**Artificial Neural Network (ANN) in Machine Learning**

Artificial Neural networks (ANN) or neural networks are computational algorithms. It intended to simulate the behavior of biological systems composed of “neurons”. ANNs are computational models inspired by an animal’s central nervous systems. It is capable of machine learning as well as pattern recognition. These presented as systems of interconnected “neurons” which can compute values from inputs. A neural network is an oriented graph. It consists of nodes which in the biological analogy represent neurons, connected by arcs. It corresponds to dendrites and synapses. Each arc associated with a weight while at each node. Apply the values received as input by the node and define Activation function along the incoming arcs, adjusted by the weights of the arcs. A neural network is a machine learning algorithm based on the model of a human neuron. The human brain consists of millions of neurons. It sends and process signals in the form of electrical and chemical signals. These neurons are connected with a special structure known as synapses. Synapses allow neurons to pass signals. From large numbers of simulated neurons neural networks forms.

**Deep Learning with TensorFlow in Python: Convolution Neural Nets**

The following problems appeared in the assignments in the Udacity course Deep Learning (by Google). The descriptions of the problems are taken from the assignments (continued from the last post). Classifying the letters with notMNIST dataset with Deep Network

**Bayesian Inference an Interactive Visualization**

The visualization shows a Bayesian two-sample t test, for simplicity the variance is assumed to be known. It illustrates both Bayesian estimation via the posterior distribution for the effect, and Bayesian hypothesis testing via Bayes factor. The frequentist p-value is also shown. The null hypothesis, H0 is that the effect d = 0, and the alternative H1: d ? 0, just like a two-tailed t test. You can use the sliders to vary the observed effect (Cohen’s d), sample size (n per group) and the prior on d.

**Driving Reliability and Improving Maintenance Outcomes with Machine Learning**

In this special guest feature, Mike Brooks, Senior Business Consultant at AspenTech, discusses how companies can no longer rely solely on traditional equipment maintenance practices but must also incorporate operational behaviors in deploying data-driven solutions using machine learning. Mike Brooks is the former Mtell President & COO. Mike’s professional background includes strategic roles with Chevron Technology Ventures and the Wonderware division of Schneider Electric. He is also a founder of INDX, provider of IT for process industry firms.

**Deep Learning Algorithms are Changing the Future. Are You Missing Out?**

Until recently, deep learning alluded to the big names in tech such as Amazon, Facebook, and Google as having a clear use for these tools. Whilst these are some of the key players in AI and DL implementation, there are also huge advantages for their applications in businesses and everyday enterprises.

**How Convolutional Neural Networks Accomplish Image Recognition?**

Image recognition is very interesting and challenging field of study. Here we explain concepts, applications and techniques of image recognition using Convolutional Neural Networks.

**Data Science for Water Utilities Using R**

Data science comes natural to water utilities because of the engineering competencies required to deliver clean and refreshing water. Many water managers I speak to are interested in a more systematic approach to creating value from data. My work in this area is gaining popularity. Two weeks ago I was the keynote speaker at an asset data conference in New Zealand. My paper about data science strategy for water utilities is the most downloaded paper this year. This week I am in Vietnam, assisting the local Phú Th? water company with their data science problems. In all my talks and publications I emphasise the importance of collaboration between utilities and that we should share code because we are all sharing the same problems. I am hoping to develop a global data science coalition for water services to achieve this goal.

**Tutorial: Deep Learning with R on Azure with Keras and CNTK**

Microsoft’s Cognitive Toolkit (better known as CNTK) is a commercial-grade and open-source framework for deep learning tasks. At present CNTK does not have a native R interface but can be accessed through Keras, a high-level API which wraps various deep learning backends including CNTK, TensorFlow, and Theano, for the convenience of modularizing deep neural network construction. The latest version of CNTK (2.1) supports Keras. The RStudio team has developed an R interface for Keras making it possible to run different deep learning backends, including CNTK, from within an R session.

**The Machine Learning Abstracts (Part 3): Support Vector Machines**

There is another machine learning algorithm which can be used for classification, Support Vector Machines (SVM).

**Preparing for the Transition to Applied AI**

A significant part of the Software Engineer role requires staying up-to-date with evolving frameworks, standards, and paradigms. Software Engineers strive to constantly learn, in order to always use the best tool for the job. As Machine Learning finds footholds in more applications every day, it has naturally become a topic that many Engineers want to master. Machine Learning, though, is harder to pick up than a new framework. To be an efficient practitioner, you require a solid understanding of the theory of the field, broad knowledge of the current state of the art, and an ability to frame problems in a non deterministic way. Many guides you can find online will simply teach you how to train an out-of-the-box model on a curated data set to achieve good accuracy and call it a day. The truth is that a much more extensive skillset is essential in becoming an effective Machine Learning Engineer. Below is a distillation of the many conversations we’ve had with over 50 top Machine Learning teams all over The Bay Area and New York, who’ve come to Insight to find AI Practitioners poised to tackle their problems and accelerate their expansion into Applied AI.

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