We can see the influence of machine-learning algorithms in social media, web search engines, mobile device spell checkers and self-driving cars. As the name suggests we will mainly focus on practical aspects of ML that involves writing code in Python with TensorFlow 2.0 API. It has a comprehensive, flexible ecosystem of tools , libraries , and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. Through practical applications and guided homework assignments, we'll develop and train neural networks using TensorFlow, Google's machine intelligence library. This will be an applied Machine Learning Course jointly offered by Google and IIT Madras. This article won’t teach you the theory or the mathematics behind machine learning. Practical Machine Learning with Tensorflow. As the name suggests we will mainly focus on practical aspects of ML that involves writing code in Python with TensorFlow 2.0 API. Machine Learning Anywhere. Practical-Machine-Learning-with-Tensorflow / Assignment Explanations / Assignment 3 - NPTEL Tensorflow Explanations.pdf Find file Copy path Fetching contributors… Master Tensorflow 2.0, Google’s most powerful Machine Learning Library, with 10 practical projects 4.4 (218 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. Practical Deep Learning is designed to meet the needs of competent professionals, already working as engineers or computer programmers, who are looking for a solid introduction to the subject of deep learning training and inference combined with sufficient practical, hands-on training to enable them to start implementing their own deep learning systems. Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. Teilnehmende sollten von einem Zeitaufwand von 3 bis 6 Stunden pro Woche ausgehen. Welcome to Practical Machine Learning with TensorFlow 2.0 MOOC. A core strength of TensorFlow has always been the ability to deploy machine learning applications to production anywhere. Machine learning models using Tensorflow 2.0. TensorFlow 2.0 is designed to make building neural networks for machine learning easy, which is why TensorFlow 2.0 uses an API called Keras. Contribute to SURE18/Practical-Machine-Learning-with-Tensorflow development by creating an account on GitHub. This type of learning is a different aspect of machine learning from the classical supervised and unsupervised paradigms. Practical-Machine-Learning-with-Tensorflow TensorFlow is an end-to-end open source platform for machine learning. The Machine Learning Crash Course with TensorFlow APIs is a self-study guide for aspiring machine learning practitioners. Read chapters 1-4 to understand the fundamentals of ML from a programmer’s perspective. Wir laden alle an Machine Learning und künstlicher Intelligenz interessierten Personen ein, unseren Kurs zu belegen. Above all, TensorFlow helps you solve challenging, real-world problems with machine learning.