Python Deep Learning Projects

This is the code repository for Python Deep Learning Projects, published by Packt.

9 projects demystifying neural network and deep learning models for building intelligent systems

What is this book about?

Deep learning has been gradually revolutionizing every field of artificial intelligence, making application development easier.

This book covers the following exciting features:

Set up a deep learning development environment on Amazon Web Services (AWS)
Apply GPU-powered instances as well as the deep learning AMI
Implement seq-to-seq networks for modeling natural language processing (NLP)
Develop an end-to-end speech recognition system
Build a system for pixel-wise semantic labeling of an image Create a system that generates images and their regions

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

import sys
import dlib
from skimage import io

Following is what you need for this book: Python Deep Learning Projects is for you if you want to get insights into deep learning, data science, and artificial intelligence. This book is also for those who want to break into deep learning and develop their own AI projects. It is assumed that you have sound knowledge of Python programming

With the following software and hardware list you can run all code files present in the book (Chapter 1-15).

Software and Hardware List

Chapter	Software required	OS required
1-14	Anaconda Package Python 2.x/3.x, TensorFlow, Keras	Ubuntu 16.04 or greater

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.

Get to Know the Author

Matthew Lamons Matthew Lamons's background is in experimental psychology and deep learning. Founder and CEO of Skejul—the AI platform to help people manage their activities. Named by Gartner, Inc. as a "Cool Vendor" in the "Cool Vendors in Unified Communication, 2017" report. He founded The Intelligence Factory to build AI strategy, solutions, insights, and talent for enterprise clients and incubate AI tech startups based on the success of his Applied AI MasterMinds group. Matthew's global community of more than 85 K are leaders in AI, forecasting, robotics, autonomous vehicles, marketing tech, NLP, computer vision, reinforcement, and deep learning. Matthew invites you to join him on his mission to simplify the future and to build AI for good.

Rahul Kumar Rahul Kumar is an AI scientist, deep learning practitioner, and independent researcher. His expertise in building multilingual NLU systems and large-scale AI infrastructures has brought him to Copenhagen, where he leads a large team of AI engineers as Chief AI Scientist at Jatana. Often invited to speak at AI conferences, he frequently travels between India, Europe, and the US where, among other research initiatives, he collaborates with The Intelligence Factory as NLP data science fellow. Keen to explore the ramifications of emerging technologies for his next book, he's currently involved in various research projects on Quantum Computing (QC), high-performance computing (HPC), and the brain-computer interaction (BCI).

Abhishek Nagaraja Abhishek Nagaraja was born and raised in India. Graduated Magna Cum Laude from the University of Illinois at Chicago, United States, with a Masters Degree in Mechanical Engineering with a concentration in Mechatronics and Data Science. Abhishek specializes in Keras and TensorFlow for building and evaluation of custom architectures in deep learning recommendation models. His deep learning skills and interest span computational linguistics and NLP to build chatbots to computer vision and reinforcement learning. He has been working as a Data Scientist for Skejul Inc. building an AI-powered activity forecast engine and engaged as a Deep Learning Data Scientist with The Intelligence Factory building solutions for enterprise clients.

Other books by the authors

Machine Learning Quick Reference

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