What is the difference between a shallow and deep machine learning model?

The primary difference between a shallow and a deep network is the difference in the number of hidden layers. Mostly deep networks are used to learn more information about the provided data and gets generalized while shallow networks are simple models which capture primary features and patterns.

As per my understanding, shallow models (SM, linear/logistic regression, decision trees, & k-nearest neighbors) are often used for relatively simple & well-structured tasks where data does not possess intricate patterns or dependencies. I see the primary use of SM is in assignment which involve straightforward relationships between input features & target variables. SM are ideal for tasks such as binary classification, basic regression, & simple decision-making problems. SM are computationally efficient, quick to train, & require fewer data to make predictions effectively. However, I must mention that it may not perform well on complex tasks which demand a more nuanced understanding of data patterns, where DL models tend to excel.

Shallow ML models, like linear regression, decision trees, and support vector machines, have limited layers and work well for simpler, structured data problems. They are easier to interpret and require less computational power, but they struggle with complex tasks. In contrast, deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), contain multiple layers that capture intricate patterns and relationships in data. These models excel in handling large, unstructured datasets like images and text but require substantial computational resources and large amounts of data for training. The key difference lies in the depth of layers and the complexity of patterns each model can learn and represent.

Shallow models refer to machine learning models that have a limited number of layers or minimal complexity. These models are typically simpler and have fewer parameters compared to deep learning models. Shallow models are often used for tasks where the data has a relatively straightforward structure or when computational resources are limited.

In my experience any model can be represented as a graph, and the depth of this graph is the difference between shallow or deep models For example in a neural network we have multiple neurons and they are connected to each other. Consider the neurons are connected to each other through edges which we mean weights in Deep learning. Now if we consider input to the model as root node of the graph, we can literally say if the longest connected path in the graph exceeds length x, it is deep model else it’s a shallow model. advantage of more connected nodes is that it increases your models ability to learn more. Thus if a task requirement is complex and requires more parameters to be learnt, choose deeper models

Simple/shallow models often require more hand-crafted features which may not be always feasible. Use-cases involving images or natural language need deep networks as universal features are not easy to generate. As we move towards AGI (Artificial General Intelligence), we have to deal with multi-modal data where we have to rely on deep model architectures. Also, TinyML frameworks like TensorflowLite allows to deploy deep models to resource constraint environments, like a mobile phone.

Typically, steps for building a model are: Input Data → Encoder (if necessary) → Numerical Representation → Model → Decoder (if necessary) → Output We may have to engineer features apart from preprocessing data (imputation, encoding etc.). Hence, these are human-made features inputted into a shallow model. However, for highly complex and unstructured data like images, audio, text etc., it’s impossible for a human to extract the right features through which a model can be learned. What if the machine not only learns patterns from features (model training) but also generates features itself? An E2E pipeline where the model does everything! Isn't it wonderful? This is achieved by learning much more deeply about data, hence Deep Learning.

Deep models applied in a wide range of use cases, including image & speech recognition, NLP, and various types of pattern recognition. Deep Model's depth allows them to automatically extract & transform features from raw data, making them suitable for tasks that involve understanding high-level abstractions and subtle relationships within the data. However, training deep models typically requires a substantial amount of data and computational resources, and they may be prone to overfitting if not properly regularized or if data is limited.

CNNs, rooted in their methodical design, are a testament to the depth and precision deep models can achieve in the evolving landscape of deep learning. While pooling previously stood as the default for spatial reduction, the emergence of 2-stride kernels has provided a nuanced alternative for efficient feature retention. Advancements, notably the integration of Inception and Reduction blocks, have been pivotal in refining feature abstraction processes. As models grow in complexity, encapsulating a spectrum of depth, the challenge amplifies: judiciously aligning the intricate configurations of these models with the specific demands of their computational tasks.

When it comes to modeling, you've always got to consider the complexity factor, and that means you're essentially playing a balancing act between bias and variance. Simple models tend to have a high bias, and they'll underfit your data by not really capturing its nuances. On the flip side, more complex models lean towards high variance and end up overfitting, latching onto noise in the dataset. So, the real challenge is finding that sweet spot of model complexity that suits your specific dataset, something that you and I both know can make or break your machine learning efforts by ensuring your model can actually make sense of new, unseen data.

When a model is over-fitting, it usually means that the model has already learnt the data characteristics and is now trying to learn patterns in the noise of the data to improve performance on a metric like accuracy. Since, patterns learnt from noise will not generalize to unseen data, the model under-performs. Solution: 1. Use a simpler model 2. Apply regularization 3. Use more data for training. In under-fitting, the model is not even able to learn the training data. Solutions: 1. Use a more complex model 2. Check if more feature can be added from the input data. 3. Reduce regularization of model. 4. Check if all features have uniform scale. If data is not normalized, then model may ignore some features.

It's pretty clear that deep models usually come with longer inference times, creating a trade-off between model efficiency and accuracy. That's why, as a general rule of thumb, we kick things off with a simpler, base model. Over time, we gradually ramp up the complexity while keeping a close eye on the accuracy improvements. The moment we notice that those accuracy gains start to level off and become marginal, that's usually our cue to stop tinkering and stick with the model we have. After all, it's all about finding that sweet spot between performance and efficiency.

Given a problem, it's always better to start with simpler/shallow models and create a baseline for model performance. If the metric in focus is accuracy, check the accuracy obtained by the shallow model. Let's say it is 60%. Then as we evaluate deeper/complex model, the gain in accuracy should justify the overhead of the model. Deeper models require more compute, data and are harder to interpret. How much gain is sufficient depends on the use-case. For trading scenarios latency may be more critical. Models deployed for industrial assets may require compute optimized models because of limited resources. These non-functional requirements are equally important when deciding between shallow vs deep model.