In our previous tutorials, we explored different machine-learning tools to solve problems like predicting house prices or classifying emails. But what if we want computers to understand the world more humanly, like recognizing different cat breeds in a photo or translating languages fluently? That’s where Deep Learning comes in! Deep learning is an advanced and dynamic subset of machine learning that has revolutionized the field of artificial intelligence. While machine learning provides valuable insights and predictions based on data, deep learning takes a giant leap forward by leveraging complex neural networks to automatically learn and make intelligent decisions.
It allows computers to learn directly from data, becoming better at complex tasks than traditional methods. Let’s dive in and discover the exciting world of Deep Learning!
Table of Contents
Prerequisites:
- Python, Numpy, Sklearn, Pandas and Matplotlib.
- Familiarity with TensorFlow and Keras
- Linear Algebra For Machine Learning.
- Statistics And Probability Theory.
- All of our previous machine-learning tutorials
Introduction To Deep Learning: What Is Deep Learning?
Imagine yourself teaching a child the intricacies of the world. You start with fundamental shapes, colours, and textures, slowly building their understanding layer by layer. But as their curiosity grows, so too does the complexity of what they can grasp. Traditional machine learning algorithms, like decision trees and support vector machines, are excellent at handling these well-defined structures. However, they often struggle with more complex tasks or perceptual tasks in domains like computer vision, natural language processing, speech recognition etc.
This is where deep learning enters the scene, a powerful tool inspired by the very structure and function of the human brain. It is just an inspiration and not the exact representation of our neurons. At its core, deep learning relies on artificial neural networks (ANNs), interconnected layers of processing units that learn and improve through experience. These networks, like their biological counterparts, can automatically extract intricate features and relationships from vast amounts of data, enabling them to tackle challenges that were previously insurmountable.
In the 1940s, the foundational concept of artificial neurons was introduced, laying the groundwork for what we now know as deep learning. However, progress was slow due to limitations in both computing power and theoretical understanding during that time.
Fast forward to the 1980s, interest was resurgent with the development of a crucial training algorithm called backpropagation. This algorithm played a key role in optimizing neural networks, but deep learning still faced challenges.
It wasn’t until the 2010s that deep learning truly flourished. This period saw significant advancements in computing power, the explosion of Big Data, and the emergence of improved algorithms. These factors collectively fueled the growth of deep learning, leading to groundbreaking breakthroughs in various fields. The combination of more powerful hardware, vast amounts of data, and enhanced algorithms enabled deep learning to reach new heights, transforming the way we approach and solve complex problems across different domains.
Let’s look at how other people define it so that we can get a better understanding of what exactly it is:
- Andrew Ng: “Deep learning is a part of machine learning that uses artificial neural networks, learning algorithms inspired by the human brain, to automatically learn and extract features from data.”
- François Chollet: “Deep learning is a set of algorithms that attempt to learn in a way that is similar to how humans learn.”
- Yann LeCun: “Deep learning is like building a child a Lego set with no instructions, and the child ends up building a spaceship.”
- Jeff Hawkins: “Deep learning is about giving computers the ability to learn the way we do, by building models of the world based on the data we experience.”
- Google AI: “Deep learning is a powerful tool for identifying and understanding complex patterns in data, enabling computers to make accurate predictions and decisions on a wide range of tasks.”
- OpenAI: “Deep learning allows computers to learn from large amounts of data and improve their performance over time without being explicitly programmed.”
Deep learning is a specific subset of machine learning that approaches representation learning differently. It places a strong emphasis on learning successive layers of representations from data. The term “deep” in deep learning doesn’t signify a deeper understanding achieved by the approach; rather, it refers to the idea of learning these layers of representations one after another.
The depth of the model is determined by how many layers contribute to its representation of the data. Alternative names for the field could have been “layered representations learning” or “hierarchical representations learning.” In modern deep learning, models often involve tens or even hundreds of successive layers of representations, all learned automatically from exposure to training data. In contrast, other machine learning approaches tend to focus on learning only one or two layers of representations, earning them the label “shallow learning.”
