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Hands-On Python and TensorFlow for Machine Learning and Deep Learning

New – Explore various possibilities with machine and deep learning, and gain amazing insights from data using Google’s brainchild, TensorFlow in this self-paced course.

GK# 7726

Course Overview

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With the help of this course, you will be able to process your data and gain insights that will change how you look at it. You will dig deeper into the hidden layers of abstraction using raw data, and use various complex algorithms for deep learning and various examples that use these deep neural networks. You will learn how to perform various machine learning tasks in different environments. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. You will come across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces.

Schedule

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What You'll Learn

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  • Set up your computing environment and install TensorFlow
  • Build simple TensorFlow graphs for everyday computations
  • Apply logistic regression for classification with TensorFlow
  • Design and train a multilayer neural network with TensorFlow
  • Intuitively understand convolutional neural networks for image recognition

Outline

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On-Demand Outline

Get started with TensorFlow

With the efficiency and simplicity of TensorFlow, process your data and gain insights that will change how you look at data

Understand complex algorithms for deep learning and various examples that use these deep neural networks

GAin knowledge on topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces

Who Should Attend

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This course is for those who have a basic knowledge of Machine Learning and wish to learn more about the ML and DL techniques and how you can implement them in TensorFlow. A basic knowledge of Python is assumed.

Course Delivery

This course is available in the following formats:

On-Demand

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