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Introduction to Deep Learning with TensorFlow

New – Learn advanced machine learning and neural networks with Python in this self-paced course.

GK# 7723

Course Overview

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This course is your guide to explore possibilities with deep learning—it will enable you to understand data like never before. You will be able to process your data and gain insights which would change how you look at data. You will dig your teeth deeper into the hidden layers of abstraction using raw data. It teaches various complex algorithms for deep learning with examples that use different deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data. During the course, you will come across topics like logistic regression, convolutional neural networks, advanced multilayer perceptrons and how to implement them using real-world datasets.

Schedule

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  • Delivery Format:
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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
  • Access public datasets and utilize them using TensorFlow to load, process, and transform data
  • Design and train a multilayer neural network with TensorFlow
  • Using deep learning for scalable object detection and mobile computing
  • Learn how to evaluate the performance of your deep learning models

Outline

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On-Demand Outline

Introduction to Deep Neural Networks

First Look at TensorFlow

TensorFlow on a Convolutional Neural Network

Implementing a Restricted Boltzmann Machine with TensorFlow

Optimizing TensorFlow using an Autoencoder

Sentiment analysis with TensorFlow and LSTMs

Who Should Attend

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Data scientists and machine learning engineers who wish to dive deep into the fundamentals of deep learning will find this course to be very useful. Some experience with Python programming will be required, while understanding of some fundamental calculus and linear algebra will also help.

Course Delivery

This course is available in the following formats:

On-Demand

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