Browse Library
Sign In Start Free Trial
Close icon Search icon
Account
Browse Library Advanced Search Sign In Start Free Trial

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying Artificial Intelligence with Python
  • Table Of Contents Toc
  • Feedback & Rating feedback
Artificial Intelligence with Python

Artificial Intelligence with Python - Second Edition

By : Alberto Artasanchez, Joshi
3.9 (20)
Buy this Book
close
close
Artificial Intelligence with Python

Artificial Intelligence with Python

3.9 (20)
By: Alberto Artasanchez, Joshi
Buy this Book

Overview of this book

Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques.
Table of Contents (26 chapters)
close
close
close
Preface
Icon
Preface
Icon
Who this book is for
Icon
What this book covers
Icon
What you need for this book
Icon
Get in touch
1
Introduction to Artificial Intelligence
Icon
Introduction to Artificial Intelligence
Icon
What is AI?
Icon
Why do we need to study AI?
Icon
Branches of AI
Icon
The five tribes of machine learning
Icon
Defining intelligence using the Turing test
Icon
Making machines think like humans
Icon
Building rational agents
Icon
General Problem Solver
Icon
Building an intelligent agent
Icon
Installing Python 3
Icon
Installing packages
Icon
Loading data
Icon
Summary
Lock Free Chapter
2
Fundamental Use Cases for Artificial Intelligence
Icon
Fundamental Use Cases for Artificial Intelligence
Icon
Representative AI use cases
Icon
Digital personal assistants and chatbots
Icon
Personal chauffeur
Icon
Shipping and warehouse management
Icon
Human health
Icon
Knowledge search
Icon
Recommendation systems
Icon
The smart home
Icon
Gaming
Icon
Movie making
Icon
Underwriting and deal analysis
Icon
Data cleansing and transformation
Icon
Summary
Icon
References
3
Machine Learning Pipelines
Icon
Machine Learning Pipelines
Icon
What is a machine learning pipeline?
Icon
Problem definition
Icon
Data ingestion
Icon
Data preparation
Icon
Data segregation
Icon
Model training
Icon
Summary
4
Feature Selection and Feature Engineering
Icon
Feature Selection and Feature Engineering
Icon
Feature selection
Icon
Feature engineering
Icon
Outlier management
Icon
One-hot encoding
Icon
Log transform
Icon
Scaling
Icon
Date manipulation
Icon
Summary
5
Classification and Regression Using Supervised Learning
Icon
Classification and Regression Using Supervised Learning
Icon
Supervised versus unsupervised learning
Icon
What is classification?
Icon
Preprocessing data
Icon
Label encoding
Icon
Logistic regression classifiers
Icon
The Naïve Bayes classifier
Icon
Confusion matrixes
Icon
Support Vector Machines
Icon
Classifying income data using Support Vector Machines
Icon
What is regression?
Icon
Building a single-variable regressor
Icon
Building a multivariable regressor
Icon
Estimating housing prices using a Support Vector Regressor
Icon
Summary
6
Predictive Analytics with Ensemble Learning
Icon
Predictive Analytics with Ensemble Learning
Icon
What are decision trees?
Icon
What is ensemble learning?
Icon
What are random forests and extremely random forests?
Icon
Dealing with class imbalance
Icon
Finding optimal training parameters using grid search
Icon
Computing relative feature importance
Icon
Predicting traffic using an extremely random forest regressor
Icon
Summary
7
Detecting Patterns with Unsupervised Learning
Icon
Detecting Patterns with Unsupervised Learning
Icon
What is unsupervised learning?
Icon
Clustering data with the K-Means algorithm
Icon
What are Gaussian Mixture Models?
Icon
Finding subgroups in stock market using the Affinity Propagation model
Icon
Segmenting the market based on shopping patterns
Icon
Summary
8
Building Recommender Systems
Icon
Building Recommender Systems
Icon
Extracting the nearest neighbors
Icon
Building a K-nearest neighbors classifier
Icon
Computing similarity scores
Icon
Finding similar users using collaborative filtering
Icon
Building a movie recommendation system
Icon
Summary
9
Logic Programming
Icon
Logic Programming
Icon
What is logic programming?
Icon
Understanding the building blocks of logic programming
Icon
Solving problems using logic programming
Icon
Installing Python packages
Icon
Matching mathematical expressions
Icon
Validating primes
Icon
Parsing a family tree
Icon
Analyzing geography
Icon
Building a puzzle solver
Icon
Summary
10
Heuristic Search Techniques
Icon
Heuristic Search Techniques
Icon
Is heuristic search artificial intelligence?
Icon
What is heuristic search?
Icon
Constraint satisfaction problems
Icon
Local search techniques
Icon
Constructing a string using greedy search
Icon
Solving a problem with constraints
Icon
Solving the region-coloring problem
Icon
Building an 8-puzzle solver
Icon
Building a maze solver
Icon
Summary
11
Genetic Algorithms and Genetic Programming
Icon
Genetic Algorithms and Genetic Programming
Icon
The evolutionists tribe
Icon
Understanding evolutionary and genetic algorithms
Icon
Fundamental concepts in genetic algorithms
Icon
Generating a bit pattern with predefined parameters
Icon
Visualizing the evolution
Icon
Solving the symbol regression problem
Icon
Building an intelligent robot controller
Icon
Genetic programming use cases
Icon
Summary
Icon
References
12
Artificial Intelligence on the Cloud
Icon
Artificial Intelligence on the Cloud
Icon
Why are companies migrating to the cloud?
Icon
The top cloud providers
Icon
Amazon Web Services (AWS)
Icon
Microsoft Azure
Icon
Google Cloud Platform (GCP)
Icon
Summary
13
Building Games with Artificial Intelligence
