Neural Networks Simplified: The Future Beyond Traditional ML

In recent years, we've been hearing a lot about deep learning and machine learning. Like many others, I was once confused, thinking deep learning was something entirely different. But today, in this article, I’ll explain both concepts to help you understand the bigger picture. We’ll also include a practical example of classification using a simple neural network.

Traditional Machine Learning (ML) and It’s Limitations

We have been using traditional machine learning for years, primarily with structured data. In traditional ML, we ** manually extract features** from the data, clean it, and use these processed features to train the model. Based on this training, the machine can later make predictions.

However, traditional ML faces significant limitations , particularly when dealing with complex and high-dimensional data:

• Manual Feature Extraction: In traditional ML, feature extraction is a manual process. It requires a deep understanding of the data, and if we miss important features, the model's accuracy suffers.

• High-Dimensional Data: Traditional ML struggles with high-dimensional data (like images, text, or videos). As the number of features grows, the complexity increases, making it harder for the model to identify meaningful patterns.

• Performance Plateau: Traditional ML models often reach a performance ceiling—once we hit a certain point, adding more data or tweaking the model further doesn't improve results significantly.

The Rise of Deep Learning: What’s Different?

Deep learning emerged as a solution to handle unstructured data—where traditional ML methods fail to identify patterns. The key difference is that deep learning models (neural networks) can automatically perform feature extraction without manual intervention.

In traditional ML, humans manually define features, and the model works with structured data. While In deep learning, the model itself learns to extract features through successive layers of neurons, making it suitable for high-dimensional and unstructured data like images, text, and speech.

Why Deep Learning?

Deep learning has become more popular due to several factors:

1. Availability of large datasets: In the last decade, we’ve generated massive amounts of data.
2. Increased computational power: Advanced hardware, like GPUs, allows faster and more efficient processing.
3. Efficient algorithms: We now have more advanced algorithms that optimize neural network training.
4. Handling Unstructured Data: Deep learning can process raw data like text, images, and speech directly, without requiring manual feature extraction.

Deep learning, especially neural networks, is behind the magic of Generative AI models like ChatGPT and Gemini. These models have billions of parameters, making them capable of solving highly complex tasks.

What Is a Neural Network?

At its core, a neural network is a set of interconnected neurons, similar to how neurons in the human brain work. These artificial neurons are mathematical functions that receive input, process it, and produce an output.

The most common activation function is ReLU (Rectified Linear Unit), which returns the maximum of two numbers, often used for non-linear transformations.

A simple neural network contains:

• Input Layer: Where the data is fed into the network.
• Hidden Layers: Where the data is processed. In deep learning, we increase the depth by adding more hidden layers.
• Output Layer: Produces the final prediction.

These layers are fully connected, meaning each neuron in one layer connects to every neuron in the next.

Why Are Neural Networks Powerful?

Neural networks are non-linear models, meaning they can handle very complex and uncertain problems, which traditional ML algorithms can struggle with.

Some of the real-world applications are

1. Healthcare: Detecting diseases in medical images (e.g., X-rays).
2. Self-driving Cars: Helping cars understand their surroundings.
3. Voice Assistants: Recognizing and responding to our voice commands (e.g., Siri, Alexa).

Types of Neural Networks

There are several types of neural networks, each suited for different tasks:

• Feedforward Neural Networks (FNN): The simplest form, where data moves in one direction.
• Recurrent Neural Networks (RNN): Best for sequential or time-series data, where the network remembers previous inputs.
• Convolutional Neural Networks (CNN): Mainly used for image and video processing tasks.
• Autoencoders: Essential for understanding models like BERT and GPT. These help with dimensionality reduction and feature learning.
• Attention Mechanism/Transformers: These are crucial for Natural Language Processing (NLP) tasks and are the backbone of models like GPT and BERT.

Key Deep Learning Concepts

• More Data, Better Learning: Deep learning improves with larger datasets. The more data a neural network is trained on, the better it performs.

• Handling Unstructured Data: Deep learning can process raw, unstructured data, making it ideal for fields like NLP, computer vision, and speech recognition.

• No Manual Feature Extraction: Unlike traditional ML, deep learning models automatically learn and extract features from raw data.

Frameworks for Implementation

The two most popular frameworks for implementing deep learning models are:

1. TensorFlow
2. PyTorch

These frameworks make it easy to build, train, and deploy neural networks for various applications.

Practical Example: HandWritten MNIST with Fully Connected Neural Network (FCN)

Let’s take a look at a practical example. Using the Handwritten Digits MNIST dataset, a Fully Connected Neural Network (FCN) can be trained to classify types of digits from 0-9. This example demonstrates how deep learning models can efficiently classify data and perform tasks like traditional ML but with more depth and complexity. To study More about this view the research paper Review on Deep Learning Handwritten Digit Recognition using Convolutional Neural Network.

Here is a practical example of classification using a Fully connected Neural Network based on MNIST Handwritten Dataset.

We will cover concepts

1. Load the MNIST dataset.
2. Preprocess the data for the model.
3. Build a simple fully connected neural network.
4. Train and evaluate the model.

Google Colab Notebook Practical Example

Conclusion

In conclusion, deep learning has allowed us to break past the limitations of traditional ML, especially in dealing with unstructured data. Neural networks and their ability to automatically learn features make them extremely powerful, capable of driving today’s advancements in AI and Generative AI models. Understanding the basic concepts of neural networks and how they work is the first step toward mastering deep learning!