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Getting Started with TensorFlow 2 - KDnuggets

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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.


TensorFlow 2.2+ and Custom Training Logic

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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.


TensorFlow 1.x vs 2.x. – summary of changes

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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.


Complete Machine Learning and Data Science: Zero to Mastery

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Created by Andrei Neagoie, Daniel Bourke Students also bought Machine Learning A-Z: Hands-On Python & R In Data Science Data Science A-Z: Real-Life Data Science Exercises Included Machine Learning, Data Science and Deep Learning with Python Statistics for Data Science and Business Analysis Data Science 2020: Complete Data Science & Machine Learning Preview this Udemy Course GET COUPON CODE Description This is a brand new Machine Learning and Data Science course just launched January 2020 and updated this month with the latest trends and skills! Become a complete Data Scientist and Machine Learning engineer! Join a live online community of 270,000 engineers and a course taught by industry experts that have actually worked for large companies in places like Silicon Valley and Toronto. Graduates of Andrei's courses are now working at Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, other top tech companies. Learn Data Science and Machine Learning from scratch, get hired, and have fun along the way with the most modern, up-to-date Data Science course on Udemy (we use the latest version of Python, Tensorflow 2.0 and other libraries).


A Lightning-Fast Introduction to Deep Learning and TensorFlow 2.0

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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.


Introduction to TensorFlow 2.0 for Beginners and experts alike

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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.


Accelerating Medical Image Segmentation with NVIDIA Tensor Cores and TensorFlow 2 NVIDIA Developer Blog

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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.


Artificial Intelligence for Business

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Online Courses Udemy Artificial Intelligence for Business, Solve Real World Business Problems with AI Solutions Created by Hadelin de Ponteves, Kirill Eremenko, SuperDataScience Team English [Auto-generated], French [Auto-generated], 5 more Students also bought Data Science: Natural Language Processing (NLP) in Python Deep Learning: Advanced Computer Vision (GANs, SSD, More!) Tensorflow 2.0: Deep Learning and Artificial Intelligence Machine Learning Practical: 6 Real-World Applications Artificial Intelligence: Reinforcement Learning in Python Preview this course GET COUPON CODE Description Structure of the course: Part 1 - Optimizing Business Processes Case Study: Optimizing the Flows in an E-Commerce Warehouse AI Solution: Q-Learning Part 2 - Minimizing Costs Case Study: Minimizing the Costs in Energy Consumption of a Data Center AI Solution: Deep Q-Learning Part 3 - Maximizing Revenues Case Study: Maximizing Revenue of an Online Retail Business AI Solution: Thompson Sampling Real World Business Applications: With Artificial Intelligence, you can do three main things for any business: Optimize Business Processes Minimize Costs Maximize Revenues We will show you exactly how to succeed these applications, through Real World Business case studies. And for each of these applications we will build a separate AI to solve the challenge. In Part 1 - Optimizing Processes, we will build an AI that will optimize the flows in an E-Commerce warehouse. In Part 2 - Minimizing Costs, we will build a more advanced AI that will minimize the costs in energy consumption of a data center by more than 50%! Just as Google did last year thanks to DeepMind.


Tensorflow 2.0: Deep Learning and Artificial Intelligence

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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.


Python Machine Learning - Third Edition - Free PDF Download

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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.