NIR Calibration-Model Services Develop & Optimize NIR chemometric methods for Chemical Analysis with ease LINK Do you want better NIRS prediction results? Check out their product page … link Get the Chemometrics and Spectroscopy News in real time on Twitter @ CalibModel and follow us. Near-Infrared Spectroscopy (NIRS) "Visible and Near-Infrared (VNIR) Hyperspectral Payload Electronics" LINK "A Real-Time Rapid Analysis Method for the Determination of Total Alkaloids in Fritillariae Cirrhosae Bulbus by AOTF-NIR" LINK "Feasibility of NIR spectroscopy detection of moisture content in coco-peat substrate based on the optimization characteristic variables" LINK "Location, year, and tree age impact NIR-based postharvest prediction of dry matter concentration for 58 apple accessions" LINK "FT-NIRS Coupled with PLS Regression as a Complement to HPLC Routine Analysis of Caffeine in Tea Samples." LINK "Vibrational coupling to hydration shell–Mechanism to performance enhancement of qualitative analysis in NIR spectroscopy of carbohydrates in aqueous environment" LINK "Determination of metmyoglobin in cooked tan mutton using Vis/NIR hyperspectral imaging system" LINK Infrared Spectroscopy (IR) and Near-Infrared Spectroscopy (NIR) "Deflected Talbot mediated overtone spectroscopy in near-infrared as a label-free sensor on a chip." LINK "Discrimination of Tetrastigma hemsleyanum according to geographical origin by near-infrared spectroscopy combined with a deep learning approach."

Vibing Out TensorFlow - We all know that there is a huge demand for qualified data scientists in the industry. So how are we exposing the world of data science to students? We all know that there is a huge demand for qualified data scientists in the industry. So how are we exposing the world of data science to students? I think it's important for students to be aware of industry standard tools from the beginning to help shape the way they learn and think about problems.

In this Guided Project, you will: … Build and train a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend. Assess the performance of trained CNN and ensure its generalization using various Key performance indicators. Build and train a Convolutional Neural Network using Keras with Tensorflow 2.0 as a backend. Assess the performance of trained CNN and ensure its generalization using various Key performance indicators. In this 1-hour long project-based course, you will be able to: – Understand the theory and intuition behind Convolutional Neural Networks (CNNs).

Artificial Intelligence will change lives. It will change the economy. It will change the world. You hear about it on the news and you see Google and other tech companies come out with this ridiculously advanced products which make you think "Oh god, in 20 years I'll have a terminator roaming around in my neighborhood". Now, while I can't say that that won't happen I can at least say that this is not at all the case with today's technology.

The idea of creating intelligent systems has always fascinated data science professionals. The advent of computers and technology uplifts the notion that an algorithm that can learn from itself and adapt to changing model inputs. The art of self-learning algorithms helping data science with valuable information is an uncharted territory that AI-powered neural networks would like to explore more, courtesy the growing interest of professionals and technology experts alike. To understand the complexities of Artificial Neural Networks (ANNs) lets first decode how our brain learns and relearns from different experiences. The human brain is made up of interconnected networks, these are called neurons.

Trust is a key factor in the implementation of deep learning applications. From training to optimization, the lifecycle of a deep learning model is tied to trusted data exchanges between different parties. That dynamic is certainly effective for a lab environment but results vulnerable to several all sorts of security attacks that manipulate the trusted relationships between the different participants in a model. Let's take the example of a credit scoring model based that uses financial transaction to classify the credit risk for a specific customer. The traditional mechanisms for training or optimizing a model assume that the entities performing those actions will have full access to those financial datasets which opens the door to all sorts of privacy risks.

The free energy plays a fundamental role in descriptions of many systems in continuum physics. Notably, in multiphysics applications, it encodes thermodynamic coupling between different fields. It thereby gives rise to driving forces on the dynamics of interaction between the constituent phenomena. In mechano-chemically interacting materials systems, even consideration of only compositions, order parameters and strains can render the free energy to be reasonably high-dimensional. In proposing the free energy as a paradigm for scale bridging, we have previously exploited neural networks for their representation of such high-dimensional functions. Specifically, we have developed an integrable deep neural network (IDNN) that can be trained to free energy derivative data obtained from atomic scale models and statistical mechanics, then analytically integrated to recover a free energy density function. The motivation comes from the statistical mechanics formalism, in which certain free energy derivatives are accessible for control of the system, rather than the free energy itself. Our current work combines the IDNN with an active learning workflow to improve sampling of the free energy derivative data in a high-dimensional input space. Treated as input-output maps, machine learning accommodates role reversals between independent and dependent quantities as the mathematical descriptions change with scale bridging. As a prototypical system we focus on Ni-Al. Phase field simulations using the resulting IDNN representation for the free energy density of Ni-Al demonstrate that the appropriate physics of the material have been learned. To the best of our knowledge, this represents the most complete treatment of scale bridging, using the free energy for a practical materials system, that starts with electronic structure calculations and proceeds through statistical mechanics to continuum physics.

Convolutional neural networks (CNN) are utilized to encode the relation between initial configurations of obstacles and three fundamental quantities in porous media: porosity ($\varphi$), permeability $k$, and tortuosity ($T$). The two-dimensional systems with obstacles are considered. The fluid flow through a porous medium is simulated with the lattice Boltzmann method. It is demonstrated that the CNNs are able to predict the porosity, permeability, and tortuosity with good accuracy. With the usage of the CNN models, the relation between $T$ and $\varphi$ has been reproduced and compared with the empirical estimate. The analysis has been performed for the systems with $\varphi \in (0.37,0.99)$ which covers five orders of magnitude span for permeability $k \in (0.78, 2.1\times 10^5)$ and tortuosity $T \in (1.03,2.74)$.

Many dynamic processes, including common scenarios in robotic control and reinforcement learning (RL), involve a set of interacting subprocesses. Though the subprocesses are not independent, their interactions are often sparse, and the dynamics at any given time step can often be decomposed into locally independent causal mechanisms. Such local causal structures can be leveraged to improve the sample efficiency of sequence prediction and off-policy reinforcement learning. We formalize this by introducing local causal models (LCMs), which are induced from a global causal model by conditioning on a subset of the state space. We propose an approach to inferring these structures given an object-oriented state representation, as well as a novel algorithm for model-free Counterfactual Data Augmentation (CoDA). CoDA uses local structures and an experience replay to generate counterfactual experiences that are causally valid in the global model. We find that CoDA significantly improves the performance of RL agents in locally factored tasks, including the batch-constrained and goal-conditioned settings.

With the starting point that implicit human biases are reflected in the statistical regularities of language, it is possible to measure biases in static word embeddings. With recent advances in natural language processing, state-of-the-art neural language models generate dynamic word embeddings dependent on the context in which the word appears. Current methods of measuring social and intersectional biases in these contextualized word embeddings rely on the effect magnitudes of bias in a small set of pre-defined sentence templates. We propose a new comprehensive method, Contextualized Embedding Association Test (CEAT), based on the distribution of 10,000 pooled effect magnitudes of bias in embedding variations and a random-effects model, dispensing with templates. Experiments on social and intersectional biases show that CEAT finds evidence of all tested biases and provides comprehensive information on the variability of effect magnitudes of the same bias in different contexts. Furthermore, we develop two methods, Intersectional Bias Detection (IBD) and Emergent Intersectional Bias Detection (EIBD), to automatically identify the intersectional biases and emergent intersectional biases from static word embeddings in addition to measuring them in contextualized word embeddings. We present the first algorithmic bias detection findings on how intersectional group members are associated with unique emergent biases that do not overlap with the biases of their constituent minority identities. IBD achieves an accuracy of 81.6% and 82.7%, respectively, when detecting the intersectional biases of African American females and Mexican American females. EIBD reaches an accuracy of 84.7% and 65.3%, respectively, when detecting the emergent intersectional biases unique to African American females and Mexican American females (random correct identification probability ranges from 1.0% to 25.5%).