Dear doctor

I admit definitions for deep learning and machine learning:

Deep learning: a type of machine learning based on artificial neural networks in which multiple layers of processing are used to extract progressively higher level features from data. "the prototype will use a combination of deep learning, natural language processing, and dynamic network analysis to detect and examine the cross-platform spread of disinformation"

Machine learning: the use and development of computer systems that are able to learn and adapt without following explicit instructions, by using algorithms and statistical models to analyse and draw inferences from patterns in data.

Dr.Sundus Fadhil Hantoosh

Dear doctor,

Generally, machine learning (ML) often requires more human intervention to provide hand-crafted features via data engineering. On the other hand, deep learning (DL) learns on its own from data/environment and past mistakes [1], which is its main advantage over ML. This benefit may grow if you provide more data for DL training, where the DL models have more data to learn from. Besides, DL models are often more complex, and can makes non-linear and complex correlations [1].

Having said so, DL is not always better. DL is usually better for unstructured data (images, videos, texts), while ML is often better for structured data [1].

Hope this helps!

References:

[1] https://www.coursera.org/articles/ai-vs-deep-learning-vs-machine-learning-beginners-guide

Dr Wisam Mohammed Abed Alqaraghuli ,

I wanted to inform you that I have found several references that may answer your research question. Here are a few suggestions:

Kulyukin, V., Mukherjee, S., & Amlathe, P. (2018). Toward Audio Beehive Monitoring: Deep Learning vs. Standard Machine Learning in Classifying Beehive Audio Samples. Applied Sciences.

Ullah, M., Marium, S., Begum, S., & Dipa, N. (2020). An algorithm and method for sentiment analysis using the text and emoticon. ICT Express.

Mutegeki, R., & Han, D. (2020). A CNN-LSTM Approach to Human Activity Recognition. 2020 International Conference on Artificial Intelligence in Information and Communication (ICAIIC).

I recommend consulting these articles to gain relevant and in-depth insights into your research topic..

it depends on the number of data, Deep learning requires a large number of data, if your dataset is too small ML algorithms may be better for obtaining better accuracy.

also, it depends on your data type, machine learning models can not be good on image datasets(i.e. KNN vs ANN for mnist dataset)

Deep learning offers several advantages over traditional machine learning methods. Some of the main ones include:

• Automatic feature learning: Deep learning algorithms can automatically learn features from the data, which means that they don’t require the features to be hand-engineered.
• Handling large and complex data: Deep learning algorithms can handle large and complex datasets that would be difficult for traditional machine learning algorithms to process.

Deep learning is a subset of machine learning that uses artificial neural networks to solve more complex problems that machine learning algorithms might be ill-equipped for. You can think of them as a series of overlapping concentric circles, with AI occupying the largest, followed by machine learning, then deep learning. Deep learning models can automatically learn hierarchical representations of data, eliminating the need for manual feature engineering. Deep learning algorithms can handle large and complex datasets that would be difficult for traditional machine learning algorithms to process. Deep learning models can achieve state-of-the-art performance on many tasks.

You may read this paper: Article Deep learning modelling techniques: current progress, applic...