Direct education is much better than education via social media because it provides the opportunity to monitor the teacher and focus on some important topics in the curriculum and direct questions from the learner to his teacher. There is a saying that says: We are a nation of readers, and distance education does not produce genius students.
Dear Doctor
"The choice between machine learning and deep learning depends on the specific problem you are trying to solve. Deep learning has shown great promise in tasks such as image and speech recognition, while machine learning techniques may be more suitable for certain other types of problems."
Dear Wisam Mohammed Abed Alqaraghuli , I'll tell you my opinion that defines how I deal with the choice between these methods.
If the data set is easily represented as a table, I use machine learning. If the data set is "less structured", such as images and sounds, I use deep learning.
Something you can also take into account is the explainability of your model. In this respect, machine learning has more explainable methods.
Regars,
Marcel
The comparison between deep learning and machine learning is not a matter of one being inherently "better" than the other; rather, it depends on the context, task at hand, and the data available. Both deep learning and machine learning are subfields of artificial intelligence, and they serve different purposes.
Machine learning is a broader concept that encompasses various techniques and algorithms designed to enable computers to learn from data. In machine learning, algorithms are often based on statistical techniques that allow systems to improve their performance on a specific task through experience. Machine learning models can perform well with smaller datasets and may not require the massive amounts of data that deep learning models often do. Machine learning models are generally more interpretable, making it easier to understand how a model reaches a particular decision.
Deep learning, on the other hand, excels at automatic feature extraction and representation learning, eliminating the need for manual feature engineering. It is a subset of machine learning that specifically focuses on neural networks with multiple layers (deep neural networks). It is particularly powerful for complex tasks such as image recognition, natural language processing, and speech recognition. Deep learning models often require large amounts of labeled data for training, and they can benefit from increased computational resources. And, in certain tasks, deep learning models have achieved state-of-the-art performance, surpassing traditional machine learning approaches.
It's not accurate to say Deep Learning is better than Machine Learning overall. Think of them as different tools for different jobs. Deep Learning excels at handling complex, unstructured data (images, speech) and excels at tasks like natural language processing and image recognition. Machine Learning offers wider versatility, handling simpler data with less computational power, and shines in tasks like spam filtering and predicting financial trends. The "better" choice depends on your specific problem and data type.
short answer
in tabular data, no Preprint Why do tree-based models still outperform deep learning on t...
in image or spatial recognition, computer vision, NLP, then yes
- Badges
- Science topic
- Similar topics
- Biological Science
- Immunology
- Antibodies