Icon
Building Games with Artificial Intelligence
Icon
Using search algorithms in games
Icon
Combinatorial search
Icon
Installing the easyAI library
Icon
Building a bot to play Last Coin Standing
Icon
Building a bot to play Tic-Tac-Toe
Icon
Building two bots to play Connect Four™ against each other
Icon
Building two bots to play Hexapawn against each other
Icon
Summary
14
Building a Speech Recognizer
Icon
Building a Speech Recognizer
Icon
Working with speech signals
Icon
Visualizing audio signals
Icon
Transforming audio signals to the frequency domain
Icon
Generating audio signals
Icon
Synthesizing tones to generate music
Icon
Extracting speech features
Icon
Recognizing spoken words
Icon
Summary
15
Natural Language Processing
Icon
Natural Language Processing
Icon
Introduction and installation of packages
Icon
Tokenizing text data
Icon
Converting words to their base forms using stemming
Icon
Converting words to their base forms using lemmatization
Icon
Dividing text data into chunks
Icon
Extracting the frequency of terms using the Bag of Words model
Icon
Building a category predictor
Icon
Constructing a gender identifier
Icon
Building a sentiment analyzer
Icon
Topic modeling using Latent Dirichlet Allocation
Icon
Summary
16
Chatbots
Icon
Chatbots
Icon
The future of chatbots
Icon
Chatbots today
Icon
Chatbot concepts
Icon
A well-architected chatbot
Icon
Chatbot platforms
Icon
Creating a chatbot using DialogFlow
Icon
Summary
17
Sequential Data and Time Series Analysis
Icon
Sequential Data and Time Series Analysis
Icon
Understanding sequential data
Icon
Handling time series data with Pandas
Icon
Slicing time series data
Icon
Operating on time series data
Icon
Extracting statistics from time series data
Icon
Generating data using Hidden Markov Models
Icon
Identifying alphabet sequences with Conditional Random Fields
Icon
Stock market analysis
Icon
Summary
18
Image Recognition
Icon
Image Recognition
Icon
Importance of image recognition
Icon
OpenCV
Icon
Frame differencing
Icon
Tracking objects using color spaces
Icon
Object tracking using background subtraction
Icon
Building an interactive object tracker using the CAMShift algorithm
Icon
Optical flow-based tracking
Icon
Face detection and tracking
Icon
Eye detection and tracking
Icon
Summary
19
Neural Networks
Icon
Neural Networks
Icon
Introduction to neural networks
Icon
Building a Perceptron-based classifier
Icon
Constructing a single-layer neural network
Icon
Constructing a multi-layer neural network
Icon
Building a vector quantizer
Icon
Analyzing sequential data using recurrent neural networks
Icon
Visualizing characters in an optical character recognition database
Icon
Building an optical character recognition engine
Icon
Summary
20
Deep Learning with Convolutional Neural Networks
chevron up
Icon
Deep Learning with Convolutional Neural Networks
Icon
The basics of Convolutional Neural Networks
Icon
Architecture of CNNs
Icon
Types of layers in a CNN
Icon
Building a perceptron-based linear regressor
Icon
Building an image classifier using a single-layer neural network
Icon
Building an image classifier using a Convolutional Neural Network
Icon
Summary
Icon
Reference
21
Recurrent Neural Networks and Other Deep Learning Models
Icon
Recurrent Neural Networks and Other Deep Learning Models
Icon
The basics of Recurrent Neural Networks
Icon
Architecture of RNNs
Icon
A language modeling use case
Icon
Training an RNN
Icon
Summary
22
Creating Intelligent Agents with Reinforcement Learning
Icon
Creating Intelligent Agents with Reinforcement Learning
Icon
Understanding what it means to learn
Icon
Reinforcement learning versus supervised learning
Icon
Real-world examples of reinforcement learning
Icon
Building blocks of reinforcement learning
Icon
Creating an environment
Icon
Building a learning agent
Icon
Summary
23
Artificial Intelligence and Big Data
Icon
Artificial Intelligence and Big Data
Icon
Big data basics
Icon
The three V's of big data
Icon
Big data and machine learning
Icon
NoSQL Databases
Icon
Summary
24
Other Books You May Enjoy
Icon
Other Books You May Enjoy
25
Index
Icon
Index

Building a perceptron-based linear regressor

Before we build a CNN, let's set the stage with a more basic model and the see how we can improve using CNNs. In this section we will see how to build a linear regression model using perceptrons. We have already seen linear regression in previous chapters, but this section is about building a linear regression model using a neural network approach.

We will be using TensorFlow in this chapter. It is a popular deep learning package that's widely used to build various real-world systems. In this section, we will get familiar with how it works. Make sure to install it before you proceed. The installation instructions can be found here:

https://www.tensorflow.org/get_started/os_setup

Once you verify that it's installed, create a new Python file and import the following packages:

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf

We will be generating some data points and see...

Visually different images
CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
Artificial Intelligence with Python
End of Section 20
Next Section
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist download Download options
font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected
Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful
Order Page Read Now

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon