Deep Neural Networks
Deep Learning is the driving force behind today’s most advanced AI breakthroughs — from voice assistants to self-driving cars. In this module, you’ll uncover what deep learning really is, why it has transformed the AI landscape, and how Deep Neural Networks mimic the way the human brain processes information.
Through real-world examples and accessible explanations, you’ll move past the buzzwords to gain a clear, practical understanding of how deep learning works and why it matters in business, science, and everyday life.
You’ll Learn in Deep Neural Networks
- The Evolution from Machine Learning to Deep Learning
- How deep learning builds on traditional machine learning concepts through Deep Neural Networks.
- Why the explosion of big data and computing power enabled its rise.
- Neural Networks Explained Simply
- What neurons, layers, weights, and activation functions are — without heavy math.
- How information flows and transforms through a Deep Neural Network to make decisions.
- Key Architectures of Neural Networks
- Convolutional Neural Networks (CNNs) for image and video recognition.
- Recurrent Neural Networks (RNNs) and LSTMs for language, speech, and time-series data.
- Transformers for modern language models like ChatGPT — all examples of Deep Neural Networks.
- Training a Neural Network
- Understanding forward and backward propagation in Deep Neural Networks.
- The role of loss functions and optimization algorithms.
- Why Deep Learning Works So Well
- How multi-layered structures of Deep Neural Networks capture complex patterns in data.
- The difference between feature engineering in ML and representation learning in DL.
Key Topics Covered
The relationship between AI, machine learning, and deep learning with a focus on Deep Neural Networks
• Biological inspiration vs. artificial implementation
• How neural networks handle complex unstructured data
• Overfitting, underfitting, and how to improve model performance
• Breakthrough applications: image classification, natural language processing, and generative AI
Why This Deep Neural Networks Matters
Deep learning has become the foundation of modern AI innovation. Understanding Deep Neural Networks isn’t just for data scientists — it’s essential for business leaders, policy makers, and innovators who want to make informed decisions about AI adoption.
By demystifying deep learning and Deep Neural Networks, this module equips you to recognise what’s possible, what’s hype, and what’s next.
Practical Outcome
By the end of Module 3, you will:
• Explain how Deep Neural Networks work in clear, non-technical language.
• Identify where deep learning is most applicable and where it may fall short.
• Understand the major architectures powering today’s AI tools.
• Be able to discuss deep learning opportunities and challenges with both technical and non-technical teams.