With the boom in tools for developing, monitoring and maintaining ML models, data science teams have many options to choose from. Proprietary tools provided by cloud service providers are enticing, but teams may fear being locked in—and may worry that […]
It’s clear that the number of sensors per vehicle—and the sophistication of these sensors—is growing rapidly, largely thanks to increased adoption of advanced safety and driver assistance features. In this presentation, we will explore likely future demand for automotive radars, […]
Edge computing opens up a new world of use cases for deep learning across numerous markets, including manufacturing, transportation, healthcare and retail. Edge deployments also pose new challenges for machine learning, not seen in cloud deployments. Constrained resources, tight latency […]
In this intermediate-level presentation, we will delve into some of the most common problems that arise when training deep neural networks. We’ll provide a brief overview of essential training metrics, including accuracy, precision, false positives, false negatives and F1 score. […]
One of the main challenges when deploying computer vision models to the edge is optimizing the model for speed, memory and energy consumption. In this talk, we’ll provide a comprehensive survey of model compression approaches, which are crucial for harnessing […]
You have a killer idea that will change the world. You’ve thought through product-market fit and differentiation. You have seed funding and a world-beating team. Best of all, you’ve caught the attention of major players in your industry. You’ve reached […]
Offloading visual processing to a hardware accelerator has many advantages for embedded vision systems. Decoupling hardware and software removes barriers to innovation and shortens time to market. Using hardware acceleration, embedded vision system manufacturers can integrate and deploy new components […]
Generative adversarial networks, or GANs, are widely used to create amazing “fake” images and realistic, synthetic training data. And yet, despite their name, mainstream GANs generate only the examples that are easiest to find, rather than the promised adversaries, which […]
The neural network models used in embedded real-time applications are evolving quickly. Transformer networks are a deep learning approach that has become dominant for natural language processing and other time-dependent, series data applications. Now, transformer-based deep learning network architectures are […]
The venture capital environment has undergone a huge shift in the past year. In this talk, we’ll explain how the VC landscape has changed and the implications for start-ups. We will examine the impacts on valuations, VC’s willingness to fund, […]
