⚡ CrackLab
beginner~6h

Introduction to Neural Networks

Learn neurons, connection weights, biases, layers, activation functions (ReLU), and how signals propagate forward.

4
Subtopics
1
Exercises
1
Projects
1
Quiz Qs
1
Flashcards
▶📚 Prerequisites(1)

🎓 Learning objectives

  • •Grasp how a artificial neuron multiplies inputs by weights and adds a bias
  • •Explain activation functions like ReLU and Sigmoid
  • •Track feed-forward signal flow through multiple hidden layers

Introduction to Neural Networks is a Pro topic

Sign in, then upgrade to Pro or Power to unlock this topic and the full AI Engineering curriculum.

📂 Subtopics

1

From a Single Neuron to a Multi-Layer Perceptron

2

The Forward Pass: How Input Flows Through Layers via Matrix Multiplication

3

Loss Functions: MSE, Cross-Entropy, and Why We Minimize Loss

4

Backpropagation: The Chain Rule and How Gradients Flow Backward

Related concepts

python-basicsprobability-basics

Next to learn

deep-learning →llm-foundations →
HomeGo to Dashboard