Deep learning-based processing and reconstruction of compromised biophotonic image data ... (PSF), signal-to-noise ratio (SNR), sampling density, and pixel resolution in biophotonic image data.

Deep learning models are typically powered with graphics processing units (GPUs), specialized chips, and other infrastructure components that can be quite expensive, especially at the scale that ...

Speech and Audio Processing: Applying deep learning models to tasks such as robust speech recognition (ASR), audio generation and synthesis, voice conversion, emotion recognition, speech translation.

Rohde & Schwarz is hosting a webinar that explores advanced solutions for classifying radar and EW signals using RF capture, ...

In announcing ML.NET 3.0 yesterday (Nov. 27), Microsoft emphasized two main points of interest, deep learning and data processing. Deep Learning This ML subset uses artificial neural networks loosely ...

When it comes to producing video, signal processing is central to the task. Simply stated, modern day signal processing encompasses the multitude of signal manipulations and modifications required to ...

However, most of these metaverse experiences will be able to continue to progress only with the use of deep learning (DL), as artificial intelligence (AI) and data science will be at the forefront ...

Course Type: Elective Specialization: Natural Language Processing: Deep Learning Meets Linguistics Instructors: Dr. Katharina von der Wense Prior knowledge needed: Students should consider their ...

Notably, deep learning capabilities have been significantly expanded with advancements in Object Detection, ML.NET version 3.0 has been officially released, introducing new features and enhancements.

Although deep learning has been around since the 1940s, the high cost and complexity of graphics processing units (GPUs) have kept the technology out of reach for many organizations.