AI & Machine Learning
Generative AI & Large Language Models
How large language models really work — and how to build reliable products on top of them.
5 free lectures$69₹4,499
AI & Machine Learning
Deep Learning
Open the black box. Build intuition for how neurons combine into networks, watch backpropagation push gradients through a computation graph, race optimizers across a 3D loss landscape and train a real network live in your browser — then learn the regularization tricks that make deep models generalize.
What you’ll learn
- Explain how neurons, weights, biases and activations turn inputs into predictions
- Trace backpropagation through a network and reason about vanishing and exploding gradients
- Choose optimizers, learning rates and schedules with confidence
- Diagnose overfitting and apply dropout, weight decay, batch normalization and early stopping
- Read modern architectures — CNNs, RNNs and transformers — as combinations of the same building blocks
Curriculum
Module 1 · Neural network fundamentals
5 lectures- 01Neurons, layers and networks3D modelFree18 min
- 02Activation functions: why depth worksInteractiveFree17 min
- 03Backpropagation: how networks learnInteractiveFree20 min
- 04Optimizers: from SGD to Adam3D modelFree18 min
- 05Regularization and generalizationInteractiveFree21 min
Module 2 · Architectures
5 lectures- 06Convolutional neural networksIncluded with the course
- 07Recurrent networks and LSTMsIncluded with the course
- 08Attention and transformersIncluded with the course
- 09Autoencoders and embeddingsIncluded with the course
- 10Generative models: GANs and diffusionIncluded with the course
Module 3 · Deep learning in practice
4 lectures- 11Transfer learning and fine-tuningIncluded with the course
- 12Training at scale: GPUs and mixed precisionIncluded with the course
- 13Debugging training runsIncluded with the course
- 14Deploying deep-learning modelsIncluded with the course
Who this course is for
- Engineers and data scientists moving from classical ML to deep learning
- ML engineers who train and fine-tune models but want deeper intuition
- Technical leads evaluating deep-learning projects
Before you start
Machine Learning Foundations or equivalent: loss functions, gradient descent and basic Python.
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Training a team on this?
We can combine Deep Learning with other courses into a learning path built around your team’s roles and goals.