Through exposure to the news and social media, you are probably familiar with the fact that machine learning has become one of the most exciting technologies of our time. While it may seem that machine learning has become the buzzword of our age, it is certainly not just hype. This exciting field opens up the way to new possibilities and has become indispensable to our daily lives.

Being exposed to practical code examples and working through example applications

of machine…

Thomas Edison famously said that success is 90% perspiration and 10% inspiration. That’s nowhere truer than in machine learning. Even though the hype around artificial intelligence has never been higher, the reality of what it takes to actually work in the field - and what it takes to use it successfully - is mired in confusion.

Indeed, hype makes it look like 100% inspiration; it hides the work involved in building knowledge and learning skills.

So, to help tackle that, here…

Deep Learning an emerging branch of machine learning, has garnered a lot of recognition in the field of technology over the last decade. It is regarded as a game-changer in AI, with distinct progress in computer vision, natural language processing (NLP), speech and other areas of machine learning. This year an Indeed survey found ‘deep learning engineer’ to be the best job in tech positions in the USA.

Though deep learning has many benefits and a very…

In this post we will talk about the Kepler dataset from Kaggle competitions and use it to build an exoplanet detection model using TensorFlow's prebuilt estimator for gradient boosting trees known as the BoostedTreesClassifier.

Detecting exoplanets in outer space

For the project explained in this post, we use the Kepler labeled time series data from Kaggle. This dataset is derived mainly from the Campaign 3 observations of the mission by…

Did you ever wonder why linear regression plays an important role in statistics and machine learning? It is witnessed that linear regression is one of the most commonly and well-understood algorithms. 

Regression is a statistical method for calculating relationships among variables. It is one of the most popular and simplest regression…

In order to be a highly efficient, flexible, and production-ready library, TensorFlow uses dataflow graphs to represent computation in terms of the relationships between individual operations. Dataflow is a programming model widely used in parallel computing and, in a dataflow graph, the nodes represent units of computation while the edges represent the data consumed or produced by a computation unit.

This post is taken from the book Hands-On Neural Networks with TensorFlow…

Keras [Chollet, François. "Keras (2015)." (2017)] is a popular deep learning library with over 250,000 developers at the time of writing, and over 600 active contributors. This library is dedicated to accelerating the implementation of deep learning models. This makes Keras ideal when we want to be practical and hands-on.

Keras enables us to build and train models efficiently. In the library, layers are connected…

Since the start of the renaissance era, technology started to take over the lives of the humans and to gown to greater heights in the coming decade with completely replacing man from few spots in terms of the manual work or heavy work, they most influenced sector was the industrial sector. Since, it’s more oriented to the manual work which has rapidly changed the face of the industrial sector.

But now when the machines already captured most of the manual workspaces from man, now they…

Face recognition is a combination of two major operations: face detection followed by Face classification. In this tutorial, we will look into a specific use case of object detection – face recognition.

The pipeline for the concerned project is as follows:

1. Face detection: Look at an image and find all the possible faces in it
2. Face extraction: Focus on each…

Aided by the availability of vast amounts of data computing resources, machine learning (ML) has made big strides. The financial industry, which at its heart is an information processing enterprise, holds an enormous amount of opportunity for the deployment of these new technologies.

Machine Learning for Finance is a practical guide to modern ML applied in the financial industry. This book is not only about investing or trading in the finance sector;…

A neural network is a series of algorithms that aims to identify underlying relationships in a set of data through a process that is similar to the way the human brain functions.

Keras is an open-source library written in Python for advancing and evaluating deep learning models. It enables you to define and train neural network models in a few lines of code. In this post, we will learn how to build a neural network using…

All the data we need today is already available on the internet, which is great news for data scientists. The only barrier to using this data is the ability to access it. There are some platforms that even include APIs (such as Twitter) that support data collection from web pages, but it is not possible to crawl most web pages using this advantage..

This…

PyTorch is known for being a clean framework, and hence it was a challenging task to achieve the production capability and flexibility needed for research. I think that the major hurdle for pushing production support to the core was going out of Python's realm and moving the PyTorch model to a faster, thread-safe language that has multithreading capability. But then, that violated the Python-first principle that PyTorch had up to that…

It has been popularly noted that artificial intelligence would be like the ultimate version of Google. With recent advancements in research and technology, Artificial Intelligence (AI) and Machine Learning (ML) are slowly becoming a part of our routine.

The pace at which technology is growing is unfathomable. As these smart technologies engulf our life, staying updated with them is the need of the day. So, here’s Packt’s selection of finest books in artificial intelligence and machine…

This article by Chanchal Singh and Manish Kumar will delve into some of the common MapReduce patterns that will help you work with Hadoop. Chanchal Singh has more than five years of experience in product development and architect design, and Manish Kumar is a technical architect with more than ten years of experience in data management, working as a data architect and product architect.

The design patterns are the solution templates for solving specific problems. Developers…

The diverse fields in which machine learning has proven its worth is nothing short of amazing. At the heart of machine learning are the various algorithms it employs to classify data and predict outcomes. This article highlights two greedy classifiers that, albeit simple, can be extremely powerful in their own right.

This article is an excerpt from the book…

Learn about Microsoft's network architecture and products in this article by Florian Klaffenbach, a technology solution professional at Microsoft who is a well-known expert when it comes to hybrid cloud scenarios, cloud connectivity, and cloud environment optimization.

This article will take a closer look at Microsoft's connectivity options and how you can connect to Microsoft Cloud Services. You will learn about Microsoft's points of presence for internet peering and…

This article delves in some of the widespread Python web frameworks and what makes them popular today.

Python is one of the most popular and versatile programming languages. There are thousands of Python packages, and these allow you to extend Python capabilities to any domain,be it web development, Internet of Things (IoT), artificial intelligence, or machine learning, and scientific computing. We can work with many different web frameworks and packages to…

Learn about CART in this guest post by Jillur Quddus, a lead technical architect, polyglot software engineer and data scientist with over 10 years of hands-on experience in architecting and engineering distributed, scalable, high-performance, and secure solutions used to combat serious organized crime, cybercrime, and fraud.

Although both linear regression models allow and logistic regression models allow us to predict a categorical outcome, both of these models assume…

Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and…