Harnessing the power of AI’s image recognition and deep learning may significantly reduce the cost of visual quality control.
The tailored Team Training Tracks (4T) method is a way of teaching data science by working with a specific team on selected real-life projects in a company. It facilitates an effective learning experience for adults addressing an increasing job market need for data scientists.
How we teach data science now has many limitations. Universities, bootcamps and online courses have yet to provide an optimal learning experience that answers job market needs. A fourth way is needed.
The number of data scientists has grown over 650% over the past five years. Machine learning and deep learning skills are in huge demand at present, and the list of reasons you should join those already working in the profession is broad.
deepsense.ai ML team has been working with Google Brain on helping AI imagine and reason about the future. They started from optimizing TensorFlow’s infrastructure for reinforcement learning and moved to end-to-end training of AI entirely on Google’s newest Cloud TPUs.
Tired of overly theoretical introductions to deep learning? Experiment hands-on with CIFAR-10 image classification with Keras by running code in Neptune.
We’ve open sourced image classification sample solution that lets data scientists start competing in the currently running Kaggle Cdiscount competition.
Monitoring brand visibility is an important business problem. We describe our solution for logo detection and visibility analytics with deep learning.
We apply three different deep learning models to reproduce state-of-the-art results in single image super resolution.
We’re thrilled today to announce the latest version of Neptune: Machine Learning Lab. This release will allow data scientists using Neptune to take some giant steps forward. Here we take a quick look at each of them.