MICRO-COURSE

Deep Learning

Deep Learning: 14 short lessons, one for each concept. Neural networks, tensors, activation functions, loss functions, backpropagation, gradient descent, optimizers, initialization, normalization, convolutional networks, recurrent networks, attention, transformers, and transfer learning.

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02 / UNDERSTAND

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Neural networks, tensors, activation functions, loss functions, backpropagation, gradient descent, optimizers, initialization, normalization, convolutional networks, recurrent networks, attention, transformers, and transfer learning.

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Concept scope

03 / TRY IT

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Choose one question about Deep Learning that you want to answer.
Write down a small example, its inputs or starting point, and the result you expect.
Explain your reasoning and note what you would like reviewed in a live or 1:1 session.

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A short example, reasoning and an open question

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Explain the concept in your own words and identify where it fits in the subject.
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Record one limitation or edge case and a question to discuss with a mentor.

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CONTINUE WITH ONE CONCEPT

Explore 14 focused topics.

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14 results1–12

Lesson

Neural networks

A short lesson on neural networks from the Deep Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

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Lesson

Tensors

A short lesson on tensors from the Deep Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Activation functions

A short lesson on activation functions from the Deep Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

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Lesson

Loss functions

A short lesson on loss functions from the Deep Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

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Lesson

Backpropagation

A short lesson on backpropagation from the Deep Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

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Lesson

Gradient descent

A short lesson on gradient descent from the Deep Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

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Lesson

Optimizers

A short lesson on optimizers from the Deep Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

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Lesson

Initialization

A short lesson on initialization from the Deep Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

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Lesson

Normalization

A short lesson on normalization from the Deep Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

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Lesson

Convolutional networks

A short lesson on convolutional networks from the Deep Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Recurrent networks

A short lesson on recurrent networks from the Deep Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

Open lesson

Lesson

Attention

A short lesson on attention from the Deep Learning micro course: what it is, where it shows up in real systems, and a practical example you can apply.

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Deep Learning

Recorded video

Start with the topic guide.

Concept scope

Prepare a focused example

Check your understanding

Need a walkthrough?Stay with this topic.

Request a live session for this topic.

Explore 14 focused topics.

Neural networks

Tensors

Activation functions

Loss functions

Backpropagation

Gradient descent

Optimizers

Initialization

Normalization

Convolutional networks

Recurrent networks

Attention

Need a walkthrough?
Stay with this topic.