AI Study Series Part 3: Deep Learning and Neural Networks

Deep Learning is the core engine of today’s most powerful AI applications—from ChatGPT and self-driving cars to facial recognition and automated translation. This part of the series will help you understand how neural networks work and how to train them effectively.

What is Deep Learning?

Deep Learning is a branch of machine learning that uses artificial neural networks with multiple layers ("deep" networks). These networks learn complex patterns in large datasets through backpropagation and gradient descent.

Core Topics in Deep Learning

• Neurons and Perceptrons
• Activation Functions (ReLU, Sigmoid, Softmax)
• Feedforward and Backpropagation
• Convolutional Neural Networks (CNNs)
• Recurrent Neural Networks (RNNs) & LSTMs
• Transformers and Attention Mechanisms

Learn Deep Learning with Trusted Sources

• Fast.ai – Deep Learning for Coders: Highly practical and intuitive course using PyTorch.
• DeepLearning.AI Specializations: Covers everything from basic neural networks to GANs and Transformers.
• CS50's AI with Python: Hands-on deep learning projects including Tic-Tac-Toe and image recognition.
• MIT OpenCourseWare: Deep learning lectures and full syllabus by world-class AI professors.
• Stanford CS221 – AI: Principles and Techniques

Recommended Tools & Libraries

• TensorFlow – Deep learning framework by Google
• PyTorch – Popular among researchers and developers
• RunwayML – Visual platform to deploy and use deep learning models
• Hugging Face Transformers – Home of pre-trained models like BERT and GPT

Bonus: Visual Deep Learning

Watch AI lectures and model walkthroughs on TED Talks, Vimeo, and Odysee for deeper insights into how these systems behave.

Next Up

In Part 4: Natural Language Processing, we’ll explore how machines understand human language, generate text, translate languages, and build chatbots.