Introduction to Deep Learning

In the previous lesson, we learned the basics of neural networks. We understood how neurons, layers, and activation functions work together.

In this lesson, we take the next step and introduce Deep Learning.

Deep learning is simply neural networks with many hidden layers, designed to learn very complex patterns from data.

---

What Is Deep Learning?

Deep learning is a subset of machine learning that uses deep neural networks.

The word “deep” refers to the number of hidden layers between the input and output layers.

While traditional neural networks may have one or two hidden layers, deep learning models can have dozens or even hundreds of layers.

This depth allows the model to learn patterns at different levels of abstraction.

---

Why Depth Matters

Each layer in a deep neural network learns a different representation of the data.

Early layers learn simple patterns. Later layers combine those patterns to learn more complex relationships.

This layered learning is what makes deep learning extremely powerful for tasks like image and speech recognition.

---

Deep Learning vs Traditional ML

Traditional machine learning often requires manual feature engineering.

Deep learning reduces this need by automatically learning features from raw data.

However, this power comes at a cost: deep learning models require large datasets and more computational resources.

---

Relating Deep Learning to Our Dataset

Using the Dataplexa ML dataset, a deep learning model could learn complex interactions between income, credit score, employment history, and loan approval outcomes.

While simpler models work well for this dataset, deep learning becomes valuable when datasets grow larger and more complex.

---

Common Deep Learning Applications

Deep learning is used in many real-world systems.

Image recognition systems use deep learning to identify objects and faces.

Speech recognition systems convert spoken words into text.

Recommendation engines use deep models to understand user preferences.

Self-driving cars rely heavily on deep learning for perception and decision-making.

---

Challenges of Deep Learning

Despite its power, deep learning is not always the best solution.

Deep models are harder to interpret, require more data, and take longer to train.

They can also overfit easily if not properly regularized.

This leads us to the next important topic.

---

Mini Practice

Think about which problems require deep learning and which can be solved with simpler models.

This judgment is a key skill for any machine learning engineer.

---

Exercises

---

Quick Quiz

---

In the next lesson, we address one of the biggest challenges in deep learning: Regularization Techniques.