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LESSON

AI 029. But why do we call it deep learning?

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ANSWER

The term “deep” in deep learning refers to the depth of the network, which is determined by the number of layers it contains. These layers are made up of nodes, or neurons, that process input data sequentially, transforming it step by step as it moves through the network. The “deep” aspect comes into play because, unlike traditional neural networks with only one or a few hidden layers, deep learning networks can have dozens, hundreds, or even thousands of layers.

The Significance of Depth:

Complex Feature Learning: In deep learning, each layer learns to transform its input data into a slightly more abstract and composite representation. Early layers might capture simple, low-level features like edges or colors in an image. As data progresses through the layers, it becomes capable of recognizing more complex features (e.g., shapes or objects). By the time data reaches the final layers, the network has learned to identify high-level features and patterns.

Hierarchical Learning: This depth allows the network to learn hierarchically. Lower layers learn basic features, and as you move deeper, the features extracted by the layers become increasingly complex and abstract. This hierarchical learning is a powerful concept that mimics the way human brains are thought to process information.

Increased Model Capacity: Having more layers increases the model’s capacity, enabling it to learn more complex patterns and relationships in the data. However, this also means that deep learning models require more data and computational power to train effectively.

Challenges with Depth:

While depth can enhance a model’s learning ability, it also introduces challenges, such as:

Vanishing Gradient Problem: In very deep networks, gradients used during training can become very small, effectively stopping the network from learning. Advanced techniques and architectures, like Residual Networks (ResNets), have been developed to address this issue.

Overfitting: Deeper models are at a higher risk of overfitting, where the model learns the training data too well, including its noise and outliers, which can degrade performance on new, unseen data.

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Quiz

What does the "deep" in deep learning refer to?
A) The extensive data required to train the models
C) The complexity of algorithms used in the model
B) The number of layers in a neural network
D) The depth of analysis performed on the data
The correct answer is B
The correct answer is B
Why is hierarchical learning significant in deep learning models?
A) It allows the model to process data faster.
C) It reduces the computational resources needed.
B) It enables the model to learn complex patterns by building up from simpler ones.
D) It simplifies the data preprocessing steps.
The correct answer is B
The correct answer is B
What challenge does increased depth in neural networks introduce?
A) Simplified model architectures
C) The vanishing gradient problem
B) Decreased need for data
D) Faster training times
The correct answer is B
The correct answer is C

Analogy

Imagine building a skyscraper. A traditional building (analogous to a shallow neural network) might have just a few floors, limiting its capacity and the complexity of activities it can host. In contrast, a skyscraper (a deep learning model) with many floors can host a wide variety of activities, from basic services on lower floors to more sophisticated, specialized activities on higher floors. Each floor builds on the functions of the ones below it, creating a complex, integrated structure capable of handling a broad range of tasks with increasing sophistication.

In summary, we call it “deep” learning because of the model’s depth, with many processing layers allowing for the learning of increasingly complex and abstract features, much like a skyscraper reaches towards the sky, offering more space and versatility the higher you go.

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Dilemmas

Resource Management in Training Deep Models: Given the substantial computational resources and data requirements for training deep neural networks, how should organizations balance the benefits of increased model capacity with the costs and environmental impacts of these resources?
Equity in Access to Deep Learning Technology: As the depth and complexity of models can lead to superior performance but require significant investment, how can we ensure that smaller entities or developing countries are not left behind in benefiting from deep learning advancements?
Ethical Considerations in Automated Decisions: With deep learning models capable of making complex decisions based on high-level features learned from data, what ethical frameworks should be in place to govern their use in critical areas such as surveillance, healthcare, and autonomous vehicles?

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