Advanced Machine Learning with TensorFlow on Google Cloud Platform Specialization

Start Date: 10/21/2018

Course Type: Specialization Course

Course Link: https://www.coursera.org/specializations/advanced-machine-learning-tensorflow-gcp

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Course Syllabus

5 courses

End-to-End Machine Learning with TensorFlow on GCP

In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization (https://www.coursera.org/specializations/machine-learning-tensorflow-gcp). On

Production Machine Learning Systems

In the second course of this specialization, we will dive into the components and best practices of a high-performing ML system in production environments. Prerequisites: Basic SQL, familiarity with P

Image Understanding with TensorFlow on GCP

This is the third course of the Advanced Machine Learning on GCP specialization. In this course, We will take a look at different strategies for building an image classifier using convolutional neural networks. We'll improve the model's accur

Sequence Models for Time Series and Natural Language Processing

This course is an introduction to sequence models and their applications, including an overview of sequence model architectures and how to handle inputs of variable length. • Predict future values of a time-series • Classify free form text •

Recommendation Systems with TensorFlow on GCP
Starts November 2018
In this course, you'll apply your knowledge of classification models and embeddings to build a ML pipeline that functions as a recommendation engine. • Devise a content-based recommendation engine • Implement a collaborative filtering recommendation engine • Build a hybrid recommendation engine with user and content embeddings

Deep Learning Specialization on Coursera

Course Introduction

-Learn Advanced Machine Learning with Google Cloud. Build production-ready machine learning models with TensorFlow on Google Cloud Platform.

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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.
Google Cloud Platform Google Cloud Platform is a cloud computing service by Google that offers hosting on the same supporting infrastructure that Google uses internally for end-user products like Google Search and YouTube. Cloud Platform provides developer products to build a range of programs from simple websites to complex applications.
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.
Google Cloud Platform Google Cloud Platform is a part of a suite of enterprise services from Google Cloud and provides a set of modular cloud-based services with a host of development tools. For example, hosting and computing, cloud storage, data storage, translations APIs and prediction APIs.
Google Cloud Dataproc Google Cloud Dataproc (Cloud Dataproc) is a cloud-based managed Spark and Hadoop service offered on Google Cloud Platform. Cloud Dataproc utilizes many Google Cloud Platform technologies such as Google Compute Engine and Google Cloud Storage to offer fully managed clusters running popular data processing frameworks such as Apache Hadoop and Apache Spark.
Google Cloud Datastore Google Cloud Datastore (Cloud Datastore) is a highly scalable, fully managed NoSQL database service offered by Google on the Google Cloud Platform. Cloud Datastore is built upon Google's Bigtable and Megastore technology.
TensorFlow In May 2016 Google announced its tensor processing unit (TPU), a custom ASIC built specifically for machine learning and tailored for TensorFlow. The TPU is a programmable AI accelerator designed to provide high throughput of low-precision arithmetic (e.g., 8-bit), and oriented toward using or running models rather than training them. Google announced they had been running TPUs inside their data centers for more than a year, and have found them to deliver an order of magnitude better-optimized performance per watt for machine learning.
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.
Google Cloud Print On July 23, 2013 Google updated the service to allow printing from any Windows application, if Google Cloud Printer is installed on the machine. Another new feature is Google Cloud Print Service, which can run as a Windows service so administrators can connect legacy printers to Google Cloud Print in their businesses.
TensorFlow Starting in 2011, Google Brain built DistBelief as a proprietary, machine learning system, based on deep learning neural networks. Its use grew rapidly across diverse Alphabet companies in both research and commercial applications.
SAP Cloud Platform Initially unveiled as SAP NetWeaver Cloud belonging to the SAP HANA Cloud portfolio on October 16, 2012 the cloud platform was reintroduced with the new name SAP HANA Cloud Platform on May 13, 2013 as the foundation for SAP cloud products, including the SAP BusinessObjects Cloud. Adoption of the SAP HANA Cloud Platform has increased steadily since the platform's launch in 2012, with SAP claiming over 4000 customers and 500 partners adopting the SAP HANA Cloud Platform.
TensorFlow Among the applications for which TensorFlow is the foundation, are automated image captioning software, such as DeepDream. Google officially implemented RankBrain on 26 October 2015, backed by TensorFlow. RankBrain now handles a substantial number of search queries, replacing and supplementing traditional static algorithm based search results.
Google Cloud Print Google Cloud Print is a Google service that was created to allow any Cloud-Print-aware application (web, desktop, mobile) on any device in the network cloud to print to any printer – without Google having to create and maintain printing subsystems for all the hardware combinations of client devices and printers, and without the users having to install device drivers to the client, but with documents being fully transmitted to Google. Since July 23, 2013 it also allows printing from any Windows application, if Google Cloud Printer is installed on the machine.
Google Cloud Dataproc Cloud Dataproc includes many open source packages used for data processing, including items from the Spark and Hadoop ecosystem, and open source tools to connect these frameworks with other Google Cloud Platform products.
SAP Cloud Platform On February 27, 2017 SAP HANA Cloud Platform was renamed SAP Cloud Platform at the Mobile World Congress.
Machine learning Machine Learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use, thus digitizing cultural prejudices. Responsible collection of data thus is a critical part of machine learning.
Active learning (machine learning) Recent developments are dedicated to hybrid active learning and active learning in a single-pass (on-line) context, combining concepts from the field of Machine Learning (e.g., conflict and ignorance) with adaptive, incremental learning policies in the field of Online machine learning.
Google Cloud Dataproc Cloud Dataproc is a Platform as a service (PaaS) product designed to combine the Spark and Hadoop frameworks with many common cloud computing patterns. Cloud Dataproc separate compute and storage, which is a relatively common design for many cloud Hadoop offerings. Cloud Dataproc utilizes Google Compute Engine virtual machines for compute and Google Cloud Storage for file storage. Cloud Dataproc has a set of control and integration mechanisms that coordinate the lifecycle, management, and coordination of clusters. Cloud Dataproc is integrated with the YARN application manager to make managing and using clusters easier.
International Conference on Machine Learning The conference attracts leading innovations in the field of machine learning. ICML is a top tier conference, and is one of the two most influential conferences in Machine Learning (along with Conference on Neural Information Processing Systems).
International Conference on Machine Learning The International Conference on Machine Learning (ICML) is the leading international academic conference in machine learning, attracting annually more than 2000 participants from all over the world. It is supported by the International Machine Learning Society (IMLS).