Deep Learning with Python, Book
Deep Learning vs. Linear Models:
Remember our tutorials on linear models like linear regression and classifiers? They were great for tackling simple problems, but what happens when we encounter more complex functions? For example, in a lock with a non-linear keyhole, linear models simply don’t fit. There are three key differences that make deep learning a paradigm shift from the limitations of linear models. These are:
1. Representation Power:
- Linear models: Think of them like straight lines, unable to capture the curves and bends of real-world data. This limits their ability to represent complex functions like XOR or even a simple parabola.
- Deep learning: Imagine a network of interconnected nodes, each performing simple calculations. By stacking these layers upon layers, deep learning models can build intricate representations, mimicking even the most complex functions. It’s like having a toolbox with tools for every shape, not just straight lines!
2. Feature Engineering:
- Linear models: Finding the right features for complex problems can be like searching for a needle in a haystack, requiring expert knowledge and intuition. It’s time-consuming and often relies on trial and error.
- Deep learning: Deep learning models automatically learn these features directly from the data! No need for manual engineering, the model itself discovers the hidden patterns and relationships within the data, building its own “toolbox” as it learns.
3. Flexibility and Scalability:
- Linear models: With limited representation power, they struggle with large or diverse datasets. Adding more features can become cumbersome and computationally expensive.
- Deep learning: The more layers a deep learning model has, the more complex functions it can represent. This allows it to handle large datasets and diverse problems efficiently, scaling its power as needed. It’s like having a model that can grow and adapt to your challenges.
Feature | Classical Machine Learning | Deep Learning |
---|---|---|
Feature Representation | Manual | Automatic |
Model Complexity | Shallow | Deep |
Learning Process | Explicit | Implicit |
Strengths | Interpretable, Efficient for small datasets, Easily explainable decisions | Can handle complex patterns and relationships, Excels with large datasets, Automatic feature learning |
Weaknesses | May struggle with complex patterns, Limited scalability, Requires significant feature engineering effort | Can be computationally expensive, Less interpretable (“black box”), Prone to overfitting with small datasets |
So, How Does Deep Learning Models Solve A Problem?
Deep learning is like learning about something step by step. Imagine you want a computer to recognize digits. A deep learning model does this by breaking down the task into different steps. Don’t worry about the technical stuff; just think of it like layers in the model learning various things from the digit picture. They work one after the other, figuring out different aspects, and in the end, they give you an answer about the digit it recognizes. It’s a way to learn data representation in multiple layers.
Another example to understand how deep learning models solve a problem: Imagine you want to train a model to recognize different dog breeds in photos. A deep learning model wouldn’t need you to manually define features like “floppy ears” or “short legs.” Instead, it would analyze millions of dog pictures, automatically identifying and combining various features (e.g., ear shape, fur texture, body proportions) to create its own internal representation of different breeds. This allows the model to recognize breeds it might never have seen before, showcasing its pattern recognition prowess.
Deep learning models aren’t just about identifying features; they can also learn intricate relationships between them. Imagine you want to predict housing prices. A simple model might only consider factors like square footage and number of bedrooms. But a deep learning model could go further, analyzing factors like neighbourhood demographics, proximity to amenities, and even historical market trends. By considering these complex relationships, the model can make more accurate and nuanced predictions.
One more important thing about deep learning is models are trained on large and diverse datasets, allowing them to generalize well to new data. This means they can be trained on a specific task (e.g., recognizing dogs) and then apply their learned knowledge to solve similar tasks (e.g., recognizing cats, or identifying other animals) with minimal adjustments. This adaptability makes them valuable for real-world applications where data may vary significantly.
Limitations:
Until now, we haven’t delved into the math behind how deep learning models work. We’ll get there soon, but for now, you’ve got a grasp of how deep learning sets itself apart from traditional machine learning methods and where it excels. Now, let’s shift gears and discuss where it falls short:
- Black box: Sometimes, even the experts can’t explain how a deep learning model arrived at its decision. This lack of transparency can be a concern in areas like healthcare or finance.
- Data dependence: Like a picky eater, deep learning models need a lot of data to perform well. If you don’t have enough data, or if it’s biased, your model might end up making some seriously flawed decisions.
- Computational cost: Training deep learning models can be expensive and time-consuming, especially if you’re using a potato for a computer (don’t do that).
