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

Muhammad Hamza Javed

€122.99

Video Aug 2019 1hr 54mins 1st Edition

Muhammad Hamza Javed

€122.99

Video Aug 2019 1hr 54mins 1st Edition

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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

Who is this book for?

This course is for developers who have a basic knowledge of Python. If you’re aware of the basics of machine learning and now want to build deep learning systems with TensorFlow 2.0 that are smarter, faster, more complex, and more practical, then this course is for you!

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

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