This article is an excerpt from the book Machine Learning with PyTorch and Scikit-Learn from the best-selling Python Machine Learning series, updated and expanded to cover PyTorch, transformers, and graph neural networks.

Broadly speaking, graphs represent a certain way we describe and capture relationships in data. Graphs are a particular kind of data structure that is nonlinear and abstract. And since graphs are abstract objects, a concrete representation needs to be defined so the graphs can be operated on. Furthermore, graphs can be defined to have certain properties that may require different representations. Figure 1 summarizes the common types of graphs, which we will discuss in more detail in the following subsections:
Common types of graph

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For decades Artificial Intelligence has been a focus of best-selling science fiction authors and an antagonist for blockbuster Hollywood movies. But AI is no longer relegated to the realm of science fiction, it inhabits the world around us. From the biggest enterprise companies to plucky startups, businesses everywhere are building and deploying AI at incredible speed. 

In fact, open source allows anyone with a laptop to build impressively good AI models in a day.

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The global artificial intelligence market is expected to reach over $200 billion by 2027. The big data market segment is anticipated to grow up to US$103 billion by 2027 with a share of 45% from the software segment. Similarly, the projected size of the global deep learning market will reach over $40 billion by 2027 at a CAGR of 39.2%.

Indeed, the implementation of technologies like data science, artificial intelligence, and machine learning in organizations has increased exponentially. In the last two years, during the pandemic outbreak, the technologies played a crucial role in saving lives and fostering economic resilience, showing many surprising trends. 

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Welcome to the third blog post in our “Kubeflow Fundamentals” series specifically designed for folks brand new to the Kubelfow project. The aim of the series is to walk you through a detailed introduction of Kubeflow, a deep-dive into the various components, and how they all come together to deliver a complete MLOps platform.

If you missed the previous installments in the “Kubeflow Fundamentals” series, you can find them here:

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Since the seminal paper “Attention Is All You Need” of Vaswani et al, transformer models have become by far the state of the art in NLP technology. With applications ranging from NER, text classification, question answering, or text generation, the applications of this amazing technology are limitless.

More specifically, BERT — which stands for Bidirectional Encoder Representations from Transformers — leverages the transformer architecture in a novel way. For example, BERT analyses both sides of the sentence with a randomly masked word to make a prediction. In addition to predicting the masked token, BERT predicts the sequence of the sentences by adding a classification token [CLS] at the beginning of the first sentence and tries to predict if the second sentence follows the first one by adding a separation token [SEP] between the two sentences.

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Today, most companies are using Python for AI and Machine Learning. With predictive analytics and pattern recognition becoming more popular than ever, Python development services are a priority for high-scale enterprises and startups. Python developers are in high-demand — mostly because of what can be achieved with the language. AI programming languages need to be powerful, scalable, and readable. Python code delivers on all three.

While there are other technology stacks available for AI-based projects, Python has turned out to be the best programming language for this purpose. It offers great libraries and frameworks for AI and Machine Learning (ML), as well as computational capabilities, statistical calculations, scientific computing, and much more. 

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Robotic Process Automation, better known as RPA, is an exciting issue among the C-suite and is rapidly making progress across many businesses. Innovation has filled up rapidly in the past few years, as have debates regarding automation and other related breakthrough advances, including Artificial Intelligence.

The concept is expectable, considering how automation tools and technologies are closely linked. RPA has always been destined to be the future of automation. If you are continually feeling stressed over your business operations’ efficiency, utilizing RPA could be the ideal decision.

Top Robotic Process Automation Trends for 2021

In this section, we will explain some of the top RPA trends businesses should look out for in 2021 and beyond.

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Machine learning-based applications have seen significant commercial success in several mainstream consumer applications in the recent past. Self-driving cars, stock-trading bots, robo-advisors, Amazon’s Alexa, and Apple’s Deep Fusion and Siri are some of the renowned examples of commercial success with artificial intelligence and machine learning. AI has also made our lives easier by improving the customer experience of the products we use. Google’s text generation software, Netflix’s recommendation engine, and Facebook and Twitter’s fake news detection are other prime examples. In fact, every single technology company uses AI in its mainstream applications either directly or indirectly. Non-technology companies are also using AI to improve customer experience, improve efficiency, and generate new revenue streams. Chatbots, robo-advisors, systems that predict system failures, and products that generate efficient supply chain routes are some of the prominent ways in which non-technology companies use AI. This is leads to a popular belief that AI and ML are primarily used by technology companies or they are being used by non-tech companies to build AI-based products.

This popular perception is not true. There are plenty of avenues in which AI/ ML is being used or can be used by non-tech and non-product-based groups to generate insights. In this article, I am going to share with you four ways in which you can augment advanced analytics into your analytics strategy to generate insights.

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Although still in its infancy, 2020 has been a year of significant growth for Natural Language Processing (NLP). In fact, research from Gradient Flow found that even in the wake of the COVID-19 pandemic, 53% of technical leaders indicated their NLP budget was at least 10% higher compared to 2019, with 31% stating their budget was at least 30% higher than the previous year. This is quite significant, given most companies are experiencing a downturn in IT budgets, as companies adjusted their spending in response to the pandemic. 

With the power to help streamline and even automate tasks across industries, from finance and healthcare to retail and sales, leaders are just beginning to reap the benefits of NLP. As the technology advances further and its value becomes more widely known, NLP can achieve outcomes from handling customer service queries to more mission-critical tasks, like detecting and preventing adverse drug events in a clinical setting. As NLP continues on its growth trajectory, here are some of the top trends to watch in 2021. 

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