The Deep Learning with PyTorch Workshop

By : Hyatt Saleh

The Deep Learning with PyTorch Workshop

By: Hyatt Saleh

Overview of this book

Want to get to grips with one of the most popular machine learning libraries for deep learning? The Deep Learning with PyTorch Workshop will help you do just that, jumpstarting your knowledge of using PyTorch for deep learning even if you’re starting from scratch. It’s no surprise that deep learning’s popularity has risen steeply in the past few years, thanks to intelligent applications such as self-driving vehicles, chatbots, and voice-activated assistants that are making our lives easier. This book will take you inside the world of deep learning, where you’ll use PyTorch to understand the complexity of neural network architectures. The Deep Learning with PyTorch Workshop starts with an introduction to deep learning and its applications. You’ll explore the syntax of PyTorch and learn how to define a network architecture and train a model. Next, you’ll learn about three main neural network architectures - convolutional, artificial, and recurrent - and even solve real-world data problems using these networks. Later chapters will show you how to create a style transfer model to develop a new image from two images, before finally taking you through how RNNs store memory to solve key data issues. By the end of this book, you’ll have mastered the essential concepts, tools, and libraries of PyTorch to develop your own deep neural networks and intelligent apps.

Preface

About the Book

1. Introduction to Deep Learning and PyTorch

Introduction

Why Deep Learning?

Introduction to PyTorch

Summary

Free Chapter

2. Building Blocks of Neural Networks

Introduction

Introduction to Neural Networks

Data Preparation

Building a Deep Neural Network

Summary

3. A Classification Problem Using DNN

Introduction

Problem Definition

Dealing with an Underfitted or Overfitted Model

Deploying Your Model

Summary

4. Convolutional Neural Networks

Summary

5. Style Transfer

Introduction

Style Transfer

Implementation of Style Transfer Using the VGG-19 Network Architecture

Summary

6. Analyzing the Sequence of Data with RNNs

Introduction

Recurrent Neural Networks

Long Short-Term Memory Networks

LSTM Networks in PyTorch

Natural Language Processing

Sentiment Analysis in PyTorch

Summary

Appendix

1. Introduction to Deep Learning and PyTorch

2. Building Blocks of Neural Networks

3. A Classification Problem Using DNNs

4. Convolutional Neural Networks

5. Style Transfer

6. Analyzing the Sequence of Data with RNNs

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3. A Classification Problem Using DNNs

Activity 3.01: Building an ANN

Solution:

Import the following libraries:

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from sklearn.metrics import accuracy_score
import torch
from torch import nn, optim
import torch.nn.functional as F
import matplotlib.pyplot as plt
torch.manual_seed(0)

Read the previously prepared dataset, which should have been named dccc_prepared.csv:
```
data = pd.read_csv("dccc_prepared.csv")
data.head()
```
The output should be as follows:
Figure 3.14: dccc_prepared.csv

Separate the features from the target:

X = data.iloc[:,:-1]
y = data["default payment next month"]

Using scikit-learn's train_test_split function, split the dataset into training, validation, and testing sets. Use a 60:20:20 split ratio. Set random_state to 0:
```
X_new, X_test, \
y_new, y_test = train_test_split(X, y, test_size=0.2, \
  ...
```

The Deep Learning with PyTorch Workshop

By : Hyatt Saleh

The Deep Learning with PyTorch Workshop

By: Hyatt Saleh

Overview of this book

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3. A Classification Problem Using DNNs

Activity 3.01: Building an ANN