feeding data in and letting the model, figure out for itself whether it has made the right interpretation or decision about a given data element.
Neural networks involve a trial-and-error process hence they need massive amounts of data to train on. It’s no coincidence that neural networks became popular only after most enterprises embraced big data analytics and accumulated large stores of data. In fact, successful DevOps practices generate huge amounts of data which can be optimized through deep learning and drive efficiencies in Continuous Testing or Continuous Delivery. DigitalOnUs has the requisite expertise to manage DevOps through deep learning.
Deep learning model’s first few iterations involve somewhat-educated guesses on the contents of image or parts of speech, the data used during the training stage must be labelled so the model can see if its guess is accurate. This means that, though many enterprises that use big data have large amounts of data, unstructured data is less helpful. Unstructured data can be analyzed by a deep learning model once it has been trained and reaches an acceptable level of accuracy, but deep learning models can’t train on unstructured data.
Use cases for deep learning
Deep learning applications in business are wide-ranging from industry to online retail to photography. Some popular use cases are enlisted below:
- Image recognition used for analyzing pictures and documents on large databases
- Natural Language Processing (NLP) used for sentiment analysis, machine translation, negative sentiment analysis
- Speech recognition used by Alexa, Siri or Cortona
- CRM used for automated marketing practices
- Recommendation engines used for a variety of applications
- Prediction for gene ontology and gene function relationship
- Wearables and ERMs data which is used for health predictions
Limitations of deep learning
The greatest limitation of deep learning models is that they learn through observations. This means they only understand and know about the data they trained on. If a user has a small amount of data or it comes from one specific source that is not necessarily representative of the broader functional area, the models will not learn in a way that can be standard or generalized.
The issue of biases is also a major problem for deep learning models. If a model trains on data that contains biases, the model will reproduce those biases in its predictions. This has been a vexing problem for deep learning programmers because models learn to differentiate based on subtle variations in data elements. This means, for example, that a facial recognition model might make determinations about people’s characteristics based on things like race or gender without the programmer being aware.
Deep learning has pervaded the global business landscape, capturing the undivided attention of industry giants like IBM, Facebook, Google, Microsoft, Twitter, PayPal, or Yahoo, among others. Large or small companies are all making heavy investments into deep learning technologies, because industry and tech pundits think such advances will be core drivers of enterprise growth, far into the future.