Intro to TensorFlow

Start Date: 07/05/2020

Course Type: Common Course

Course Link:

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About Course

We introduce low-level TensorFlow and work our way through the necessary concepts and APIs so as to be able to write distributed machine learning models. Given a TensorFlow model, we explain how to scale out the training of that model and offer high-performance predictions using Cloud Machine Learning Engine. Course Objectives: Create machine learning models in TensorFlow Use the TensorFlow libraries to solve numerical problems Troubleshoot and debug common TensorFlow code pitfalls Use tf.estimator to create, train, and evaluate an ML model Train, deploy, and productionalize ML models at scale with Cloud ML Engine

Course Syllabus

The tool we will use to write machine learning programs is TensorFlow and so in this course, we will introduce you to TensorFlow. In the first course, you learned how to formulate business problems as machine learning problems and in the second course, you learned how machine works in practice and how to create datasets that you can use for machine learning. Now that you have the data in place, you are ready to get started writing machine learning programs.

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Course Introduction

Intro to TensorFlow This course is going to help you get up to speed on the state-of-the-art in machine learning, and cover most of the topics covered in an introductory week of machine learning courses. We are going to cover modeling, optimization, and inference, which are all foundational topics in machine learning. We will cover most of the topics covered in an introductory week of machine learning courses, including: - Modeling, through optimization problems, in TensorFlow - Preprocessing, preloading, and optimizing problems - Compilation and debugging - The introduction to inference, and the problems and challenges it provides - Encouragement of inference for machine learning - Learning common problems, and applications - Networking and embedding You will learn everything needed to get started with these topics covered in a week. For each topic, we will take a deep dive into the literature and lecture on a topic and method that you can use. We will also do some examples, and then discuss how these methods can be applied to solve a problem. We’ll also cover the theoretical ground rules that you need to know to operate on these topics, and then go into more in-depth training and optimization problems. Before we get started, let's make sure we have some background information about ourselves. First, let's take a look at what "Machine Learning" is all about. You'll learn what a "machine learning" is,

Course Tag

Application Programming Interfaces (API) Estimator Machine Learning Tensorflow Cloud Computing

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TensorFlow TensorFlow computations are expressed as stateful dataflow graphs. The name TensorFlow derives from the operations which such neural networks perform on multidimensional data arrays. These multidimensional arrays are referred to as "tensors". In June 2016, Google's Jeff Dean stated that 1,500 repositories on GitHub mentioned TensorFlow, of which only 5 were from Google.
TensorFlow TensorFlow is Google Brain's second generation machine learning system, released as open source software on November 9, 2015. While the reference implementation runs on single devices, TensorFlow can run on multiple CPUs and GPUs (with optional CUDA extensions for general-purpose computing on graphics processing units). TensorFlow is available on 64-bit Linux, macOS, and mobile computing platforms including Android and iOS.
TensorFlow TensorFlow provides a Python API, as well as somewhat less documented C++, Java and Go APIs.
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TensorFlow TensorFlow is an open source software library for machine learning across a range of tasks, and developed by Google to meet their needs for systems capable of building and training neural networks to detect and decipher patterns and correlations, analogous to the learning and reasoning which humans use. It is currently used for both research and production at Google products,   often replacing the role of its closed-source predecessor, DistBelief. TensorFlow was originally developed by the Google Brain team for internal Google use before being released under the Apache 2.0 open source license on November 9, 2015.
TensorFlow Google assigned multiple computer scientists, including Jeff Dean, to simplify and refactor the codebase of DistBelief into a faster, more robust application-grade library, which became TensorFlow. In 2009, the team, led by Geoffrey Hinton, had implemented generalized backpropagation and other improvements which allowed generation of neural networks with substantially higher accuracy, for instance a 25% reduction in errors in speech recognition.
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