- Ethical Concerns: Deep learning applications raise ethical concerns related to biases in data, transparency, and fairness in decision-making processes.
Artificial Neural Networks (ANNs)
The core of deep learning is ANNs, they are inspired by biological neural connections but they are not exactly the same. All deep learning networks that we will study are some form of ANNs with different architectures. Different architectures are useful in different tasks. We will study each network type in different tutorials but first, let’s have an overview of what all types of ANNs exist. Don’t worry about the terms. We will learn each concept one by one.
1. Feedforward Neural Networks (FNNs):
- How they work: FNNs consist of multiple interconnected layers of neurons (nodes) arranged successively. Information flows from the input layer through hidden layers (if any) to the output layer.
- Strengths: Simple, versatile, and efficient for various tasks. Able to learn complex relationships between input and output data.
- Other names: Multi-Layer Perceptrons (MLPs)
- Applications: Image recognition, spam filtering, classification and regression tasks.
We will study FNN or MLPs in this tutorial and other architectures in future tutorials.
2. Convolutional Neural Networks (CNNs):
- How they work: CNNs utilize a specialized architecture with convolutional layers that extract features from input data (typically images or videos) through convolutions and pooling operations. These features are then processed by fully connected layers for classification or regression tasks.
- Strengths: Highly effective for image and video processing tasks due to their ability to learn spatial features and hierarchical representations.
- Other names: ConvNets
- Applications: Self-driving cars, medical image analysis, image captioning, object detection, facial recognition.
3. Recurrent Neural Networks (RNNs):
- How they work: RNNs are designed to handle sequential data like text and speech. They incorporate an internal memory mechanism that allows them to process information one step at a time, taking into account the context from previous inputs.
- Strengths: Able to learn long-term dependencies within sequential data, making them suitable for tasks like machine translation and language modelling.
- Other names: Simple RNNs
- Applications: Machine translation, sentiment analysis, text generation, music generation.
4. Long Short-Term Memory (LSTMs):
- How they work: LSTMs are a variant of RNNs specifically designed to address the vanishing gradient problem in RNNs. They utilize special memory cells that can store information for longer periods, allowing them to learn long-term dependencies more effectively.
- Strengths: Improved memory capabilities compared to vanilla RNNs, making them ideal for tasks with long-term dependencies like natural language processing and video captioning.
- Other names: Gated Recurrent Units (GRUs) are similar with slight variations.
- Applications: Machine translation, natural language processing, video captioning, speech recognition.
5. Gated Recurrent Units (GRUs):
- How they work: GRUs are a variant of RNNs similar to LSTMs, but with a simpler architecture. They utilize update gates and reset gates to control the flow of information within the memory cell, allowing them to learn long-term dependencies.
- Strengths: Offer similar capabilities to LSTMs in handling long-term dependencies, but with fewer parameters and potentially faster training times.
- Applications: Similar to LSTMs, including machine translation, natural language processing, speech recognition, and text generation.
6. Transformers:
- How they work: Transformers utilize an attention mechanism that allows them to focus on specific parts of the input data, leading to more accurate and nuanced results. This attention mechanism can be applied to various tasks, including machine translation and text summarization.
- Strengths: Revolutionizing tasks like machine translation and summarization with their powerful attention mechanism. Can handle long-range dependencies effectively.
- Other names: Attention-based models
- Applications: Machine translation, text summarization, question answering, speech recognition.
7. Generative Adversarial Networks (GANs):
- How they work: GANs consist of two competing networks: a generator and a discriminator. The generator creates new data (e.g., images, text), while the discriminator tries to distinguish the generated data from real data. This adversarial training process allows the generator to create increasingly realistic and complex outputs.
- Strengths: Able to generate realistic and creative data, making them suitable for tasks like image generation, music composition, and text style transfer.
- Other names: None widely used, but often described by their specific application (e.g., StyleGAN for image generation).
- Applications: Creating realistic images and videos, composing music, generating art styles, drug discovery.
There can be other network types but these are a few of them that are mostly used. Also, each of these architectures is used depending on the kind of problem you are working on. Now that we have a basic understanding of deep learning let’s learn each of the architecture one by one in depth. In this tutorial, I will only explain FNN and its implementation, the rest in future tutorials.
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