Deep learning is a subfield of machine learning that has gained significant attention and demonstrated remarkable power in various domains. This interesting topic came to us from Embedded in their article, “Deep learning model optimization reduces embedded AI inference time.”

Deep learning utilizes neural networks with multiple layers, enabling the model to learn intricate patterns and representations from vast amounts of data. It has achieved remarkable success in various real-world applications. It has revolutionized computer vision tasks such as image classification, object detection, and image segmentation. Natural language processing tasks like language translation, sentiment analysis and text generation have also greatly benefited from deep learning techniques. Additionally, deep learning has made significant contributions to speech recognition, recommendation systems, autonomous vehicles, and many other fields.

Deep learning’s power is not without limitations. It typically requires large amounts of labeled data, significant computational resources, and careful model design and training. Nonetheless, the power of deep learning lies in its ability to automatically learn complex representations and make accurate predictions across a wide range of domains and applications.

Explainable AI allows users to comprehend and trust the results and output created by machine learning algorithms. Explainable AI is used to describe an AI model, its expected impact, and its potential biases. Why is this important? Because explainability becomes critical when the results can have an impact on data security or safety.

Melody K. Smith

Data Harmony is an award-winning semantic suite that leverages explainable AI.

Sponsored by Access Innovations, the intelligence and the technology behind world-class explainable AI solutions.