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Python Deep Learning Projects

You're reading from Python Deep Learning Projects 9 projects demystifying neural network and deep learning models for building intelligent systems

Product type Paperback

Published in Oct 2018

Publisher Packt

ISBN-13 9781788997096

Length 472 pages

Edition 1st Edition

Languages

Python

Concepts

Deep Learning

Authors (3):

Rahul Kumar

Matthew Lamons

Abhishek Nagaraja

View More author details

Table of Contents (17) Chapters

Preface

1. Building Deep Learning Environments

2. Training NN for Prediction Using Regression FREE CHAPTER

3. Word Representation Using word2vec

4. Building an NLP Pipeline for Building Chatbots

5. Sequence-to-Sequence Models for Building Chatbots

6. Generative Language Model for Content Creation

7. Building Speech Recognition with DeepSpeech2

8. Handwritten Digits Classification Using ConvNets

9. Object Detection Using OpenCV and TensorFlow

10. Building Face Recognition Using FaceNet

11. Automated Image Captioning

12. Pose Estimation on 3D models Using ConvNets

13. Image Translation Using GANs for Style Transfer

14. Develop an Autonomous Agent with Deep R Learning

15. Summary and Next Steps in Your Deep Learning Career

16. Other Books You May Enjoy

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Setting up a DL environment in the cloud

All the steps we performed up to now remain the same for the cloud as well, but there are a few additional modules required to configure the cloud virtual machines to make your DL applications servable and scalable. So, before setting up your server, follow the instructions from the preceding section.

To deploy your DL applications in the cloud, you will need a server good enough to train your models and serve at the same time. With huge development in the sphere of DL, the need for cloud servers to practice and deploy projects has increased drastically, and so have the options on the market. The following is a list of some of the best options on offer:

Paperspace (https://www.paperspace.com/)
FloydHub (https://www.floydhub.com)
Amazon Web Services (https://aws.amazon.com/)
Google Cloud Platform (https://cloud.google.com/)
DigitalOcean (https://cloud.digitalocean.com/)

All of these options have their own pro and cons, and the final choice totally depends on your use case and preferences, so feel free to explore more. In this book, we will build and deploy our models mostly on Google Compute Engine (GCE), which is a part of Google Cloud Platform (GCP). Follow the steps mentioned in this chapter to spin up a VM server and get started.

Google has released an internal notebook platform, Google Colab (https://colab.research.google.com/), which is pre-installed with all the DL packages and other Python libraries. You can write all of your ML/DL applications on the Google Cloud, leveraging free GPUs for 10 hours.

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Authors (3)

Lamons

Lamons

Matthew Lamons's background is in experimental psychology and deep learning. Founder and CEO of Skejulthe 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.

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Nagaraja

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.

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Kumar

Kumar

Ashish Kumar is a seasoned data science professional, a publisher author and a thought leader in the field of data science and machine learning. An IIT Madras graduate and a Young India Fellow, he has around 7 years of experience in implementing and deploying data science and machine learning solutions for challenging industry problems in both hands-on and leadership roles. Natural Language Procession, IoT Analytics, R Shiny product development, Ensemble ML methods etc. are his core areas of expertise. He is fluent in Python and R and teaches a popular ML course at Simplilearn. When not crunching data, Ashish sneaks off to the next hip beach around and enjoys the company of his Kindle. He also trains and mentors data science aspirants and fledgling start-ups.

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