Deep Learning with Python

If you’ve picked up this book, you’re probably aware of the exưaordinary progress that deep leam ing has represented for the íìeld of artificial intelligence in the recent past. In a mere fĩve years, we’ve gone from near-unusable image recognition and speech transcription, to superhum an períorm ance on these tasks.

Deep Learning with Python

Chollet Francois

Manning Publications Co.

2017

Tóm tắt

This book was written for anyone who wishes to explore deep learming from scratch or broaden their understanding of deep learming. Whether you’re a practicing machine-learming engineer, a software developer, or a college student, you’ll find value in these pages. This book offers a practical, hands-on exploration of deep learning. It avoids mathematical notation, preferring instead to explain quantitative concepts via code snippets and to build practical intuition about the core ideas of m achine learning and deep learning.
You’ll learn from more than 30 code examples that include detailed commentary, practical recommendations, and simple high-level explanations of everything you need to know to start using deep learning to solve concrete problems.
The code examples use the Python deep-learning framework Keras, with TensorFlow as a backend engine. Keras, one of the most popular and fastest-growing deep learning frameworks, is widely recommended as the best tool to get started with deep learning.
After reading this book, you’ll have a solid understand of what deep learning is, when it's applicable, and what its limitations are. You’ll be familiar with the Standard workflow for approaching and solving machine-learning problems, and you’ll know how to address commonly encountered issues. You’ll be able to use Keras to tackle real-world problems ranging from Computer Vision to natural-language Processing:
image classification, timeseries forecasting, sentiment analysis, image and text generation, and more.

Citation

Chollet, Francois. Deep learning with Python. Vol. 361. New York: Manning, 2018.

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