Deep Learning with TensorFlow 2.0 in 7 Steps [Video]

More Information
Learn
  • Set up a conda environment for training models
  • Learn the basics of machine learning and deep learning to help you cut through the jargon
  • Learn about high-level TensorFlow 2.0 APIs so that you can quickly train your own models
  • Import data into your models for building real-world applications
  • Build APIs for software applications to make use of your model in production
  • Use Convolutional Neural Networks (CNN) for image classification
About

Image classification and language modelling are two fields of computing that are difficult for computers to tackle without implementing deep neural networks. How do you recognize the difference or similarity between two fruits or two words? This is required for various applications, ranging from e-commerce sites to educational software. While these tasks are non-trivial, TensorFlow provides a gentle introduction to solving them.

In this course, you will learn how to get started with TensorFlow 2.0 in a unique and enticing way, using an ambitious approach that's perfect for learning and implementing deep learning models. You will learn how to start building and training your own models to classify images and also differentiate between different text. Using TensorFlow at a high level, you will learn to implement Convolutional Neural Networks (CNN), as well as sequence networks such as Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN).

By the end of this course, you will be confident about building and implementing deep learning models effectively and easily with TensorFlow 2.0, collecting image data, splitting it into training, validation and test sets, and training a model to classify images.

All the code and supporting files for this course are available on GitHub at https://github.com/PacktPublishing/Deep-Learning-with-TensorFlow-2.0-in-7-Steps

Features
  • Design and train a multilayer neural network with TensorFlow.
  • Implement Deep Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks
  • Using deep learning for well-known ML problems: regression, classification, clustering, and autoencoding
Course Length 2 hours 44 minutes
ISBN 9781789958614
Date Of Publication 27 Sep 2019

Authors

Robert Thas John

Robert Thas John is a Google developer expert in machine learning. His day job involves working as a data engineer on the Google Cloud Platform by building, training, and deploying large-scale machine learning models. He also makes decisions about how to store and process large amounts of data. He has more than 10 years of experience in building enterprise-grade solutions and working with data. He spends his free time learning or contributing to the developer community. He frequently travels to speak at technology events or to mentor developers. He also writes a blog on data science.