Before starting, you will need to ensure that your system meets the following technical requirements:

• Python 3.9: You can download Python from https://www.python.org/downloads/.
• pip (23.3.1) or Anaconda: These are popular package managers for Python. pip comes with Python by default. Anaconda can be downloaded from https://www.anaconda.com/products/distribution.
• torch (2.2.0): The main library we will be using for deep learning in this chapter.
• CUDA (optional): If you have a CUDA-capable GPU on your machine, you can install a version of PyTorch that supports CUDA. This will enable computations on your GPU and can significantly speed up your deep learning experiments.

It’s worth noting that the code presented in this chapter is platform-independent and should run on any system with the preceding requirements satisfied.

The code for this chapter can be found at the following GitHub URL: https://github.com/PacktPublishing/Deep...

To start with PyTorch, we need to install it first. As of the time of writing, PyTorch supports Linux, macOS, and Windows platforms. Here, we will guide you through the installation process on these operating systems.

Getting ready

PyTorch is usually installed via pip or Anaconda. We recommend creating a new Python environment before installing the library, especially if you will be working on multiple Python projects on your system. This is to prevent any conflicts between different versions of Python libraries that different projects may require.

How to do it…

Let’s see how to install PyTorch. We’ll describe how to do this using either pip or Anaconda. We’ll also provide some information about how to use a CUDA environment.

If you’re using pip, Python’s package manager, you can install PyTorch by running the following command in your terminal:

pip install torch

With the Anaconda Python distribution,...

Before we start building neural networks with PyTorch, it is essential to understand the basics of how to manipulate data using this library. In PyTorch, the fundamental unit of data is the tensor, a generalization of matrices to an arbitrary number of dimensions (also known as a multidimensional array).

Getting ready

A tensor can be a number (a 0D tensor), a vector (a 1D tensor), a matrix (a 2D tensor), or any multi-dimensional data (a 3D tensor, a 4D tensor, and so on). PyTorch provides various functions to create and manipulate tensors.

How to do it…

Let’s start by importing PyTorch:

import torch

We can create a tensor in PyTorch using various techniques. Let’s start by creating tensors from lists:

t1 = torch.tensor([1, 2, 3])
print(t1)
t2 = torch.tensor([[1, 2], [3, 4]])
print(t2)

PyTorch can seamlessly integrate with NumPy, allowing for easy tensor creation from NumPy arrays:

import numpy as np
np_array...

After exploring basic tensor operations, let’s now dive into more advanced operations in PyTorch, specifically the linear algebra operations that form the backbone of most numerical computations in deep learning.

Getting ready

Linear algebra is a subset of mathematics. It deals with vectors, vector spaces, and linear transformations between these spaces, such as rotations, scaling, and shearing. In the context of deep learning, we deal with high-dimensional vectors (tensors), and operations on these vectors play a crucial role in the internal workings of models.

How to do it…

Let’s start by revisiting the tensors we created in the previous section:

print(t1)
print(t2)

The dot product of two vectors is a scalar that measures the vectors’ direction and magnitude. In PyTorch, we can calculate the dot product of two 1D tensors using the torch.dot() function:

dot_product = torch.dot(t1, t3)
print(dot_product...

This section will build a simple two-layer neural network from scratch using only basic tensor operations to solve a time series prediction problem. We aim to demonstrate how one might manually implement a feedforward pass, backpropagation, and optimization steps without leveraging PyTorch’s predefined layers and optimization routines.

Getting ready

We use synthetic data for this demonstration. Suppose we have a simple time series data of 100 samples, each with 10 time steps. Our task is to predict the next time step based on the previous ones:

X = torch.randn(100, 10)
y = torch.randn(100, 1)

Now, let’s create a neural network.

How to do it…

Let’s start by defining our model parameters and their initial values. Here, we are creating a simple two-layer network, so we have two sets of weights and biases:

We use the requires_grad_() function to tell PyTorch that we want to compute gradients with...

This recipe walks you through the process of building a feedforward neural network using PyTorch.

Getting ready

Feedforward neural networks, also known as multilayer perceptrons (MLPs), are one of the simplest types of artificial neural networks. The data flows from the input layer to the output layer, passing through hidden layers without any loop. In this type of neural network, all hidden units in one layer are connected to the units of the following layer.

How to do it…

Let’s create a simple feedforward neural network using PyTorch. First, we need to import the necessary PyTorch modules:

import torch
import torch.nn as nn

Now, we can define a simple feedforward neural network with one hidden layer:

class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(10...

Recurrent Neural Networks (RNNs) are a class of neural networks that are especially effective for tasks involving sequential data, such as time series forecasting and natural language processing.

Getting ready

RNNs use sequential information by having hidden layers capable of passing information from one step in the sequence to the next.

How to do it…

Similar to the feedforward network, we begin by defining our RNN class. For simplicity, let’s define a single-layer RNN:

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size
        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size...

RNNs suffer from a fundamental problem of “vanishing gradients” where, due to the nature of backpropagation in neural networks, the influence of earlier inputs on the overall error diminishes drastically as the sequence gets longer. This is especially problematic in sequence processing tasks where long-term dependencies exist (i.e., future outputs depend on much earlier inputs).

Getting ready

LSTM networks were introduced to overcome this problem. They use a more complex internal structure for each of their cells compared to RNNs. Specifically, an LSTM has the ability to decide which information to discard or to store based on an internal structure called a cell. This cell uses gates (input, forget, and output gates) to control the flow of information into and out of the cell. This helps maintain and manipulate the “long-term” information, thereby mitigating the vanishing gradient problem.

How to do it…

Convolutional neural networks (CNNs) are a class of neural networks particularly effective for tasks involving grid-like input data such as images, audio spectrograms, and even certain types of time series data.

Getting ready

The central idea of CNNs is to apply a convolution operation on the input data with convolutional filters (also known as kernels), which slide over the input data to produce output feature maps.

How to do it…

For simplicity, let’s define a single-layer 1D convolutional neural network, which is particularly suited for time series and sequence data. In PyTorch, we can use the nn.Conv1d layer for this:

class ConvNet(nn.Module):
    def __init__(self,
        input_size,
        hidden_size,
        output_size,
        ...