This book will help you successfully implement deep learning in Python to create smart web applications from scratch. You will learn how deep learning can transform a simple web app into a smart, business-friendly product. You will also develop neural networks using open-source libraries and also integrate them with different web stack front-ends.

• Cover
• Title Page
• Copyright and Credits
• About Packt
• Contributors
• Dedication
• Preface
• Table of Contents
• Section 1: Artificial Intelligence on the Web
• Chapter 1: 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
• Section 2: Using Deep Learning for Web Development
• Chapter 2: 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
• Chapter 3: 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
• Chapter 4: 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
• Section 3: Getting Started with Different Deep Learning APIs for Web Development
• Chapter 5: 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
• Chapter 6: 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
• Chapter 7: 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
• Chapter 8: 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
• Section 4: Deep Learning in Production (Intelligent Web Apps)
• Chapter 9: 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
• Chapter 10: 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 
• Chapter 11: 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
• Chapter 12: 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
• Other Books You May Enjoy
• Index