Deep learning is a special topic of machine learning where the learning models increase in their depth (number of layers) as opposed to increasing in width (number of features per layer).
Deep learning is a subsidiary of machine learning. The difference between them lies in the size of data they process. Deep learning algorithms need a large amount of data. When the data is small, deep learning algorithms don’t perform well. On the other hand, machine learning algorithms with their high quality principles win in this situation.
Deep learning is a special topic of machine learning where the learning models increase in their depth (number of layers) as opposed to increasing in width (number of features per layer).
Deep learning is just a type of machine learning algorithm.
ML is the discovery of patterns in data, and usage of those patterns to make informed decisions.
Deep learning is a particular type of ML algorithm which happens to work really well on problems with high-dimensional data, like images and language. It works well because it can automatically extract useful features from data.
A nice answer is here: https://stats.stackexchange.com/questions/205738/what-is-the-difference-between-machine-learning-and-deep-learning/205741#205741
Najla Matti please check
What's the difference between AI, machine learning and deep learning?
https://www.pycon.it/media/conference/slides/ai-e-machine-learning-cosa-bisogna-sapere.pdf
https://www.quora.com/Whats-the-difference-between-AI-machine-learning-and-deep-learning
https://www.youtube.com/watch?v=WSbgixdC9g8&t=753s
https://www.zendesk.com/blog/machine-learning-and-deep-learning/
https://www.analyticsvidhya.com/blog/2017/04/comparison-between-deep-learning-machine-learning/
Also, keep in mind that the adjectival term "deep" is very recent, not necessarily naming an equally recent concept.
Firstly, we have to clarify that Deep Learning (DL) is a subset of Machine Learning (ML), which is also a subset of Artificial Intelligent. Then, DL is a technique for realising ML.
Furthermore, in contrast to ML, DL:
1. needs high-end machines
2. needs considerably big amounts of training data to deliver accurate results
3. Uses Neural Networks
4. The output can be anything from score, and element, free text or sound, etc.
Deep learning gives better result in case of images, it is extended machine learning
Deep learning is a machine learning technique. The main advantage of DL is that we do not need to manually extract features .The network learns to extract features while training.
Deep learning is a special branch of machine learning where we increase the depth and features are automatically extracted by deep learning methods.
Dear Najla,
Deep learning is sub branch of machine learning under the umbrella of artificial intelligence that deals big data analysis and no need especial kind of engineering for features extractions.
Deep learning is a particular case of a broader family of machine learning methods base on learning data representation, as opposed to task specific algorithms.
Dear Najla Matti,
01. Machine Learning: (Programs that alter them selves):
Machine learning can be loosely interpreted to mean empowering computer systems with the ability to “learn”.
The intention of ML is to enable machines to learn by themselves using the provided data and make accurate predictions.
ML is a subset of artificial intelligence; in fact, it’s simply a technique for realizing AI.
It is a method of training algorithms such that they can learn how to make decisions.
Training in machine learning entails giving a lot of data to the algorithm and allowing it to learn more about the processed information.
For example:
Here is a table that identifies the type of fruit based on its characteristics:
Weight (grams) -- Texture ---------- Type of Fruit
135 ---------------------- Rough ------------------ Orange
160 ---------------------- Rough ------------------ Orange
115 ---------------------- Smooth ---------------- Apple
90 ----------------------- Smooth ---------------- Apple
100 ---------------------- Smooth ---------------- ?
As you can see on the table above, the fruits are differentiated based on their weight and texture.
However, the last row gives only the weight and texture, without the type of fruit.
And, a machine learning algorithm can be developed to try to identify whether the fruit is an orange or an apple.
After the algorithm is fed with the training data, it will learn the differing characteristics between an orange and an apple.
Therefore, if provided with data of weight and texture, it can predict accurately the type of fruit with those characteristics.
02. Deep Learning: (More Accuracy, More Math and More Compute):
As earlier mentioned, deep learning is a subset of ML; in fact, it’s simply a technique for realizing machine learning. In other words, DL is the next evolution of machine learning.
DL algorithms are roughly inspired by the information processing patterns found in the human brain.
Just like we use our brains to identify patterns and classify various types of information, deep learning algorithms can be taught to accomplish the same tasks for machines.
The brain usually tries to decipher the information it receives. It achieves this through labelling and assigning the items into various categories.
Whenever we receive a new information, the brain tries to compare it to a known item before making sense of it — which is the same concept deep learning algorithms employ.
For example:
Comparing deep learning vs machine learning can assist you to understand their subtle differences.
For example,
while DL can automatically discover the features to be used for classification, ML requires these features to be provided manually.
Furthermore,
in contrast to ML,
DL needs high-end machines and considerably big amounts of training data to deliver accurate results.
03. Where Is ML and Deep Learning Being Applied?
A. Computer Vision: We use this for different applications like vehicle number plate identification and facial recognition.
B. Information Retrieval: We use ML and DL for applications like search engines, both text search, and image search.
C. Marketing: We use this learning technique in automated email marketing and target identification.
D. Medical Diagnosis: It has a very wide usage in the medical field also. Applications like cancer identification and anomaly detection.
- Natural Language Processing
- For applications like sentiment analysis, photo tagging, online Advertising, etc
04. Uses:
Nowadays, Machine Learning and data science are in trend. In companies, demand for them both is rapidly increasing. They're in demand particularly for companies who want to survive to integrate Machine Learning in their business.
Deep Learning is discovered and proves to have the best techniques with state-of-the-art performances. Thus, Deep Learning is surprising us and will continue to do so in the near future.
Recently, researchers are continuous in exploring Machine Learning and Deep Learning. In the past, researchers were limited to academia. But, nowadays, research in ML and DL is making their place in both industries and academia.
I hope the information will help you.
With Best Wishes,
Samir G. Pandya
Deep learning is advancement of machine learning where we increase the depth. Algorithms like CNN, RNN
Deep learning is a branch of machine learning. In deep learning, the depth of the layers is increased and much broader.
https://www.analyticsvidhya.com/blog/2017/04/comparison-between-deep-learning-machine-learning/
Difference Between Machine Learning and Deep Learning. ... In the case of machine learning, the algorithm needs to be told how to make an accurate prediction by providing it with more information, whereas, in the case of deep learning, the algorithm is able to learn that through its own data processing. machine learning is a concept in which algorithms parse the data, learn from it, and then apply the same to make informed decisions. A simple example would be of Netflix, which uses an algorithm to learn about your preferences and present you with the choices that you may like to watch.
- Machine Learning Will be a Must for Survival - As we see a continuous growth in the popularity of machine learning and deep learning, it will become increasingly competitive for organizations to survive in the industry if they are not part of this bandwagon soon.
- Research to Flourish - Previously, research was limited to only academia, but now research has been flourishing in academics as well as the industry. Research in this field continues to expand as a number of funds being invested now is more than ever.
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