Deep Reinforcement Learning (RL) has made remarkable progress in the past years.

Deep Reinforcement Learning (RL) has made remarkable progress in the past years.

For all the hype around machine learning, its practical uses and what programmers and IT pros need to know is often left out of the conversation. The Machine Learning Mastery eBook Bundle provides a full and up-to-date training library on machine learning and its professional use cases. The 10-book set opens with three books for people new to the field with a volume on machine learning algorithms, mastery of those algorithms and a practical manual that shows you how to apply those algorithms to real-world data problems. A follow-up volume looks at the statistical basis for machine learning to help you build your own statistically sound tools. From there, three books look at Python's role in machine learning, with the first looking at Python libraries more generally and the second a more general advanced course using Python as the basis.

Accurate decomposition of the mixed Mn oxidation states is highly important for characterizing the electronic structures, charge transfer, and redox centers for electronic, electrocatalytic, and energy storage materials that contain Mn. Electron energy loss spectroscopy (EELS) and soft X-ray absorption spectroscopy (XAS) measurements of the Mn L2,3 edges are widely used for this purpose. To date, although the measurement of the Mn L2,3 edges is straightforward given the sample is prepared properly, an accurate decomposition of the mix valence states of Mn remains non-trivial. For both EELS and XAS, 2+, 3+, 4+ reference spectra need to be taken on the same instrument/beamline and preferably in the same experimental session because the instrumental resolution and the energy axis offset could vary from one session to another. To circumvent this hurdle, in this study, we adopted a deep learning approach and developed a calibration-free and reference-free method to decompose the oxidation state of Mn L2,3 edges for both EELS and XAS. To synthesize physics-informed and ground-truth labeled training datasets, we created a forward model that takes into account plural scattering, instrumentation broadening, noise, and energy axis offset. With that, we created a 1.2 million-spectrum database with a three-element oxidation state composition label. The library includes a sufficient variety of data including both EELS and XAS spectra. By training on this large database, our convolutional neural network achieves 85% accuracy on the validation dataset. We tested the model and found it is robust against noise (down to PSNR of 10) and plural scattering (up to t/{\lambda} = 1). We further validated the model against spectral data that were not used in training.

Welcome to SuperGradients, a free, open-source training library for PyTorch-based deep learning models. SuperGradients allows you to train or fine-tune SOTA pre-trained models for all the most commonly applied computer vision tasks with just one training library. We currently support object detection, image classification and semantic segmentation for videos and images. Easily load and fine-tune production-ready, pre-trained SOTA models that incorporate best practices and validated hyper-parameters for achieving best-in-class accuracy. Why do all the grind work, if we already did it for you?

Data-driven, model-free analytics are natural choices for discovery and forecasting of complex, nonlinear systems. Methods that operate in the system state-space require either an explicit multidimensional state-space, or, one approximated from available observations. Since observational data are frequently sampled with noise, it is possible that noise can corrupt the state-space representation degrading analytical performance. Here, we evaluate the synthesis of empirical mode decomposition with empirical dynamic modeling, which we term empirical mode modeling, to increase the information content of state-space representations in the presence of noise. Evaluation of a mathematical, and, an ecologically important geophysical application across three different state-space representations suggests that empirical mode modeling may be a useful technique for data-driven, model-free, state-space analysis in the presence of noise.

A hierarchical Bayesian classifier is trained at pixel scale with spectral data from the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) imagery. Its utility in detecting rare phases is demonstrated with new geologic discoveries near the Mars-2020 rover landing site. Akaganeite is found in sediments on the Jezero crater floor and in fluvial deposits at NE Syrtis. Jarosite and silica are found on the Jezero crater floor while chlorite-smectite and Al phyllosilicates are found in the Jezero crater walls. These detections point to a multi-stage, multi-chemistry history of water in Jezero crater and the surrounding region and provide new information for guiding the Mars-2020 rover's landed exploration. In particular, the akaganeite, silica, and jarosite in the floor deposits suggest either a later episode of salty, Fe-rich waters that post-date Jezero delta or groundwater alteration of portions of the Jezero sedimentary sequence.

He enrolled in the NYC Data Science Academy 17-week remote bootcamp program, taking place from January to April 2017. This post is based on his third class project focusing on web scraping in Python. The original article can be found here. Clean technology continues to undergo significant advancements spanning technology, sustainability, financial, and policy issues. Given the field's large scope, there is no shortage of news outlets covering the action.

Images used to be made by people, for people. Today, there's an entirely new kind of image: pictures taken by machines, for other machines to use. This new genre–created by cameras mounted on traffic lights, in shopping malls, on advertisements, and on computers and smartphones–is teaching computers how to see. "You have a moment where for the first time in history most of the images in the world are made by machines for other machines, and humans aren't even in the loop," says the Berlin-based artist Trevor Paglen. "I think the automation of vision is a much bigger deal than the invention of perspective."

He enrolled in the NYC Data Science Academy 17-week remote bootcamp program, taking place from January to April 2017. This post is based on his third class project focusing on web scraping in Python. The original article can be found here. Clean technology continues to undergo significant advancements spanning technology, sustainability, financial, and policy issues. Given the field's large scope, there is no shortage of news outlets covering the action.