How can you decide when to use deep learning models instead of traditional Machine Learning models?

From my experience, the choice between deep learning and traditional ML heavily depends on the nature of the data. With limited data, hand-crafted features and domain expertise often lead to better results using traditional methods. Conversely, deep learning shines when abundant data is available, allowing for automatic feature extraction.

If you have a lot of data and your problem includes complex patterns or different types of data like images, audio, and text, then using deep learning might be a good idea. On the other hand, if you don't have much data or you need to work with structured data where creating features is really important, then traditional machine learning could be a better choice.

In addition to other answers, the value of interpretability and familiarity may be important depending on the domain. For example, clinicians often prefer classical ML models due to the interpretability of features - they can weigh the choice of features and set of features against their intuition and expertise, as well as clinical literature and the standard of care.

Deep Learning Models- Data Requirements: Large Datasets: Deep learning models, such as neural networks, require large volumes of data to train effectively. The reason is that these models have many parameters & layers, which need extensive data to learn complex patterns without overfitting .- Traditional ML Models- Data Requirements: Smaller Datasets: Traditional machine learning models generally perform well with smaller datasets. They are less complex and require fewer parameters to tune, which makes them suitable for scenarios with limited data. - Strategies for Small to Medium-Sized Datasets - Use Traditional ML Models: Start Simple: Begin with simpler models that require fewer parameters & less data to achieve good performance.

It all boils down to data. The basics have it covered and works in real world settings too. A dataset simple enough to be comprehended by a human, mostly tabular with a limited number of features and not so complex intricacies between them. A DL model will be an overkill for such a dataset. In contrast, a dataset, for tabular - imagine a dataset with, say 400 columns or unstructured data like image, text, speech, or any frequency. Such data contains many underlying patterns and insights and to uncover them, a traditional ML model will only be able to scratch the surface.

As I mentioned in the above point, based on the simplicity of the problem statement, data availability and complexity of the architecture, we should choose the model training approach or else the model training will be costlier.

The computational demands of deep learning require GPUs/TPUs for acceptable training times. With limited resources, traditional ML models that train quickly on CPUs may be preferable. Optimization and precision tuning can further improve deep learning efficiency.

Computational Resources and Time Deep Learning: Requires substantial computational resources (e.g., GPUs or TPUs) and longer training times. This is due to the complexity of the models and the large amount of data processed. Traditional ML: Typically more computationally efficient and faster to train, especially on smaller datasets or when using simpler models.

That is correct. GPUs and TPUs are designed to accelerate the matrix calculations that underlie neural network computations. They can dramatically reduce the training time of deep learning models, making them a crucial consideration for those dealing with resource constraints. Traditional models often have simpler architectures and require fewer computational resources, leading to quicker training times and lower infrastructure costs.

When approaching some problems you may find yourself in a situation where even a 16 GPUs is not enough to iterate fast enough. It is always a trade-off between model complexity and capability and speed of computations. If you need to train 100k models in an hour with a cap on the budget you need to look for ways how to get things done fast!

Model Complexity and Interpretability Deep Learning: Models can be very complex and often operate as “black boxes,” making them less interpretable. This complexity can be a disadvantage when understanding model decisions is crucial. Traditional ML: Models like decision trees, linear regression, and logistic regression are generally more interpretable and provide insights into feature importance and model decisions.

Working in fields where decision transparency was crucial, I've found traditional models to be highly beneficial. In applications like medical diagnostics or financial decision-making, being able to explain the model's reasoning is often more critical than achieving slightly better accuracy. Deep learning models, while powerful, can create obstacles when interpretability is required.

From where I stand, this is the most important aspect. ML models are more suitable to business logic, and are rapidly grasped by different professionals. ML pipelines are deployed faster, so in a world where answers need to be available for yesterday, they are kings. Furthermore, ML models can be explained more intuitively for investors and it is easier to have fast, insightful metrics. DL models fit better in research and development on new features. Then, for a long -term project, DL would be a good option. Furthermore, DL models will add more computational resources to be deployed. Finally, the key question to ask about a set of results is: which is the database that trains this model? That answer may make the best option to take.

This is accurate. Deep neural networks, can indeed pose challenges in tracing and articulating the decision-making process. This is due to the complex interactions between numerous parameters, making it difficult to pinpoint the exact features or patterns influencing a specific prediction. On the other hand, Many traditional models, such as linear regression or decision trees, generate predictions through relatively straightforward mechanisms (such as if else conditions). These models often involve clear rules and feature weights, allowing for a more intuitive understanding of how inputs translate into outputs.

It is always important to understand how your model works regardless if it is a Deep Learning, Reinforced Learning or simple Logistic Regression. I do not trust models I cannot explain. Moreover in many industries you simply cannot deploy a model that is a black box.

In my experience this is the main deciding factor about what model structure to choose. While DL models are very popular and extremely useful when dealing with complex problems, in many cases it can be absolute overkill when a simpler model will do the trick. Like the old saying goes "No need to use a sledgehammer to crack a nut!". A good rule of thumbs is if you're unsure about how complex of a model is needed, start with the simpler models and if their performance is not adequate, move on to more complex models. Simpler models are usually far quicker to implement and train so rather than spending a great many hours (or weeks) on putting together a complex DL model, just start with the basics. If that's enough, you've saved a lot of time!

I only use deep learning, for certain use cases. Deep Learning excels at finding complex patterns in high-dimensional data like images, audio and text. For well-defined tasks on tabular data, traditional ML is likely sufficient. ?? Problem complexity guides architecture choices.

I agree with this point, that we should consider complex problems for deep learning models, that is difficult for human to visualise the solution steps, write code to get the result and validate the output, predicted by the machine learning model.

The accuracy depends on various factors. Traditional ML models can outperform DL models if the data is less complex or smaller in size.

Beyond accuracy metrics, factors like training time, deployment costs, and integration with existing systems determine real-world viability. Deep learning's advantages may not always outweigh greater implementation complexity.

This is way too much of a generalization. No rule says that deep learning models have higher accuracy. All depends on the pipeline you're trying to achieve and what the goal state output is. For many simple use cases deep learning is overkill.

One crucial consideration that's guided my decision-making process is the availability of pre-trained models and existing research in the domain. Utilising transfer learning with deep learning models can sometimes mitigate the need for extensive data and resources. Meanwhile, the simplicity and interpretability of traditional models often make them a first choice in prototyping and understanding the underlying structure of a problem.

I advice to invest some time to try both techniques and learn the insights by yourself. Learning was never a waste of time. Also Try to implement the network architecture by yourself which can greatly help if you want to modify an existing architecture.

Deep learning does not eliminate the need for subject-matter experts. Human guidance is key to success, whether ensuring quality data or setting evaluation metrics.

I would follow Andrew Ng's advice to start small, validate the initial idea, and then iterate with more complex models until you reach your desired performance.

Deep learning models would do the feature learning process by itself. Which helps in finding hidden complex patterns within raw data and also at model training. we consider a deep learning approach when data volume is large and task are complex , like computer vision.