If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
But wait… What is Tensorflow? Tensorflow is a Deep Learning Framework by Google, which released its 2nd version in 2019. It is one of the world's most famous Deep Learning frameworks widely used by Industry Specialists and Researchers. Tensorflow v1 was difficult to use and understand as it was less Pythonic, but with v2 released with Keras now fully synchronized with Tensorflow.keras, it is easy to use, easy to learn, and simple to understand. Remember, this is not a post on Deep Learning so I expect you to be aware of Deep Learning terms and the basic ideas behind it.
Since TensorFlow 2.2, all this boiler plate code is no longer needed. You only need to tell TensorFlow how every single train step (and possibly test step) will look like. The rest is done inside the tf.keras.Model class. Let's not beat around the bush, here is the code: The example was far to simple to use them. But I hope it served its purpose in demonstrating the different approaches without the need to explain a complicated model. I would like to do than in some upcoming article.
In 2019, Google announced TensorFlow 2.0, it is a major leap from the existing TensorFlow 1.0. Ease of use: Many old libraries (example tf.contrib) were removed, and some consolidated. For example, in TensorFlow1.x the model could be made using Contrib, layers, Keras or estimators, so many options for the same task confused many new users. TensorFlow 2.0 promotes TensorFlow Keras for model experimentation and Estimators for scaled serving, and the two APIs are very convenient to use. The writing of code was divided into two parts: building the computational graph and later creating a session to execute it.
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From navigating to a new place to picking out new music, algorithms have laid the foundation for large parts of modern life. Similarly, artificial intelligence is booming because it automates and backs so many products and applications. Recently, I addressed some analytical applications for TensorFlow. In this article, I'm going to lay out a higher-level view of Google's TensorFlow deep learning framework, with the ultimate goal of helping you to understand and build deep learning algorithms from scratch. Over the past couple of decades, deep learning has evolved rapidly, leading to massive disruption in a range of industries and organizations. The term was coined in 1943 when Warren McCulloch and Walter Pitts created a computer model based on neural networks of a human brain, creating the first artificial neural networks (or ANNs). Backpropagation is a popular algorithm that has had a huge impact in the field of deep learning.
TensorFlow 2.0 is all about ease of use, and there has never been a better time to get started. In this talk, we will introduce model-building styles for beginners and experts, including the Sequential, Functional, and Subclassing APIs. We will share complete, end-to-end code examples in each style, covering topics from "Hello World" all the way up to advanced examples. At the end, we will point you to educational resources you can use to learn more.
Medical image segmentation is a hot topic in the deep learning community. Proof of that is the number of challenges, competitions, and research projects being conducted in this area, which only rises year over year. Among all the different approaches to this problem, U-Net has become the backbone of many of the top-performing solutions for both 2D and 3D segmentation tasks. This is due to its simplicity, versatility, and effectiveness. When practitioners are confronted with a new segmentation task, the first step commonly is to use an existent implementation of U-Net as a backbone.
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BESTSELLER, 4.7 (143 ratings), Created by Lazy Programmer Team, Lazy Programmer Inc. English [Auto-generated] It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version. Tensorflow is Google's library for deep learning and artificial intelligence. Deep Learning has been responsible for some amazing achievements recently, such as: Generating beautiful, photo-realistic images of people and things that never existed (GANs) Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning) Self-driving cars (Computer Vision) Speech recognition (e.g. Siri) and machine translation (Natural Language Processing) Even creating videos of people doing and saying things they never did (DeepFakes - a potentially nefarious application of deep learning) Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning.
Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. It acts as both a step-by-step tutorial, and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and working examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself.