Description

Intro to TensorFlow is an open source software library for high performance numerical computation which is highly effective for writing models that can train and run on platforms from laptops to a large group of servers in the Cloud to an edge device. In this course, we will take a bottom-up tour of this library, beginning with low-level math operations and finishing with the high-level operations can be used to concisely create ML models and pipelines. We then take those models and train them in a server-less environment.

 

Radiant Techlearning offers Intro to TensorFlow training program in Classroom & Virtual Instructor Led / online mode.

 

Duration: 15 Days

 

Learning Objectives:

After this course, learners will be proficient in:

  • Creating ML models in TensorFlow
  • Using the TensorFlow libraries to solve numerical problems
  • Troubleshooting and debugging common TensorFlow code pitfalls
  • Using tf.estimator to create, train, and evaluate an ML model
  • Train, implement, and productionalize ML models at scale with Cloud ML Engine.

Pre-requisites

Audience Profile:

  • Programmers, Data Engineers and Machine learning enthusiasts interested in practical applications.
  • Professionals who want to learn and apply Machine learning in their organization or business

Course Content

With this course, we areintroducing beginner-level TensorFlow and will try to work out through the necessary concepts and APIs in order to learn writing distributed machine learning models. With the help of a given TensorFlow model, we willuse Cloud Machine Learning Engine toexplain how to scale themodel training and offer high-performance predictions.

 

Introduction

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

 

Core TensorFlow

  • We will introduce professionals to the core components of TensorFlow and professionals will get hands-on practice building machine learning programs. Professionals will work with graphs, sessions, variables,compare and write lazy evaluation and imperative programs, and finally debug TensorFlow programs.

 

Estimator API

  • In this module we will walk professionals through the Estimator API.

 

Scaling TensorFlow models with CMLE

  • I’m here to talk about how professionals would go about taking professional’s TensorFlow model and training it on GCP’s managed infrastructure for machine learning model training and deployed.

 

Summary

  • Here we summarize the TensorFlow topics we covered so far in this course. We’ll revisit core TensorFlow code, the Estimator API, and end with scaling professional’s machine learning models with Cloud Machine Learning Engine.

FAQs

Q: What is the main feature of TensorFlow?

 

A: Tensorflow has APIs support for a large variety of languages such as Matlab and C++. Researchers are continuously trying to making it more better. A javascript library, tensorflow.js, has also been introduced for training and deploying machine learning models.

 

Q: What are the different dashboards in Tensorflow?

 

A: To perform various tasks in tensor board different types of dashboards are present and i.e.

  • Scalar Dashboard
  • Histogram Dashboard
  • Distributer Dashboard
  • Image Dashboard
  • Audio Dashboard
  • Graph Explorer
  • Projector
  • Text Dashboard

 

Q: What are the components of deploying a lite model file in TensorFlow?

 

A: The components of deploying lite model file in Tensorflow are listed below:-

  • Java API: Java API is a wrapper around C++ API on Android.
  • C++ API: C++ API loads the TensorFlow Lite model and calls the interpreter.
  • Interpreter: The interpreter can be used to execute the model. It uses particular kernel loading, which is a unique feature of TensorFlow Lite.

 

Q: What are the option to load data in Tensorflow?

 

A: Loading the data into TensorFlow is the initial step before training a machine learning algorithm. There are generally two ways to load the data:

  • Load data in memory: It is the easiest method. All the data is loaded into memory as a single array. One can write a Python code which is unrelated to TensorFlow.
  • Tensorflow data pipeline: TensorFlow has built-in APIs which help to load the data, perform the operations, and feed the machine learning algorithm easily. This method is mostly used when there is a large dataset.

 

Q: When the training would be conducted?

 

A: Once we receive your enrollment request, we will share the enrollment details with you to select and complete the enrollment process.

You can always email us on the below email address (whichever applicable) to know the upcoming schedule for a specific technology training program.

Individual:  training@radianttechlearning.com

Corporate: Corporate@radiantechlearning.com

 

Q: Will I get course completion certificate?

 

A: The course completion certification would be awarded to all the professionals, who have completed the training program and the project assignment given by your instructor.

You can use the certificate in your future job interviews will surely help you to land in your dream job.

 

Q: What is the infrastructure required to attend your training program?

 

A: To attend the training session you should have an operational Desktops or Laptops with required specification along with good internet connection to access the labs.

 

Q: What is the infrastructure required to attend your training program?

 

A: To attend the training session you should have an operational Desktops or Laptops with required specification along with good internet connection to access the labs.

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