This book has one goal — to help developers, researchers, and students just like yourself become experts in deep learning for image recognition and classification.
Inside this book you’ll find:
Inside this book you’ll find:
- Super practical walkthroughs that present solutions to actual, real-world image classification problems, challenges, and competitions.
- Hands-on tutorials (with lots of code) that not only show you the algorithms behind deep learning for computer vision but their implementations as well.
- A no-nonsense teaching style that is guaranteed to cut through all the cruft and help you master deep learning for image understanding and visual recognition.
Are you just getting started in deep learning? Don’t worry; you won’t get bogged down by tons of theory and complex equations. We’ll start off with the basics of machine learning and neural networks. Learn in a fun, practical way with lots of code. You’ll be a neural network ninja in no time, and be able to graduate to the more advanced content.
Are you already a seasoned deep learning pro? This book isn’t just for beginners — there’s advanced content in here, too. You’ll discover how to train your own custom object detectors using deep learning. You’ll build a custom framework that can be used to train very deep architectures on the challenging ImageNet dataset from scratch. I’ll even show you my personal blueprint which I use to determine which deep learning techniques to apply when confronted with a new problem. Best of all, these solutions and tactics can be directly applied to your current job and research.
Deep Learning for Computer Vision with Python will make you an expert in deep learning for computer vision and visual recognition tasks.
Inside the book we will focus on:
Are you already a seasoned deep learning pro? This book isn’t just for beginners — there’s advanced content in here, too. You’ll discover how to train your own custom object detectors using deep learning. You’ll build a custom framework that can be used to train very deep architectures on the challenging ImageNet dataset from scratch. I’ll even show you my personal blueprint which I use to determine which deep learning techniques to apply when confronted with a new problem. Best of all, these solutions and tactics can be directly applied to your current job and research.
Deep Learning for Computer Vision with Python will make you an expert in deep learning for computer vision and visual recognition tasks.
Inside the book we will focus on:
- Neural Networks and Machine Learning
- Convolutional Neural Networks (CNNs)
- Object detection/localization with deep learning
- Training large-scale (ImageNet-level) networks
- Hands on implementations using the Python programming language and the Keras (which is compatible with either TensorFlow or Theano) + mxnet libraries
After going through Deep Learning for Computer Vision with Python, you’ll be able to solve real-world problems with deep learning.
Utilize Python, Keras (with either a TensorFlow or Theano backend), and mxnet to build deep learning networks.
Python, Keras, and mxnet are all well-built tools that, when combined, create a powerful deep learning development environment that you can use to master deep learning for computer vision and visual recognition.
We’ll be utilizing the Python programming language for all examples in this book. Python is an easy language to learn and is hands-down the best way to work with deep learning algorithms.
To build and train our deep learning networks we’ll primarily be using the Keras library. Keras supports both TensorFlow and Theano, making it super easy to build and train networks quickly.
We’ll also use mxnet, a deep learning library that specializes in distributed, multi-machine learning. The ability to parallelize training across GPUs/devices is critical when training deep neural network architectures on massive datasets (such as ImageNet).
Each library that we use in this book will be thoroughly reviewed to ensure you understand how to build & train your own deep learning networks.
Utilize Python, Keras (with either a TensorFlow or Theano backend), and mxnet to build deep learning networks.
Python, Keras, and mxnet are all well-built tools that, when combined, create a powerful deep learning development environment that you can use to master deep learning for computer vision and visual recognition.
We’ll be utilizing the Python programming language for all examples in this book. Python is an easy language to learn and is hands-down the best way to work with deep learning algorithms.
To build and train our deep learning networks we’ll primarily be using the Keras library. Keras supports both TensorFlow and Theano, making it super easy to build and train networks quickly.
We’ll also use mxnet, a deep learning library that specializes in distributed, multi-machine learning. The ability to parallelize training across GPUs/devices is critical when training deep neural network architectures on massive datasets (such as ImageNet).
Each library that we use in this book will be thoroughly reviewed to ensure you understand how to build & train your own deep learning networks.
Deep Learning for Computer Vision with Python by Dr. Adrian Rosebrock
English | 2017 | 322 Pages | PDF | 26 MB
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