Every time a phone unlocks with a glance or a voice assistant understands a spoken request, a neural network is quietly doing the work behind the scenes. These systems now learn with little human guidance, adapt to new data, and handle problems once thought impossible for a machine.

This course is a hands-on entry point into that world. Rather than treating deep learning as an abstract topic, it walks participants from the underlying mathematics through to building, training and troubleshooting real networks, preparing them to apply machine learning directly in their own technical work.

Knowledge and Benefits:

By the end of this course, participants will be able to:

• Explain what a neural network is and why deep learning has taken off.
• Implement logistic regression, gradient descent and vectorized computations using Python/Numpy.
• Design shallow neural networks, choosing activation functions and applying backpropagation.
• Build deep, multi-layer networks and reason about parameters versus hyperparameters.
• Apply these foundations toward CNNs, RNNs and transformer-based NLP tasks.

Who Should Attend?

• Software engineers.
• Members of technical teams in modern organisations.
• Anyone with a strong interest in artificial intelligence and its applications.
• Anyone who wants to develop their skills and experience in this field.

Module 1: Why Deep Learning Matters Now

• Introduction to deep learning and what a neural network is.
• Supervised learning with neural networks.
• Why deep learning is taking off.

Module 2: The Math Engine Behind Neural Networks

• Binary classification and logistic regression.
• The logistic regression cost function and gradient descent.
• Derivatives, more derivative examples, and computation graphs.
• Logistic regression gradient descent, including gradient descent on m examples.

Module 3: From Code to Computation: Vectorized Implementation

• Vectorization and more vectorization examples.
• Vectorizing logistic regression and its gradient output.
• Broadcasting in Python, and a note on Python/Numpy vectors.
• A quick tour of Jupyter/iPython notebooks.

Module 4: Designing Shallow Networks

• Neural network overview and representation.
• Computing a neural network's output, and vectorizing across multiple examples.
• Activation functions and why non-linear ones are needed.
• Gradient descent for neural networks, backpropagation intuition, and random initialization.

Module 5: Going Deep: Multi-Layer Architectures

• The deep L-layer neural network and forward propagation in a deep network.
• Getting your matrix dimensions right, and why deep representations work.
• Building blocks of deep neural networks: forward and backward propagation.
• Parameters versus hyperparameters.