Hands-On Python Deep Learning for the Web by Singh, Anubhav -- Read -- Imperial Library of Trantor

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Hands-On Python Deep Learning for the Web

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Dedication Preface

Who this book is for What this book covers To get the most out of this book

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Artificial Intelligence on the Web Demystifying Artificial Intelligence and Fundamentals of Machine Learning

Introduction to artificial intelligence and its types

Factors responsible for AI propulsion

Data Advancements in algorithms Advancements in hardware The democratization of high-performance computing

ML – the most popular form of AI What is DL? The relation between AI, ML, and DL Revisiting the fundamentals of ML

Types of ML

Supervised learning Unsupervised learning Reinforcement learning Semi-supervised learning

Necessary terminologies

Train, test, and validation sets Bias and variance Overfitting and underfitting Training error and generalization error

A standard ML workflow

Data retrieval Data preparation

Exploratory Data Analysis (EDA) Data processing and wrangling Feature engineering and extraction/selection

Modeling

Model training Model evaluation Model tuning

Model comparison and selection Deployment and monitoring

The web before and after AI

Chatbots Web analytics Spam filtering Search

Biggest web-AI players and what are they doing with AI

Google

Google Search Google Translate Google Assistant Other products

Facebook

Fake profiles Fake news and disturbing content Other uses

Amazon

Alexa Amazon robotics DeepLens

Summary

Using Deep Learning for Web Development Getting Started with Deep Learning Using Python

Demystifying neural networks

Artificial neurons

Anatomy of a linear neuron Anatomy of a nonlinear neuron

A note on the input and output layers of a neural network Gradient descent and backpropagation

Different types of neural network

Convolutional neural networks Recurrent neural networks

Feeding the letters to the network Initializing the weight matrix and more Putting the weight matrices together Applying activation functions and the final output

Exploring Jupyter Notebooks

Installing Jupyter Notebook

Installation using pip Installation using Anaconda

Verifying the installation Jupyter Notebooks

Setting up a deep-learning-based cloud environment

Setting up an AWS EC2 GPU deep learning environment

Step 1: Creating an EC2 GPU-enabled instance Step 2: SSHing into your EC2 instance Step 3: Installing CUDA drivers on the GPU instance Step 4: Installing the Anaconda distribution of Python Step 5: Run Jupyter

Deep learning on Crestle Other deep learning environments

Exploring NumPy and pandas

NumPy

NumPy arrays Basic NumPy array operations NumPy arrays versus Python lists

Array slicing over multiple rows and columns Assignment over slicing

Pandas

Summary

Creating Your First Deep Learning Web Application

Technical requirements Structuring a deep learning web application

A structure diagram of a general deep learning web application

Understanding datasets

The MNIST dataset of handwritten digits Exploring the dataset

Creating functions to read the image files Creating functions to read label files A summary of the dataset

Implementing a simple neural network using Python

Importing the necessary modules Reusing our functions to load the image and label files Reshaping the arrays for processing with Keras Creating a neural network using Keras Compiling and training a Keras neural network Evaluating and storing the model

Creating a Flask API to work with server-side Python

Setting up the environment Uploading the model structure and weights Creating our first Flask server Importing the necessary modules Loading data into the script runtime and setting the model Setting the app and index function Converting the image function Prediction APIs

Using the API via cURL and creating a web client using Flask

Using the API via cURL Creating a simple web client for the API

Improving the deep learning backend Summary

Getting Started with TensorFlow.js

Technical requirements The fundamentals of TF.js

What is TensorFlow? What is TF.js? Why TF.js? The basic concepts of TF.js

Tensors Variables Operators Models and layers

A case study using TF.js

A problem statement for our TF.js mini-project The Iris flower dataset

Your first deep learning web application with TF.js

Preparing the dataset Project architecture Starting up the project Creating a TF.js model Training the TF.js model Predicting using the TF.js model Creating a simple client Running the TF.js web app

Advantages and limitations of TF.js Summary

Getting Started with Different Deep Learning APIs for Web Development Deep Learning through APIs

What is an API? The importance of using APIs How is an API different from a library? Some widely known deep learning APIs Some lesser-known deep learning APIs Choosing a deep learning API provider Summary

Deep Learning on Google Cloud Platform Using Python

Technical requirements Setting up your GCP account Creating your first project on GCP Using the Dialogflow API in Python

Creating a Dialogflow account Creating a new agent Creating a new intent Testing your agent Installing the Dialogflow Python SDK Creating a GCP service account Calling the Dialogflow agent using Python API

Using the Cloud Vision API in Python

The importance of using pre-trained models Setting up the Vision Client libraries The Cloud Vision API calling using Python

Using the Cloud Translation API in Python

Setting up the Cloud Translate API for Python Using the Google Cloud Translation Python library

Summary

DL on AWS Using Python: Object Detection and Home Automation

Technical requirements Getting started in AWS A short tour of the AWS offerings Getting started with boto3

Configuring environment variables and installing boto3 Loading up the environment variables in Python Creating an S3 bucket Accessing S3 from Python code with boto3

