Deep Learning Specialization

Deep learning is the engine behind modern AI: the image recognizers, the voice assistants, the chatbots, and the large language models. This specialization takes you from a single artificial neuron all the way to the transformer architecture behind today's most powerful systems, one clear idea at a time.

You will not just read about these ideas. You will play with them: fire a neuron by tuning its weights, stack layers into a working network, slide a filter across an image to see a machine detect an edge, and watch a word decide which other words to pay attention to.

What you will learn

• Neural networks: what a neuron computes, how layers stack into deep networks, and how they learn from data through gradient descent and backpropagation.
• Making training work: overfitting, regularization, optimization, and the practical craft of getting a network to generalize.
• Computer vision: how convolution lets a machine see, how convolutional neural networks build features from edges to objects, and how transfer learning reuses that knowledge.
• Natural language processing: how words become vectors, why sequence matters, and how self-attention and the transformer power modern language models.

Who this is for

Anyone who wants to truly understand how deep learning works, not just use it. Comfort with basic algebra and a little curiosity about how the math turns into intelligence is all you need. No prior machine learning is assumed: the course builds every idea from the ground up.