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Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide:…

Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide:…

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Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide

Deep learning has become a crucial aspect of artificial intelligence, and PyTorch is one of the most popular frameworks used for building and training deep learning models. In this article, we will provide a step-by-step guide to getting started with deep learning using PyTorch, specifically designed for beginners.

Introduction to PyTorch

PyTorch is an open-source machine learning library developed by Facebook’s AI Research Lab (FAIR). It provides a dynamic computation graph and is particularly well-suited for rapid prototyping and research. PyTorch is known for its simplicity, flexibility, and ease of use, making it an ideal choice for beginners.

Setting Up PyTorch

To get started with PyTorch, you’ll need to install it on your system. You can do this using pip, the Python package manager, by running the following command:

pip install torch torchvision

Once installed, you can verify that PyTorch is working correctly by running a simple script:

python
import torch
print(torch.version)

Basic PyTorch Concepts

Before diving into deep learning, it’s essential to understand some basic PyTorch concepts:

  • Tensors: Tensors are multi-dimensional arrays used to represent data in PyTorch. They can be created using the torch.tensor() function.
  • Autograd: Autograd is PyTorch’s automatic differentiation system, which is used to compute gradients.
  • Modules: Modules are pre-built functions in PyTorch that can be used to create neural networks.

Building a Simple Neural Network

Now that you have a basic understanding of PyTorch, let’s build a simple neural network using the nn.Module class:

python
import torch
import torch.nn as nn

class SimpleNet(nn.Module):
def init(self):
super(SimpleNet, self).init()
self.fc1 = nn.Linear(5, 10) # input layer (5) -> hidden layer (10)
self.fc2 = nn.Linear(10, 5) # hidden layer (10) -> output layer (5)

def forward(self, x):
    x = torch.relu(self.fc1(x))  # activation function for hidden layer
    x = self.fc2(x)
    return x

Initialize the network and optimizer

net = SimpleNet()
optimizer = torch.optim.SGD(net.parameters(), lr=0.01)

Train the network

for epoch in range(100):
optimizer.zero_grad()
inputs = torch.randn(10, 5) # random input
labels = torch.randn(10, 5) # random label
outputs = net(inputs)
loss = torch.mean((outputs – labels) ** 2)
loss.backward()
optimizer.step()
print(f’Epoch {epoch+1}, Loss: {loss.item()}’)

Deep Learning with PyTorch

Now that you have a basic understanding of PyTorch and have built a simple neural network, let’s move on to more advanced topics in deep learning:

  • Convolutional Neural Networks (CNNs): CNNs are used for image classification tasks. PyTorch provides a nn.Conv2d module for building CNNs.
  • Recurrent Neural Networks (RNNs): RNNs are used for sequential data such as text or speech. PyTorch provides a nn.RNN module for building RNNs.
  • Transfer Learning: Transfer learning involves using pre-trained models as a starting point for your own models. PyTorch provides a torchvision library for loading pre-trained models.

Best Practices for Deep Learning with PyTorch

Here are some best practices to keep in mind when working with PyTorch:

  • Use pre-trained models: Pre-trained models can save you a lot of time and effort.
  • Use batch normalization: Batch normalization can help stabilize training and improve performance.
  • Monitor your metrics: Keep an eye on your metrics, such as loss and accuracy, to ensure that your model is training correctly.

Conclusion

In this article, we provided a step-by-step guide to getting started with deep learning using PyTorch. We covered the basics of PyTorch, built a simple neural network, and explored more advanced topics in deep learning. By following these steps and best practices, you can unlock the power of deep learning with PyTorch and start building your own models today.

Further Reading

  • PyTorch Documentation: The official PyTorch documentation provides a wealth of information on getting started with PyTorch.
  • Deep Learning with PyTorch: This book provides a comprehensive introduction to deep learning with PyTorch.
  • PyTorch Tutorials: The official PyTorch tutorials provide a range of examples and exercises to help you get started with PyTorch.

Customers say

Customers find the book easy to understand and follow, with one mentioning it provides a helpful introduction to PyTorch. They appreciate its content, with one customer noting it’s particularly suitable for advanced learners.

13 reviews for Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide:…

  1. Mike Phelps –

    Great beginner text, even for advanced practitioners
    Basic plain clear language. The text is not academic or arrogant, uses simple words and is easy to absorb and understand. The pace is slow and the text is not dense. There is not a ton of math notation, and the rare cases where it is included the text really helps.I love that the author takes you through how you would build and train a neural net without Torch first. I knew how things like gradient descent worked, but this book really helped me -understand- what was happening under the hood, and now when I see my nets going sideways, I can visualize the issues. The rest of the series is the same quality I own all three now.

  2. california_virginia –

    Very good
    This book was amazing, guiding the reader step by step into the intricate structure of the Pytorch framework. It requires that you know the concept of OOP, but the book is very kind about introducing many Python concepts. I bought all three and found that the first two of the series are very good. The third is a bit difficult to follow, because the example becomes quite complex. Yet, overall, if you’re new to DL/NN, this book is a gem. Highly highly recommended.

  3. A.S.A –

    Great starter!
    This is enjoyable and easy to follow. I ordered the other 2 volumes before finishing this one. Interesting!

  4. TS. –

    Excellent insights
    This book does not just shove the code on your face. It explains “how” things work under the hood. I loved this style so much that I made this book and its Volume 2 the textbook for my ECE655 Advanced GPU Programming and Deep Learning class. This book really spoke to me, since I am, just like the author Dan Godoy, are curious people who are not going to be satisfied when they get things to work. We want to know WHY they worked ! We want to know what is under the hood. This style of the author made it a perfect textbook material for a graduate class! The code he provided is a gold mine.LOVE IT!

  5. Jason Yim –

    so enjoyed
    I like the intuitive and easy explanation of the author. I I also learned some important concept here in this book. I guess this book is the best for the beginners

  6. Mark T Wallace –

    Just what I was hoping to learn!
    This book was great. I was able to work through it in a few days, and it was easy to understand. I was familiar with scikit-learn, but it’s been a few years since I used it, so the book was a fast way to get comfortable with PyTorch. The review of ML concepts was just enough depth to make it so I understood the code. I am moving on to volume 2 now!For comparison, a few years ago I bought “Hands-On Machine Learning with Scikit-Learn and TensorFlow” but I was never able to make myself read it. This book, OTOH, was an absolute pleasure.

  7. Jesse –

    Very clear and helpful introduction to PyTorch
    I love this author’s style. He begins with the foundational concepts behind deep learning and walks you through building up increasing levels of abstraction and complexity from hardcoding gradient descent yourself, to implementing it efficiently with PyTorch. In the end, you will be able to code up a clean and compact model in PyTorch, and you will understand what each sub component is doing under the hood.

  8. Consumer Joe –

    Provides all the details but in snippets
    I got the book in Kindle version. It is very readable, and it’s easy to follow and understand the snippets and the incremental versions but I found it to be very difficult to put it all together in my own program because I need to chase all the snippets of the right version instead of having a full beginning to end .py example.In my opinion, the heavy reliance on notebook and not providing full .py examples makes the book less useful than I expected. It’s a matter of ‘plenty of versioned trees’ vs ‘a single best-practice forrest’ if you like.

  9. Shanoj –

    As I near the final chapters of this incredible book, I felt compelled to share my thoughts. For anyone looking to explore the depths of deep learning and PyTorch, this isn’t just a technical guide—it’s a conversation.Reading Daniel Voigt Godoy’s book feels like sitting across the table from a mentor who knows how to make complex topics click. From the very first chapter (Chapter 0 Visualizing Gradient Descent), he breaks down intimidating concepts like autograd and model training loops with an approach so conversational and intuitive, you don’t feel overwhelmed—you feel empowered.What makes this book stand out for me? 🌟1️⃣ 𝗔 𝗕𝗲𝗴𝗶𝗻𝗻𝗲𝗿-𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆 𝗝𝗼𝘂𝗿𝗻𝗲𝘆: Yes, Daniel assumes a bare minimum knowledge of Python and basic machine learning principles, but the way he structures the learning curve is a gift. You don’t feel left behind; instead, you grow chapter by chapter with confidence.2️⃣ 𝗛𝗮𝗻𝗱𝘀-𝗢𝗻: Every explanation comes with practical examples. The included Jupyter notebooks (yes, an entire GitHub treasure trove!) let you get your hands dirty. It’s the kind of interactive learning that sticks with you.3️⃣ 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗧𝗼𝗻𝗲: This is rare. The writing feels personal, like Daniel is right there guiding you. No robotic instructions, no overwhelming jargon. Just clear, actionable guidance that anyone willing to learn can follow.4️⃣ 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗳𝗼𝗿 𝗔𝗹𝗹 𝗟𝗲𝘃𝗲𝗹𝘀: While it’s beginner-friendly, the series builds depth. By the time you finish, you’ll not only know how to train models—you’ll understand why certain techniques work better than others.💡 This book was more than just a technical read for me. It was a reminder that learning complex topics doesn’t have to feel isolating or daunting. It’s proof that great teachers, like Daniel, can bring clarity to even the most challenging subjects.If you’re curious about deep learning or PyTorch, or if you’ve been putting off starting because it seemed too “technical,” trust me—this series is the perfect launchpad.

  10. Amazon カスタマー –

    I’m a fastai lover and trying to understand what’s going on behind the scene. This book shows me the detail about PyTorch’s features like Tensor, Auto-Grad, DataLoader, Model, and so on.If you have some experience of TensorFlow or fastai and you are considering to dive into the PyTorch world, this is the book for you!!

  11. Ishank Dubey –

    1. This book needs some pre-built background on the Math2. If the reader has some background then this book will help them reinforce the concepts and add more practical knowledge3. Writing style is very innovative, I like the idea of having an intuitive understanding with some theoretical background.

  12. Seyedpedram –

    A great buy, with step by step guide from the very beginner.It should have been published in color.

  13. Leon Chant Dakessian –

    Excellent and didactic book with clear explanations and Python codes. Highly recommendable for mastering concepts related to regression and classification tasks, Python codes and the PyTorch library.

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