Getting Started with TensorFlow for Deep Learning

$125 | Duration: 2h 45m | Video: h264, 1920×1080 | Audio: AAC, 48kHz, 2 Ch | 885 MB
Genre: eLearning | Language: English | November 30, 2018

Apply Deep Learning to different data types and solve real-world problems with TensorFlow

We will not only get you up-and-running with deep learning, but also equip you with the skills to implement your own neural networks and apply them to the real world.

We will use TensorFlow, an efficient Python library used to create and train our neural networks. You’ll learn the skills to implement their architecture quickly and efficiently without having to deal with minutiae.

You can rely on our expert guidance while learning the basic theory, backed up with relevant examples. We provide examples of neural networks, which you can use to highlight the key features. We then build up to more advanced networks. You’ll learn to utilize a Convolutional Neural Network to classify images of handwritten text and then take your CNN further to perform object detection and localization in an image.

This course will quickly get you past the fundamentals of TensorFlow; you’ll go on to more exciting things such as implementing a variety of image recognition tasks. All the code and this course’s supporting files are available on GitHub at – https://github.com/PacktPublishing/Getting-Started-with-TensorFlow-for-Deep-Learning-

Style and Approach
This course will breeze through some essential textbook knowledge when it comes to machine learning. Following a brief math section, we get started with deep learning straight away.

Table of Contents
AN INTRODUCTION TO DEEP LEARNING AND TENSORFLOW
GETTING STARTED WITH TENSORFLOW
IMPLEMENTING YOUR FIRST NEURAL NETWORK
HANDWRITTEN DIGIT CLASSIFICATION
OBJECT DETECTION AND CLASSIFICATION

What You Will Learn
Properly understand the meaning of deep learning
Train a neural network and understand the often complicated process of backpropogation.
Create datasets in the correct format for use with TensorFlow—a key step when it comes to training your own models.
Create your own neural network architecture in TensorFlow using Keras, allowing you to define any architecture for your own needs.
Get accustomed to convolutional neural networks and understand why they are so powerful for image classification.
Use the TensorFlow ObjectDetection API to classify and localize objects in an image

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