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Advanced 12 planned topics AI

Introduction to Deep Learning

Learn neural network training, layers, and loss functions. Free deep learning introduction.

Learning Outcomes

  • Describe a forward pass and a loss
  • Explain what a layer and an activation do
  • See why more data and compute matter
  • Name common frameworks (PyTorch, TensorFlow)

Overview

Deep learning stacks layers of neurons and learns weights with gradient descent. It powers vision, speech, and large language models.

This page introduces tensors, loss, and why GPUs help.

Linear algebra comfort helps but we start from intuition.

What this course will cover

  1. 1. Tensors
  2. 2. Layers
  3. 3. Loss
  4. 4. Backprop idea
  5. 5. Overfitting
  6. 6. Frameworks

Start a complete tutorial now

Full 6-level lessons are live for C, C++, Java, Python, and JavaScript. Use them while the remaining Deep Learning lessons are added.

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