What Is the Difference Between Machine Learning and Deep Learning?

Machine learning and Deep learning are the two tech buzzwords and it seems that everyone is just talking about these futuristic technologies. But interestingly many knows the working of the technology and how they differ from each other. Most people used languages interchangeably without knowing anything about the individual aspects of the technology.

Machine learning as a service and Deep Learning does in fact stem from a similar technology that is; Artificial Intelligence (AI), which is maybe one of the reasons why numerous people see both of the technologies as the same.

Deep learning and Machine learning – Interesting Facts

People across the world have been wary of the rise of Artificial Intelligence for a long and it seems that their concerns have some grounds at least. That’s because according to a recent study by PwC, Artificial Intelligence can claim as important as 38 of the jobs in the USA, where humans would be replaced by automated technology

Newell & Simon wrote the world’s first Artificial Intelligence program “ The Logical Philosopher”. This was written in 1955

The request for artificial intelligence is anticipated to swell to over USD 5 billion by 2020

Seems intriguing right?

Now let’s see the difference between two subsets of Artificial Intelligence; machine literacy and deep literacy and how each of these technologies can help improve the openings for businesses.

Deep learning & Machine learning

Machine learning

Machine learning is an important secondary technology of artificial intelligence that is primarily associated with the development of tone-learning algorithms. The machine learning algorithms can be trained using sample data, which also can perform any designated tasks automatically without the need for human intervention. Folio3 machine learning result company offer numerous services in this field

Deep learning

Deep learning is yet another outgrowth of artificial intelligence technology. Deep learning is also associated with the development of algorithms, still, in this case, the algorithms are created at multiple levels, each position detailing a different data interpretation. Together, the levels of the deep learning algorithms make up the artificial neural network, which in simple words replicate the working of neural connections in the mortal brain.

Working on Machine Learning

As mentioned before, machine literacy algorithms need to be trained for a specific work task before they can take over the task and deliver it automatically. Then how you'll have to train the algorithm;

To be able to train the machine learning algorithm, you'll need a massive volume of structured data, which in this case means a large number ( conceivably thousands). At the first step, you'll classify tykes and pussycats images for the machine to train the algorithm about the characteristics of each beast, the key then the volume of the data. The larger the training data the better results you can anticipate from the algorithm. Formerly, the algorithm is trained for the datasets, it'll continue to classify the images automatically using the characteristics it had developed during the training module.

Working on Deep Learning

Unlike machine learning, the deep learning neural network will take a different approach to come up with the result for the given problem. For starters, the benefit of using deep learning neural networks is that you don’t have to train the network using a massive volume of training data sets. Rather, the images will be transferred through the different situations of the neural networks, where each scale will determine the characteristics of the image.

Again, the neural network will serve relatively analogous to our brain; running queries across the different situations to find the answer. After processing the data across all the situations of the neural network, the system will come up with the stylish result; in this case, the bracket of the images.

Now, as can be taken by this illustration, the machine learning algorithm would require large structured data to train the algorithm for the task, whereas, in deep learning neural network, the system will automatically process the images across its different scale situations to find the best possible result.

Differences between deep learner and machine learning

The primary difference between deep learning and machine learning technologies is the way they reuse the datasets. Where machine learning requires structured data for training, deep learning neural network are suitable to automatically reuse the data with its scale grounded algorithm situations.

Machine literacy algorithms are developed to “ learn” from the training data, to be suitable to produce results with new datasets. The larger the training data, the better results you can anticipate from the machine learning algorithms

Deep learning artificial neural networks “ ANN” don’t bear any mortal intervention for training. The multi-level algorithms in the ANN system are to self- process the data and learn by themselves ultimately after making mistakes.