Hands-On Python Deep Learning for the Web
Anubhav Singh Sayak Paul更新时间：2021-06-24 16:24:16
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Title Page
Copyright and Credits
Hands-On Python Deep Learning for the Web
About Packt
Why subscribe?
Contributors
About the authors
About the reviewer
Packt is searching for authors like you
Dedication
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
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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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更新时间：2021-06-24 16:24:16