neural networks

It's Time to Start Your Ai Adventure

AI is a vast collection of technologies that creates a framework of capabilities. These capabilities enable machines to exhibit human-like levels of intelligence. In order to realize AI’s potential for value creation, you must have clear and concise problem statements, each with desired outcomes defined. The services we offer are designed to reinforce each other to ensure an alignment of strategy, structure, process and people in a domain that is rapidly changing.

deep learning

There is so much information about neural networks and their use in machine learning algorithms that we won’t repeat the fundamentals. Our mission is to focus on applied AI and, more specifically, applied deep learning. Neural Networks resemble the human brain and can model intricate patterns in datasets using multiple hidden layers and non-linear activation functions to get the training and learning process started.

There are three classes of neural networks

  • Multilayer Perceptrons (MLPs)
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)

MLPs are considered “classical” deep learning where the algorithm has one or more layers of neurons. Data is fed to the input layer and flows through one or more hidden layers. These layers can be enormous in number and hence “deep.” The predictions appear at the output or visible layer. MLPs work well when you have tabular datasets and need to do classification or regression prediction problems for image or text data and time-series data.

Convolutional neural networks (CNN) work for machine vision, image recognition, object detection, text classification, sentiment analysis, and pattern recognition.

Recurrent neural networks (RNN) work for natural language processing (NLP), handwriting, and speech recognition. Long Short-Term Memory (LSTM) networks, a type of RNN, can process entire data sequences, like video, and search for anomalies, especially in time-series data.

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