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Practical Deep Learning A Python Based Introduction 1st Edition by Ronald T Kneusel ISBN 1718500742 9781718500747

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Practical Deep Learning A Python Based Introduction 1st Edition Ronald T. Kneusel [Ron Kneusel] Digital Instant Download

Author(s): Ronald T. Kneusel [Ron Kneusel]
ISBN(s): 9781718500747, 1718500742
Edition: 1
File Details: PDF, 13.59 MB
Year: 2021
Language: english
SKU: EB-34803998 Category: Tag:

Practical Deep Learning A Python Based Introduction 1st Edition by Ronald T Kneusel – Ebook PDF Instant Download/Delivery: 1718500742, 9781718500747
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ISBN 10: 1718500742 
ISBN 13: 9781718500747
Author: Ronald T Kneusel

Practical Deep Learning teaches total beginners how to build the datasets and models needed to train neural networks for your own DL projects. If you’ve been curious about artificial intelligence and machine learning but didn’t know where to start, this is the book you’ve been waiting for. Focusing on the subfield of machine learning known as deep learning, it explains core concepts and gives you the foundation you need to start building your own models. Rather than simply outlining recipes for using existing toolkits, Practical Deep Learning teaches you the why of deep learning and will inspire you to explore further. All you need is basic familiarity with computer programming and high school math—the book will cover the rest. After an introduction to Python, you’ll move through key topics like how to build a good training dataset, work with the scikit-learn and Keras libraries, and evaluate your models’ performance. You’ll also learn: How to use classic machine learning models like k-Nearest Neighbors, Random Forests, and Support Vector Machines How neural networks work and how they’re trained How to use convolutional neural networks How to develop a successful deep learning model from scratch You’ll conduct experiments along the way, building to a final case study that incorporates everything you’ve learned.  The perfect introduction to this dynamic, ever-expanding field, Practical Deep Learning will give you the skills and confidence to dive into your own machine learning projects.

Practical Deep Learning A Python Based Introduction 1st Table of contents:

1 GETTING STARTED
The Operating Environment
Installing the Toolkits
Basic Linear Algebra
Statistics and Probability
Graphics Processing Units
Summary
2 USING PYTHON
The Python Interpreter
Statements and Whitespace
Variables and Basic Data Structures
Control Structures
Functions
Modules
Summary
3 USING NUMPY
Why NumPy?
Basic Arrays
Accessing Elements in an Array
Operators and Broadcasting
Array Input and Output
Random Numbers
NumPy and Images
Summary
4 WORKING WITH DATA
Classes and Labels
Features and Feature Vectors
Features of a Good Dataset
Data Preparation
Training, Validation, and Test Data
Look at Your Data
Summary
5 BUILDING DATASETS
Irises
Breast Cancer
MNIST Digits
CIFAR-10
Data Augmentation
Summary
6 CLASSICAL MACHINE LEARNING
Nearest Centroid
k-Nearest Neighbors
Naïve Bayes
Decision Trees and Random Forests
Support Vector Machines
Summary
7 EXPERIMENTS WITH CLASSICAL MODELS
Experiments with the Iris Dataset
Experiments with the Breast Cancer Dataset
Experiments with the MNIST Dataset
Classical Model Summary
When to Use Classical Models
Summary
8 INTRODUCTION TO NEURAL NETWORKS
Anatomy of a Neural Network
Implementing a Simple Neural Network
Summary
9 TRAINING A NEURAL NETWORK
A High-Level Overview
Gradient Descent
Stochastic Gradient Descent
Backpropagation
Loss Functions
Weight Initialization
Overfitting and Regularization
Summary
10 EXPERIMENTS WITH NEURAL NETWORKS
Our Dataset
The MLPClassifier Class
Architecture and Activation Functions
Batch Size
Base Learning Rate
Training Set Size
L2 Regularization
Momentum
Weight Initialization
Feature Ordering
Summary
11 EVALUATING MODELS
Definitions and Assumptions
Why Accuracy Is Not Enough
The 2 × 2 Confusion Matrix
Metrics Derived from the 2 × 2 Confusion Matrix
More Advanced Metrics
The Receiver Operating Characteristics Curve
Handling Multiple Classes
Summary
12 INTRODUCTION TO CONVOLUTIONAL NEURAL NETWORKS
Why Convolutional Neural Networks?
Convolution
Anatomy of a Convolutional Neural Network
Convolutional Layers
Pooling Layers
Fully Connected Layers
Fully Convolutional Layers
Step by Step
Summary
13 EXPERIMENTS WITH KERAS AND MNIST
Building CNNs in Keras
Basic Experiments
Fully Convolutional Networks
Scrambled MNIST Digits
Summary
14 EXPERIMENTS WITH CIFAR-10
A CIFAR-10 Refresher
Working with the Full CIFAR-10 Dataset
Animal or Vehicle?
Binary or Multiclass?
Transfer Learning
Fine-Tuning a Model
Summary
15 A CASE STUDY: CLASSIFYING AUDIO SAMPLES
Building the Dataset
Classifying the Audio Features
Spectrograms
Classifying Spectrograms
Ensembles
Summary
16 GOING FURTHER
Going Further with CNNs
Reinforcement Learning and Unsupervised Learning
Generative Adversarial Networks
Recurrent Neural Networks

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