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Home > Data > Chatbots > TensorFlow Deep Learning Projects
TensorFlow Deep Learning Projects
TensorFlow Deep Learning Projects

TensorFlow Deep Learning Projects: 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning

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TensorFlow Deep Learning Projects

Annotating Images with Object Detection API

Computer vision has made great leaps forward in recent years because of deep learning, thus granting computers a higher grade in understanding visual scenes. The potentialities of deep learning in vision tasks are great: allowing a computer to visually perceive and understand its surroundings is a capability that opens the door to new artificial intelligence applications in both mobility (for instance, self-driving cars can detect if an appearing obstacle is a pedestrian, an animal or another vehicle from the camera mounted on the car and decide the correct course of action) and human-machine interaction in everyday-life contexts (for instance, allowing a robot to perceive surrounding objects and successfully interact with them).

After presenting ConvNets and how they operate in the first chapter, we now intend to create a quick, easy...

The Microsoft common objects in context

Advances in application of deep learning in computer vision are often highly focalized on the kind of classification problems that can be summarized by challenges such as ImageNet (but also, for instance, PASCAL VOC - http://host.robots.ox.ac.uk/pascal/VOC/voc2012/) and the ConvNets suitable to crack it (Xception, VGG16, VGG19, ResNet50, InceptionV3, and MobileNet, just to quote the ones available in the well-known package Keras: https://keras.io/applications/).

Though deep learning networks based on ImageNet data are the actual state of the art, such networks can experience difficulties when faced with real-world applications. In fact, in practical applications, we have to process images that are quite different from the examples provided by ImageNet. In ImageNet the elements to be classified are clearly the only clear element present...

The TensorFlow object detection API

As a way of boosting the capabilities of the research community, Google research scientists and software engineers often develop state-of-the-art models and make them available to the public instead of keeping them proprietary. As described in the Google research blog post, https://research.googleblog.com/2017/06/supercharge-your-computer-vision-models.html , on October 2016, Google's in-house object detection system placed first in the COCO detection challenge, which is focused on finding objects in images (estimating the chance that an object is in this position) and their bounding boxes (you can read the technical details of their solution at https://arxiv.org/abs/1611.10012).

The Google solution has not only contributed to quite a few papers and been put to work in some Google products (Nest Cam - https://nest.com/cameras/nest-aware...

Presenting our project plan

Given such a powerful tool made available by TensorFlow, our plan is to leverage its API by creating a class you can use for annotating images both visually and in an external file. By annotating, we mean the following:

  • Pointing out the objects in an image (as recognized by a model trained on MS COCO)
  • Reporting the level of confidence in the object recognition (we will consider only objects above a minimum probability threshold, which is set to 0.25, based on the speed/accuracy trade-offs for modern convolutional object detectors discussed in the paper previously mentioned)
  • Outputting the coordinates of two opposite vertices of the bounding box for each image
  • Saving all such information in a text file in JSON format
  • Visually representing the bounding box on the original image, if required

In order to achieve such objectives, we need to:

  1. Download...

Provisioning of the project code

We start scripting our project in the file tensorflow_detection.py by loading the necessary packages:

import os
import numpy as np
import tensorflow as tf
import six.moves.urllib as urllib
import tarfile
from PIL import Image
from tqdm import tqdm
from time import gmtime, strftime
import json
import cv2

In order to be able to process videos, apart from OpenCV 3, we also need the moviepy package. The package moviepy is a project that can be found at http://zulko.github.io/moviepy/ and freely used since it is distributed with an MIT license. As described on its home page, moviepy is a tool for video editing (that is cuts, concatenations, title insertions), video compositing (non-linear editing), video processing, or to create advanced effects.

The package operates with the most common video formats, including the GIF format. It needs the FFmpeg converter...

Acknowledgements

Everything related to this project started from the following paper: Speed/accuracy trade-offs for modern convolutional object detectors(https://arxiv.org/abs/1611.10012) by Huang J, Rathod V, Sun C, Zhu M, Korattikara A, Fathi A, Fischer I, Wojna Z, Song Y, Guadarrama S, Murphy K, CVPR 2017. Concluding this chapter, we have to thank all the contributors of the TensorFlow object detection API for their great job programming the API and making it open-source and thus free and accessible to anyone: Jonathan Huang, Vivek Rathod, Derek Chow, Chen Sun, Menglong Zhu, Matthew Tang, Anoop Korattikara, Alireza Fathi, Ian Fischer, Zbigniew Wojna, Yang Song, Sergio Guadarrama, Jasper Uijlings, Viacheslav Kovalevskyi, Kevin Murphy. We also cannot forget to thank Dat Tran for his inspirational posts on medium of two MIT licensed projects on how to use the TensorFlow object...

Summary

This project has helped you to start immediately classifying objects in images with confidence without much hassle. It helps you to see what a ConvNet could do for your problem, focusing more on the wrap up (possibly a larger application) you have in mind,and annotating many images for training more ConvNets with fresh images of a selected class.

During the project, you have learned quite a few useful technicalities you can reuse in many projects dealing with images. First of all, you now know how to process different kinds of visual inputs from images, videos, and webcam captures. You also know how to load a frozen model and put it to work, and also how to use a class to access a TensorFlow model.

On the other hand, clearly, the project has some limitations that you may encounter sooner or later, and that may spark the idea to try to integrate your code and make it shine...

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Key benefits

  • Build efficient deep learning pipelines using the popular Tensorflow framework
  • Train neural networks such as ConvNets, generative models, and LSTMs
  • Includes projects related to Computer Vision, stock prediction, chatbots and more

Description

TensorFlow is one of the most popular frameworks used for machine learning and, more recently, deep learning. It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy. This book is your guide to master deep learning with TensorFlow with the help of 10 real-world projects. TensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning. You'll learn how to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks. While doing this, you will build end-to-end deep learning solutions to tackle different real-world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few. You will also develop systems that perform machine translation and use reinforcement learning techniques to play games. By the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidently.

Who is this book for?

This book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow. Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this book.

What you will learn

  • •Set up the TensorFlow environment for deep learning
  • •Construct your own ConvNets for effective image processing
  • •Use LSTMs for image caption generation
  • •Forecast stock prediction accurately with an LSTM architecture
  • •Learn what semantic matching is by detecting duplicate Quora questions
  • •Set up an AWS instance with TensorFlow to train GANs
  • •Train and set up a chatbot to understand and interpret human input
  • •Build an AI capable of playing a video game by itself –and win it!
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Table of Contents

11 Chapters
Recognizing traffic signs using Convnets Chevron down icon Chevron up icon
Recognizing traffic signs using Convnets
The dataset
The CNN network
Image preprocessing
Train the model and make predictions
Follow-up questions
Summary
Annotating Images with Object Detection API Chevron down icon Chevron up icon
Annotating Images with Object Detection API
The Microsoft common objects in context
The TensorFlow object detection API
Presenting our project plan
Provisioning of the project code
Acknowledgements
Summary
Caption Generation for Images Chevron down icon Chevron up icon
Caption Generation for Images
What is caption generation?
Exploring image captioning datasets
Converting words into embeddings
Image captioning approaches
Implementing a caption generation model
Summary
Building GANs for Conditional Image Creation Chevron down icon Chevron up icon
Building GANs for Conditional Image Creation
Introducing GANs
The project
Putting CGAN to work on some examples
Resorting to Amazon Web Service
Acknowledgements
Summary
Stock Price Prediction with LSTM Chevron down icon Chevron up icon
Stock Price Prediction with LSTM
Input datasets – cosine and stock price
Format the dataset
Using regression to predict the future prices of a stock
Long short-term memory – LSTM 101
Stock price prediction with LSTM
Possible follow - up questions
Summary
Create and Train Machine Translation Systems Chevron down icon Chevron up icon
Create and Train Machine Translation Systems
A walkthrough of the architecture
Preprocessing of the corpora
Training the machine translator
Test and translate
Summary
Train and Set up a Chatbot, Able to Discuss Like a Human Chevron down icon Chevron up icon
Train and Set up a Chatbot, Able to Discuss Like a Human
Introduction to the project
The input corpus
Creating the training dataset
Training the chatbot
Chatbox API
Summary
Detecting Duplicate Quora Questions Chevron down icon Chevron up icon
Detecting Duplicate Quora Questions
Presenting the dataset
Starting with basic feature engineering
Creating fuzzy features
Resorting to TF-IDF and SVD features
Mapping with Word2vec embeddings
Testing machine learning models
Building a TensorFlow model
Processing before deep neural networks
Deep neural networks building blocks
Designing the learning architecture
Summary
Building a TensorFlow Recommender System Chevron down icon Chevron up icon
Building a TensorFlow Recommender System
Recommender systems
Matrix factorization for recommender systems
RNN for recommender systems
Summary
Video Games by Reinforcement Learning Chevron down icon Chevron up icon
Video Games by Reinforcement Learning
The game legacy
The OpenAI version
Installing OpenAI on Linux (Ubuntu 14.04 or 16.04)
Exploring reinforcement learning through deep learning
Starting the project
Acknowledgements
Summary
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N31LD Apr 18, 2018
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Three problems with this book.(1) The Kindle version does not render the code well. The code wraps and indentation does not hold up as the page width varies, so it's impossible to tell whether a line of code belongs to one level of indentation or another. Sure, I can download the code from the repo and look at it in Jupyter, but what's the use of the book about code if you can't read the code?(2) The text between the code chunks is frequently trivial, and in many cases should have just been comments in the code. e.g. "And finally, here's the code for training the model with minibatches:"(3) The book assumes a moderately high level of understanding of TensorFlow. Do not buy this book unless you already have a strong understanding of terms like "dropout", "flattenizer", "softmax", and "leaky ReLU activation". Terms like these are used continuously without explanation. If you don't already understand them, this book will not help you understand TensorFlow.Given (3), I have to wonder who the target audience is for this book - presumably if the reader already understands TensorFlow they will have access to code examples for different types of project. If the reader has data science skills but does not understand TensorFlow, it's not helpful. This book missed out on an important niche of helping data scientists learn TensorFlow through practical projects.
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About the authors

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Shanmugamani
Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of TechnologyMadras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.
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Alexey Grigorev
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Alexey Grigorev is a skilled data scientist, machine learning engineer, and software developer with more than 8 years of professional experience. He started his career as a Java developer working at a number of large and small companies, but after a while he switched to data science. Right now, Alexey works as a data scientist at Simplaex, where, in his day-to-day job, he actively uses Java and Python for data cleaning, data analysis, and modeling. His areas of expertise are machine learning and text mining.
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Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

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Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela