Neural Networks Explained: What They Are and How to Build One in Python

Saudações, comunidade do **webmastersmz.com**!

Como especialista em tecnologia, analisei detalhadamente o tópico em inglês **"My First GitHub Project: From a Local Folder to GitHub Using Git and SSH"**. Este é um marco fundamental para qualquer desenvolvedor ou entusiasta de tecnologia que está a dar os primeiros passos no controlo de versões e colaboração de código.

Abaixo, destaco os pontos técnicos principais abordados no tópico:

1. **Transição do Desenvolvimento Local para o Repositório Remoto:** O autor aborda a necessidade lógica de retirar o código de uma pasta local na máquina de desenvolvimento e levá-lo para a nuvem (GitHub). Isto mitiga o risco de perda de dados e serve como base para o trabalho em equipa.
2. **Inicialização do Repositório (`git init`):** O processo começa com a transformação de um diretório comum num repositório Git ativo, permitindo o rastreio de alterações (*commits*).
3. **Autenticação Segura via SSH:** Em vez de usar métodos obsoletos baseados em palavras-passe (como os *Personal Access Tokens* para operações via HTTPS, embora estes ainda sejam comuns), o tópico enfatiza a configuração de chaves SSH (*Secure Shell*). Esta é uma prática altamente recomendada no ecossistema tecnológico atual, pois garante uma comunicação encriptada e sem atritos entre a máquina local e os servidores do GitHub.
4. **Vinculação e Envio (`git remote add origin` e `git push`):** Por fim, demonstra-se como conectar o repositório local ao remoto e publicar a branch principal (`main`), concluindo o ciclo básico de implementação.

**Incentivo ao Debate:**
Para os membros mais experientes da nossa comunidade, como é que vocês costumam gerir as vossas chaves SSH ao trabalhar com múltiplos projetos e contas (por exemplo, pessoal e profissional) na mesma máquina? Utilizam ficheiros `~/.ssh/config` para simplificar? E para quem está a começar, qual foi a maior dificuldade ao executar o primeiro `git push`? Deixem as vossas opiniões e dúvidas aqui nos comentários para enriquecermos este debate!

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                     Neural Networks Explained: What They Are and How to Build One in Python  
               



Tópico:
                     Neural Networks Explained: What They Are and How to Build One in Python  
               
Categoria: Tutoriais | FreeCodeCamp Premium
Idioma Principal: Português (Conteúdo de Tecnologia)

Conteúdo do Tutorial / Guia Passo a Passo:
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Have you ever wondered how a computer can recognize a handwritten number, predict whether an email is spam, recommend a video, or understand a sentence?

A lot of modern AI systems rely on something called a neural network.

Now, the name can make them sound much more complicated than they really are. You might imagine that you need advanced calculus, a huge computer, and thousands of lines of code to build one.

You don't.

At its most basic level, a neural network is a mathematical model that takes some numbers as input, performs calculations on those numbers, makes a prediction, checks how far that prediction was from the correct answer, and then adjusts itself so it can do a little better next time.

In this tutorial, we're going to build one ourselves using Python and NumPy.

Here's What We'll Cover:

• 1. What Is a Neural Network?

• 2. Why Are They Called Neural Networks?

• 3. The Three Main Parts of a Neural Network

• 4. What Is a Neuron?

• 5. What Is a Weight?

• 6. What Is a Bias?

• 7. Why Do We Need Activation Functions?

• 8. Building Our First Neuron in Python

• 9. From One Neuron to a Layer

• 10. How Does a Neural Network Actually Learn?

• 11. Predictions and Loss

• 12. What Are Gradients?

• 13. What Is Gradient Descent?

• 14. What Is Backpropagation?

• 15. The Complete Learning Cycle

• 16. Let's Build a Neural Network From Scratch

• 17. Understanding the Network Architecture

• 18. Setting Up the Data

• 19. Creating the Weights and Biases

• 20. The Sigmoid Function

• 21. Forward Propagation

• 22. Calculating the Loss

• 23. Backpropagation in Code

• 24. Updating the Weights

• 25. The Complete NumPy Neural Network

• 26. Testing the Network

• 27. Why Did We Need a Hidden Layer?

• 28. What Happens in a Larger Neural Network?

• 29. Do You Have to Build Neural Networks From Scratch?

• 30. Building the Same Network With PyTorch

• 31. Training the Network With PyTorch

• 32. NumPy vs. PyTorch

• 33. What Is Deep Learning?

• 34. Where Are Neural Networks Used?

• 35. The Whole Process in One Picture

• 36. The Most Important Ideas to Remember

• 37. What Should You Learn Next?

• Final Takeaway

We'll start with a single artificial neuron, then gradually put together a complete neural network. By the end, you'll understand what weights and biases are, what activation functions do, how a network learns from its mistakes, what backpropagation and gradient descent actually mean, and how all of those pieces fit together.

You don't need to know advanced machine learning to follow along. Some basic Python and algebra will help, but I'll explain the important math as we go.

1. What Is a Neural Network?

Let's start with a simple example.

Imagine that we want a computer to predict whether a student will pass an exam.

We could give the computer information such as:

• How many hours the student studied

• How many practice questions they completed

• Their previous test score

For example:

Código Selecionar Expandir

Study Hours = 5 Practice Questions = 80 Previous Score = 82
We also know whether the student actually passed:

Código Selecionar Expandir

Passed = 1
After seeing many examples like this, we want the computer to learn a pattern.

Maybe students who study more tend to perform better. Maybe previous test scores are useful. Maybe practice questions are helpful, too.

Instead of writing all of those rules ourselves, we can give the examples to a neural network and let it learn the relationships.

... [O tutorial continua no link abaixo] ...