{"id":19533,"date":"2022-12-02T09:00:00","date_gmt":"2022-12-02T08:00:00","guid":{"rendered":"https:\/\/www.codemotion.com\/magazine\/?p=19533"},"modified":"2022-12-01T12:30:36","modified_gmt":"2022-12-01T11:30:36","slug":"tensorflow-tutorial-an-essential-deep-learning-language","status":"publish","type":"post","link":"https:\/\/www.codemotion.com\/magazine\/ai-ml\/deep-learning\/tensorflow-tutorial-an-essential-deep-learning-language\/","title":{"rendered":"TensorFlow Tutorial: An Essential Deep Learning Language?"},"content":{"rendered":"\n

Why does it make sense to check out a TensorFlow Tutorial today? Well, Artificial intelligence (AI) is the future. Recently, we\u2019ve seen the emergence of the first driverless cars and developments such as Project InnerEye<\/a> have huge implications for healthcare. But it’s not all about the technology of tomorrow.\u00a0<\/p>\n\n\n\n

If you look closely you\u2019ll find evidence of AI in your day-to-day life. AI is everywhere, from smartwatches and automated lighting to film and product recommendations. AI is changing businesses too.<\/strong> We\u2019ve seen the introduction of the workflow orchestrator<\/a>. This has allowed for the coordination and management of systems across multiple cloud vendors and domains.\u00a0\u00a0<\/p>\n\n\n\n

The AI industry is expected to grow to $1,394.30 billion USD<\/a> by 2029. If you want to capitalize on this growth, innovation is key. Underpinning many recent developments is machine learning<\/strong>, a branch of AI that allows it to adapt. This article will look at Tensorflow<\/strong>, and how it can help you to build your own machine-learning models.\u00a0<\/p>\n\n\n\n

With Tensorflow on your side, you can be at the forefront of AI innovation. So, without further ado, let\u2019s learn more about the language. <\/p>\n\n\n\n

What is Tensorflow? <\/h2>\n\n\n\n

Tensorflow was developed in 2012 by the Google Brain Team. It\u2019s an open-source, end-to-platform designed to support innovations in machine learning. At the core of Tensorflow is usability. In fact, it’s often viewed as one of the easiest code development platforms to use.  <\/p>\n\n\n\n

One of the reasons for this is that it includes readily available APIs. This saves users the time-consuming task of editing certain sections of code. You\u2019ll spend less time training a model and find fewer errors in the program. The use of the IBM Mainframe AS400<\/a> can further enhance the use of APIs. <\/p>\n\n\n\n

Tensorflow has integration with multiple programming languages. This includes popular options such as C++ and Python, making development even quicker. It is also extremely scalable. Once code has been written, it can be run on either the CPU or GPU. <\/p>\n\n\n\n

Take a look at Azure Databricks TensorFlow<\/a> for even deeper insights into Tensorflow. <\/p>\n\n\n\n

Tensorflow offers a number of improvements to how we will access Web 3.0 storage<\/a> data.<\/p>\n\n\n\n

Machine Learning Vs Deep Learning<\/h2>\n\n\n\n

As we\u2019ve explained, machine learning has powered a great deal of recent AI development<\/a>. Deep learning is a subset of machine learning, an AI technology that can complete tasks without human intervention. Deep learning is essentially a more complex version of the former. <\/p>\n\n\n\n

In this system, the structure of AI is based on the human brain. Like the human mind, it can adapt to different circumstances and make independent decisions. Another difference is that deep learning relies on a large amount of unstructured data, perhaps stored in virtual servers<\/a>. Unstructured data can take many forms, including: <\/p>\n\n\n\n