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Deep-Learning-with-TensorFlow-2.0-in-7-Steps

Learn image classification and language modeling

This is the code repository for Deep Learning with TensorFlow 2.0 in 7 Steps [Video], published by Packt. It contains all the supporting project files necessary to work through the video course from start to finish.

About the Video Course

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.

What You Will 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

Instructions and Navigation

Assumed Knowledge

This course is for developers, programmers, and data scientists who are familiar with machine learning concepts and want to get into deep learning using TensorFlow 2.0 in a fast and compelling way. Previous programming knowledge of Python is a must-have to take advantage of the content in this course.

Technical Requirements

This course has the following requirements:
Basic Python programming language skills
Basic statistics
Google account
Jupiter notebooks
Anaconda / Miniconda
Windows, Mac OS or Linux

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