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Getting Started with TensorFlow 2.0 for Deep Learning [Video]
Getting Started with TensorFlow 2.0 for Deep Learning [Video]

Getting Started with TensorFlow 2.0 for Deep Learning: Leverage the power of deep learning with TensorFlow 2.0 [Video]

By Muhammad Hamza Javed
$15.99 per month
Video Aug 2019 1 hours 54 minutes 1st Edition
Video
$130.99
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Video
$130.99
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Product Details


Publication date : Aug 22, 2019
Length 1 hours 54 minutes
Edition : 1st Edition
Language : English
ISBN-13 : 9781789954470
Vendor :
Google
Category :
Concepts :

Key benefits

  • Explore the latest feature set and modern deep learning APIs in TensorFlow 2.0
  • Develop computer vision and text sequences based on deep learning models
  • Learn advanced deep learning topics including Keras functional API

Description

Deep learning is a trending technology if you want to break into cutting-edge AI and solve real-world, data-driven problems. Google’s TensorFlow is a popular library for implementing deep learning algorithms because of its rapid developments and commercial deployments. This course provides you with the core of deep learning using TensorFlow 2.0. You’ll learn to train your deep learning networks from scratch, pre-process and split your datasets, train deep learning models for real-world applications, and validate the accuracy of your models. By the end of the course, you’ll have a profound knowledge of how you can leverage TensorFlow 2.0 to build real-world applications without much effort. All the notebooks and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Getting-Started-with-TensorFlow-2.0-for-Deep-Learning-Video

What you will learn

Develop real-world deep learning applications Classify IMDb Movie Reviews using Binary Classification Model Build a model to classify news with multi-label Train your deep learning model to predict house prices Understand the whole package: prepare a dataset, build the deep learning model, and validate results Understand the working of Recurrent Neural Networks and LSTM with hands-on examples Implement autoencoders and denoise autoencoders in a project to regenerate images

What do you get with a Packt Subscription?

Free for first 7 days. $15.99 p/m after that. Cancel any time!
Product feature icon Unlimited ad-free access to the largest independent learning library in tech. Access this title and thousands more!
Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
Product feature icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Product feature icon Thousands of reference materials covering every tech concept you need to stay up to date.
Subscribe now
View plans & pricing

Product Details


Publication date : Aug 22, 2019
Length 1 hours 54 minutes
Edition : 1st Edition
Language : English
ISBN-13 : 9781789954470
Vendor :
Google
Category :
Concepts :

Table of Contents

7 Chapters
1. Deep Learning Basics Chevron down icon Chevron up icon
2. TensorFlow 2.0 for Deep Learning Chevron down icon Chevron up icon
3. Working with CNNs for Computer Vision and Deep Learning Chevron down icon Chevron up icon
4. Working with LSTM for Text Data and Deep Learning Chevron down icon Chevron up icon
5. Working with RNNs for Time Series Sequences and Deep Learning Chevron down icon Chevron up icon
6. Autoencoders – AE and Denoising AE Chevron down icon Chevron up icon
7. Deep Learning Mini-Projects Chevron down icon Chevron up icon

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