Deploy deep learning applications into production across multiple platforms. You will work on
computer vision applications that use the convolutional neural network (CNN) deep learning
model and Python. This book starts by explaining the traditional machine-learning pipeline
where you will analyze an image dataset. Along the way you will cover artificial neural
networks (ANNs) building one from scratch in Python before optimizing it using genetic
algorithms. For automating the process the book highlights the limitations of traditional
hand-crafted features for computer vision and why the CNN deep-learning model is the
state-of-art solution. CNNs are discussed from scratch to demonstrate how they are different
and more efficient than the fully connected ANN (FCNN). You will implement a CNN in Python to
give you a full understanding of the model. After consolidating the basics you will use
TensorFlow to build a practical image-recognition model that you will deploy to a web server
using Flask making it accessible over the Internet. Using Kivy and NumPy you will create
cross-platform data science applications with low overheads. This book will help you apply deep
learning and computer vision concepts from scratch step-by-step from conception to production.
What You Will Learn Understand how ANNs and CNNs work Create computer vision applications and
CNNs from scratch using Python Follow a deep learning project from conception to production
using TensorFlow Use NumPy with Kivy to build cross-platform data science applications Who This
Book Is ForData scientists machine learning and deep learning engineers software developers.
