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Learn Deep Learning

Understand neural networks from a single neuron up to transformers.

Free to start. Built from a single prompt. No credit card.

Deep learning is machine learning with neural networks of many layers, and it drives modern image recognition, speech recognition and language models. Under the hood, every network does the same thing: it computes a prediction through layers of weighted sums and nonlinear activations, measures the error with a loss function, and adjusts its weights by backpropagation and gradient descent. Once that loop makes sense, architectures like convolutional networks and transformers are variations on a theme. Learnr builds a personalized deep learning course and teaches it in short interactive lessons that pair each idea with the code that implements it.

How to learn Deep Learning, step by step

Refresh the math that training depends on. Review vectors, matrix multiplication, derivatives and the chain rule, plus basic probability. You do not need a full course in each, but backpropagation is the chain rule applied repeatedly, so that part must be solid.
Build a tiny network from scratch. Write a two-layer network in plain NumPy, compute the forward pass, the loss and the gradients by hand, and train it on a small dataset like handwritten MNIST digits. Doing this once removes the mystery from every framework.
Learn a framework properly. Move to PyTorch and learn tensors, autograd, nn.Module, DataLoader and the standard training loop of forward pass, loss, backward pass and optimizer step. Check that a model can overfit a tiny batch before training on everything.
Master the habits that make training work. Experiment with learning rates, the Adam optimizer, batch normalization, dropout, weight decay and early stopping, and plot training against validation loss. Most failed runs come from a bad learning rate or a data bug, not the architecture.
Study CNNs, then attention and transformers. Learn convolutions, pooling and residual connections on image tasks, then tokens, embeddings, self-attention and positional encodings. Reading a short annotated transformer implementation line by line is worth more than many summaries.
Fine-tune a pretrained model on your own data. Take a pretrained image or text model, replace the final layer and fine-tune it on a small dataset you collected. Keep the concepts fresh with short daily Learnr reviews while the longer experiments run.

What you'll learn

Explain how a neural network computes a prediction and learns from its error
Implement backpropagation and gradient descent for a small network
Train and debug models in PyTorch with sensible learning rates and regularization
Build convolutional networks for images and fine-tune pretrained models
Describe how attention and transformers power modern language models

What's inside your Deep Learning course

A sample path Learnr builds for Deep Learning. Your real course adapts to your goal and level.

1. From linear models to neurons and activations
2. Loss functions, gradients and backpropagation
3. Training well: optimizers, initialization and regularization
4. Convolutional networks for images
5. Sequence models, attention and transformers
6. Transfer learning and a full project

How long does it take to learn Deep Learning?

With Python and classical machine learning in place, the core of deep learning takes about 3 to 6 months at 6 to 10 hours a week. Without that background, expect closer to a year. A GPU, often available free in cloud notebooks, speeds up the hands-on part.

What you need before you start

Working Python, NumPy basics, linear algebra (vectors and matrices), derivatives and the chain rule, and the core ideas of machine learning such as training and test data and overfitting.

Common mistakes to avoid

Copying training code without understanding the loop, then being stuck when loss turns to NaN. Learn what each line of the training step does.
Training a huge model before checking it can overfit a single small batch, which is the fastest test that the pipeline is wired correctly.
Forgetting to switch between training and evaluation mode, so dropout and batch normalization behave wrongly when measuring validation accuracy.

How Learnr builds your course

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Move through bite-sized lessons and interactive practice, earning XP and keeping your streak.

Deep Learning questions, answered

Should I learn PyTorch or TensorFlow?

Either teaches the same concepts. PyTorch is widely used in research and many courses, while TensorFlow with Keras remains common in some production settings. Learn one well, and switching later mostly means learning new syntax.

Do I need a GPU to learn deep learning?

Not to start. Small networks train fine on a laptop CPU, and free cloud notebooks offer limited GPU time for larger experiments like fine-tuning image models.

How much math does deep learning require?

Linear algebra, derivatives with the chain rule, and basic probability cover most of what you need to understand training. Research-level work demands more, but practitioners can go far with those foundations.