Book Image

TensorFlow Developer Certificate Guide

By : Oluwole Fagbohun
4 (2)
Book Image

TensorFlow Developer Certificate Guide

4 (2)
By: Oluwole Fagbohun

Overview of this book

The TensorFlow Developer Certificate Guide is an indispensable resource for machine learning enthusiasts and data professionals seeking to master TensorFlow and validate their skills by earning the certification. This practical guide equips you with the skills and knowledge necessary to build robust deep learning models that effectively tackle real-world challenges across diverse industries. You’ll embark on a journey of skill acquisition through easy-to-follow, step-by-step explanations and practical examples, mastering the craft of building sophisticated models using TensorFlow 2.x and overcoming common hurdles such as overfitting and data augmentation. With this book, you’ll discover a wide range of practical applications, including computer vision, natural language processing, and time series prediction. To prepare you for the TensorFlow Developer Certificate exam, it offers comprehensive coverage of exam topics, including image classification, natural language processing (NLP), and time series analysis. With the TensorFlow certification, you’ll be primed to tackle a broad spectrum of business problems and advance your career in the exciting field of machine learning. Whether you are a novice or an experienced developer, this guide will propel you to achieve your aspirations and become a highly skilled TensorFlow professional.
Table of Contents (20 chapters)
1
Part 1 – Introduction to TensorFlow
6
Part 2 – Image Classification with TensorFlow
12
Part 3 – Natural Language Processing with TensorFlow
15
Part 4 – Time Series with TensorFlow

Further reading

To learn more, you can check out the following resources:

  • Kapoor, A., Gulli, A. and Pal, S. (2020) Deep Learning with TensorFlow and Keras Third Edition: Build and deploy supervised, unsupervised, deep, and reinforcement learning models. Packt Publishing Ltd.
  • Adapting Deep Convolutional Neural Networks for Transfer Learning: A Comparative Study by C. M. B. Al-Rfou, G. Alain, and Y. Bengio, published in arXiv preprint arXiv:1511.
  • Very Deep Convolutional Networks for Large-Scale Image Recognition by K. Simonyan and A. Zisserman, published in arXiv preprint arXiv:1409.1556 in 2014.
  • EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks by M. Tan and Q. Le, published in International Conference on Machine Learning in 2019.
  • MobileNetV2: Inverted Residuals and Linear Bottlenecks by M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, published in arXiv preprint arXiv:1801.04381 in 2018.
  • DeCAF: A Deep Convolutional Activation...