Using the Rekognition API in Python Using the Alexa API in Python

Prerequisites and a block diagram of the project Creating a configuration for the skill Setting up Login with Amazon Creating the skill Configuring the AWS Lambda function Creating the Lambda function Configuring the Alexa skill Setting up Amazon DynamoDB for the skill Deploying the code for the AWS Lambda function Testing the Lambda function Testing the AWS Home Automation skill

Summary

Deep Learning on Microsoft Azure Using Python

Technical requirements Setting up your account in Azure A walk-through of the deep learning services provided by Azure Object detection using the Face API and Python

The initial setup Consuming the Face API from Python code

Extracting text information using the Text Analytics API and Python

Using the Text Analytics API from Python code

An introduction to CNTK

Getting started with CNTK

Installation on a local machine Installation on Google Colaboratory

Creating a CNTK neural network model Training the CNTK model Testing and saving the CNTK model

A brief introduction to Django web development

Getting started with Django Creating a new Django project Setting up the home page template

Making predictions using CNTK from the Django project

Setting up the predict route and view Making the necessary module imports Loading and predicting using the CNTK model Testing the web app

Summary

Deep Learning in Production (Intelligent Web Apps) A General Production Framework for Deep Learning-Enabled Websites

Technical requirements Defining the problem statement

Building a mental model of the project Avoiding the chances of getting erroneous data in the first place

How not to build an AI backend

Expecting the AI part of the website to be real time Assuming the incoming data from a website is ideal

A sample end-to-end AI-integrated web application

Data collection and cleanup Building the AI model

Making the necessary imports Reading the dataset and preparing cleaning functions Slicing out the required data Applying text cleaning Splitting the dataset into train and test parts Aggregating text about products and users Creating TF-IDF vectorizers of users and products Creating an index of users and products by the ratings provided Creating the matrix factorization function Saving the model as pickle

Building an interface

Creating an API to answer search queries Creating an interface to use the API

Summary

Securing Web Apps with Deep Learning

Technical requirements The story of reCAPTCHA Malicious user detection An LSTM-based model for authenticating users

Building a model for an authentication validity check Hosting the custom authentication validation model

A Django-based app for using an API

The Django project setup Creating an app in the project Linking the app to the project Adding routes to the website Creating the route handling file in the billboard app Adding authentication routes and configurations Creating the login page Creating a logout view Creating a login page template The billboard page template Adding to Billboard page template The billboard model Creating the billboard view Creating bills and adding views Creating the admin user and testing it

Using reCAPTCHA in web applications with Python Website security with Cloudflare Summary

DIY - A Web DL Production Environment

Technical requirements An overview of DL in production methods

A web API service Online learning Batch forecasting Auto ML

Popular tools for deploying ML in production

creme Airflow AutoML

Implementing a demonstration DL web environment

Building a predictive model

Step 1 – Importing the necessary modules Step 2 – Loading the dataset and observing Step 3 – Separating the target variable Step 4 – Performing scaling on the features Step 5 – Splitting the dataset into test and train datasets Step 6 – Creating a neural network object in sklearn Step 7 – Performing the training

Implementing the frontend Implementing the backend

Deploying the project to Heroku Security measures, monitoring techniques, and performance optimization Summary

Creating an E2E Web App Using DL APIs and Customer Support Chatbot

Technical requirements An introduction to NLP

Corpus Parts of speech Tokenization Stemming and lemmatization Bag of words Similarity

An introduction to chatbots Creating a Dialogflow bot with the personality of a customer support representative

Getting started with Dialogflow

Step 1 – Opening the Dialogflow console Step 2 – Creating a new agent Step 3 – Understanding the dashboard Step 4 – Creating the intents

Step 4.1 – Creating HelpIntent Step 4.2 – Creating the CheckOrderStatus intent

Step 5 – Creating a webhook Step 6 – Creating a Firebase cloud function

Step 6.1 – Adding the required packages to package.json Step 6.2 – Adding logic to index.js

Step 7 – Adding a personality to the bot

Using ngrok to facilitate HTTPS APIs on localhost Creating a testing UI using Django to manage orders

Step 1 – Creating a Django project Step 2 – Creating an app that uses the API of the order management system Step 3 – Setting up settings.py

Step 3.1 – Adding the apiui app to the list of installed apps Step 3.2 – Removing the database setting

Step 4 – Adding routes to apiui Step 5 – Adding routes within the apiui app Step 6 – Creating the views required

Step 6.1 – Creating indexView Step 6.2 – Creating viewOrder

Step 7 – Creating the templates

Speech recognition and speech synthesis on a web page using the Web Speech API

Step 1 – Creating the button element Step 2 – Initializing the Web Speech API and performing configuration Step 3 – Making a call to the Dialogflow agent Step 4 – Creating a Dialogflow API proxy on Dialogflow Gateway by Ushakov

Step 4.1 – Creating an account on Dialogflow Gateway Step 4.2 – Creating a service account for your Dialogflow agent project Step 4.3 – Uploading the service key file to Dialogflow Gateway

Step 5 – Adding a click handler for the button

Summary

Appendix: Success Stories and Emerging Areas in Deep Learning on the Web

Success stories

Quora Duolingo Spotify Google Search/Photos

Key emerging areas

Audio search Reading comprehension Detection of fake news on social media

Summary

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