Strengths: I found the authors' formulation of network pseudo-depth to be a very interesting and potentially useful metric for comparing artificial neural network models to neural data. I think their finding (displayed in Figure 2) that the number of sampled neurons had to be around at least 1000-2000 for the VGG-16 pseudo-depth to be consistently estimated, and that this finding holds when comparing representations against another network (VGG-19), demonstrates a potentially useful rule-of-thumb for adequate population sizes in neural data. Furthermore, their finding that mouse visual cortex is more parallel after a few stages of hierarchical processing starting at around area VISp, could be useful for building better task-driven models of mouse visual cortex, and indicates an important distinction with the traditional, hierarchical primate ventral visual pathway. Weaknesses: I would have liked to see more analyses of the robustness of the pseudo-depth metric with different networks, especially those not in the VGG family. I am aware that the Allen Institute has compared VGG-16/19 to their mouse data, and therefore, this is likely why the authors chose this model to begin with.

Acid mine drainage (AMD) is one of the common environmental problems in the coal mining industry that was formed by the oxidation of sulfide minerals in the overburden or waste rock. The prediction of acid generation through AMD is important to do in overburden management and planning the post-mining land use. One of the methods used to predict AMD is a lab-scale kinetic test to determine the rate of acid formation over time using representative samples in the field. However, this test requires a long-time procedure and large amount of chemical reagents lead to inefficient cost. On the other hand, there is potential for machine learning to learn the pattern behind the lab-scale kinetic test data. This study describes an approach to use artificial neural network (ANN) modeling to predict the result from lab-scale kinetic tests. Various ANN model is used based on 83 weeks experiments of lab-scale kinetic tests with 100\% potential acid-forming rock. The model approaches the monitoring of pH, ORP, conductivity, TDS, sulfate, and heavy metals (Fe and Mn). The overall Nash-Sutcliffe Efficiency (NSE) obtained in this study was 0.99 on training and validation data, indicating a strong correlation and accurate prediction compared to the actual lab-scale kinetic tests data. This show the ANN ability to learn patterns, trends, and seasonality from past data for accurate forecasting, thereby highlighting its significant contribution to solving AMD problems. This research is also expected to establish the foundation for a new approach to predict AMD, with time efficient, accurate, and cost-effectiveness in future applications.

In this article, we are going to build an artificial neural network using Tensor flow. We are going to build a deep neural network with multiple neurons and fully connected layers. The dataset we are going to work on belongs to a bank studying its customer to see if they will leave or stay and it contains around 10,000 observations. You will see that we will have to use the data preprocessing template we created in a previous article, and we will use a different set of tools as well. Here we will have an input vector containing a set of features and we will predict the outcome which will be a binary variable. As you know that ANN can be used for regression or classification and here we are going to do it for classification in this tutorial.

From a broad perspective as a software engineer a big part of my work consists in aligning expectations between stakeholders and creating code that transforms and forwards data. During the last 15 years I have developed software and feel fascinated by this part of computer sciences. Curiosity and interest in other topics from the same field; data sciences and machine learning, made me decide to start a simple hands-on project in order to learn and practice in a typical experiment oriented approach. After two months I had created a software different from the others I had developed so far, it "learned" from data and after that it could make "predictions", and correct ones. The software result is an International Bank Account Number (or IBAN) validator.

As credit markets continue to evolve, banks may take advantage of products which utilise machine learning – software which allows banks to anticipate risks more effectively. But should banks revise their credit risk management processes accordingly and employ these new solutions? According to McKinsey, AI and machine learning technologies could add up to $1 trillion in additional value to global banking every year. Financial institutions are using machine learning to make credit decisions more accurately and consistently while reducing risk, fraud, and costs. For example, Citi bank recently transformed its critical internal audit using machine learning--something that has contributed to high-quality credit decisions.

A team of researchers at the Graduate School of Informatics, Nagoya University, have brought us one step closer to the development of a neural network with metamemory through a computer-based evolution experiment. This type of neural network could help experts understand the evolution of metamemory, which could help develop artificial intelligence (AI) with a human-like mind. "In order to elucidate the evolutionary basis of the human mind and consciousness, it is important to understand metamemory," says Professor Arita. The team of researchers, which included Professor Takaya Arita, Yusuke Yamato, and Reiji Suzuki of the Graduate School of Informatics developed an artificial neural network model that performed the delayed matching-to-sample task and analyzed its behavior. The neural network was able to examine its memories, keep them, and separate outputs all without requiring assistance or human intervention.