Neural Networks Refresher
Coming Soon
This lesson is currently under development. Check back soon for comprehensive content covering:
- Neural Network Basics: Neurons, layers, activation functions, forward propagation
- Training Process: Backpropagation, gradient descent, loss functions, optimizers (SGD, Adam)
- CNN Architectures: Convolutional layers, pooling, ResNet, EfficientNet for robot vision
- Sequential Models: RNNs, LSTMs, GRUs for temporal data, trajectory prediction
- Transformers: Attention mechanisms, ViT (Vision Transformers), applications in robotics
Expected Completion: This lesson will be available soon.
Learning Objectives
By the end of this lesson, you will be able to:
- Explain neural network components and the training process
- Select appropriate architectures for robotics perception tasks
- Implement neural networks using PyTorch/TensorFlow
- Understand modern architectures (CNNs, Transformers) used in Physical AI
Further Reading
- Deep Learning by Ian Goodfellow et al. — Comprehensive reference
- PyTorch Tutorials — Hands-on deep learning
- CS231n: Convolutional Neural Networks — Stanford course on vision
What's Next?
Continue to Lesson 3: Computer Vision Fundamentals to learn how robots perceive the visual world.
This lesson is part of Chapter 2: AI Fundamentals Review