TensorFlow Developer Certificate Guide: Efficiently tackle deep learning and ML problems to ace the Developer Certificate exam - Softcover

Inhaltsangabe

Ace the TensorFlow Developer Certificate exam with this comprehensive guide. Learn key TensorFlow concepts and techniques, including data loading and visualization, neural networks, and machine learning. Perfect for aspiring TensorFlow developers.

Key Features

• Data Representation and manipulation with Tensorflow
• Be able to build, compile and train a regression model with Tensorflow
• Be able to build, compile and train a classification model with Tensorflow

Book Description

Tensorflow enables ML practitioners to build Deep learning models use to solve complex task across various industries. With Tensorflow Practitioners can easily build and train ML models using Keras API. Tensorflow is used by Twitter, Intel, Google, Airbnb amongst others in solving business problems.

Learning Tensorflow gives you the requisite skills and know how required to build complex deep learning models used in solving small, medium, and large-scale problems in various industries.

Complete with step-by-step explanations of the fundamentals of Tensorflow, image classification, natural language processing and time series analysis and prediction. This book provides practical examples and do it yourself exercises.

By the end of this book, readers should be confident enough to take up and ace their TensorFlow developer certificate exam and start building real world projects

What you will learn

• Image Classification with Tensorflow
• Natural Language Processing with Tensorflow
• Time series, sequences and predictions with Tensorflow

Who This Book Is For

This book is for ML practitioners and enthusiast studying for their Tensorflow Certified Developer Exams.

Table of Contents

1. Introduction to Machine Learning
2. Introduction to Tensorflow
3. Linear Regression with Tensorflow
4. Classification with Tensorflow
5. Image Classification with Neural Networks
6. Improving the model
7. Image Classification with Convolution Neural Networks
8. Handling Overfitting
9. Transfer Learning
10. Introduction to Natural Language Processing
11. Natural Language Processing with Tensorflow
12. Introduction to Time Series, Sequence and Prediction
13. Time Series, Sequence and Prediction with Tensorflow

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