AI Technologies

Deep Learning

Deep Learning is one of the more successful approaches to Machine Learning, capable of solving problems involving unstructured data or a large number of features with minimal user intervention.

What is Deep Learning

The what

Deep Learning is one of the more successful approaches to Machine Learning. Deep Learning uses artificial neural networks inspired by the human brain and it has the ability to automatically discover the best representations of the input data required for the task at hand. This ability, in conjunction with the ability to understand very complex and feature-rich patterns, makes Deep Learning an excellent approach for solving problems involving unstructured data or a large number of features with minimal user intervention.

Deep Learning is excellent for complex problems like speech recognition, image classification, and natural language processing.

Why You Need Deep Learning

The why

Traditional Machine Learning algorithms are beneficial when you have structured data and well-engineered features. Still, they’re limited because a human expert needs to select and transform raw data into features, or attributes that better represent an underlying problem.

Feature engineering is complex. It requires a significant amount of domain knowledge and a serious time investment – and the task becomes almost impossible when you have vast amounts of data added in a constant stream.

There are situations where Deep Learning is useful, and human manipulation is unrealistic:

When there is a large number of features

When there is a lack of domain understanding

There is a complex relationship between the features and the goal

When the data is unstructured like text, audio, images, or video

Deep Learning algorithms incrementally learn high-level features from data and eliminate the need for domain expertise.

How Deep Learning works

Deep Learning neural networks contain layers of nodes designed to behave like a human brain’s neurons. Nodes are interconnected, and signals traveling between them are assigned a number or “weight,” with a heavier weight exerting more effect on the next layer of nodes.

In Deep Learning, a machine learns to filter data through multiple layers – an input layer that receives input objects like text, images, or sounds, multiple hidden layers that compute and transform the data, and an output layer that assigns a particular outcome or prediction. “Deep” means there is more than one hidden layer.

Each layer transforms data from its original representation to a new representation that retains the important information to give the desired answer. This way, very complex data can be reduced to a smaller set of features that are critical to classify or predict. This property is called representation learning.

Two popular Deep Learning algorithms are Convolutional Neural Networks (CNN) used for object detection and image processing and Recurrent Neural Networks that “remember” past data to inform how they understand current events or predict the future.

How DL works

in Kepler

in Kepler

Kepler has many strategies and isn’t limited to using the same algorithm for all cases. Instead, it determines if Deep Learning is the right approach for you by analyzing the most important characteristics of your data.

Kepler automated pipeline builder capabilities not only choose the right Deep Learning architecture for your task but select the correct data preprocessing steps in order to maximize performance.

Kepler has multiple Deep Learning architectures from custom 1D and 2D Convolutional Neural Networks (CNN), Long Short Term Memory Networks (LSTM), AutoEncoders, Information Maximizing Self-Augmented Training (IMSAT), Pre-trained Resnet50, Pre-trained VGG, Pre-trained BERT, and Fully Connected Neural Networks.

Kepler offers Deep Learning capabilities to predict categories or quantities, forecast time series, segment data, or to detect anomalies.

Kepler Deep Learning algorithms are trained using GPUs to better manage compute requirements and to increase training speed.

Deep Learning applications

Using Deep Learning for predictive maintenance

A large manufacturer in the automotive industry is transforming its process following Industry 4.0 guidelines. Preventive maintenance requires monitoring the condition of an asset for early detection of any degradation and taking immediate action to fix the problem. Hundreds of sensors need to be continuously monitored, so doing this task by hand or using simple statistics is impossible – and anomalous behavior can’t be detected by the value in just one sensor but needs a non-linear combination of values. Kepler anomaly detection can monitor up to 2,000 variables in real-time. Kepler gets the proper alert when an anomaly is detected, and with the Kepler interpretability feature, you can know which sensors are the most influential for the prediction.

Predicting crop yield
with Deep Learning

An enterprise in the precision agriculture space needs to predict crop yield to do field mapping and for matching crop supply with demand. This prediction involves historical yield data and soil characteristics through IoT, weather information, seed prices, water requirements and availability, and even some macroeconomics factors. Kepler can predict the production yield by incorporating hundreds of different variables with far greater speed and accuracy than a human.

Customer onboarding
with Deep Learning

An insurance company uses Deep Learning to significantly reduce the time it takes to organize documents, detect missing information, and to send an automated email asking for more details during the customer onboarding and claims process. Kepler NLU capabilities use Deep Learning to categorize this information and Kepler anomaly detection capabilities can flag a claim that’s suspicious and automatically send it to a human reviewer. Back-office automation in any industry has the greatest potential for using machine learning, particularly Deep Learning. There are hundreds of routine tasks that employees perform every day, from categorizing documents, extracting key indicators from bank statements to assess customer risk level for a loan, or categorizing customers into different segments according to their financial situation.

Want to upskill your AI? We’ve got you:

In addition to platform training, our onboarding experience helps you build a foundation that helps you achieve success from Day 1:

AI primer on key data science terms and processes

Applied AI training on how to run a successful AI project

AI roadmap advice

How to pilot AI use cases

We also offer in-depth services related to data strategy and transformation, system integration and more.

Tap into the power of AI

With Kepler, it’s easy to answer complex questions using the power of machine learning and deep learning.

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