Machine learning and deep learning are the two tech buzz words and it seems that everyone is just talking about these futuristic technologies. But interestingly few knows the working of the technology and how they differ from each other. Most people used terminologies interchangeably without knowing anything about the individual aspects of the technology.
Machine learning and deep learning does in fact stem from a similar technology that is; Artificial Intelligence (AI), which is perhaps one of the reasons why many people see both of the technologies as the same.
Deep learning and Machine learning – Interesting Facts:
- According to the New York Times, the salary of an AI specialist is equal to that of a Rolls Royce Ghost Series II 2017
- People across the world have been wary of the rise of Artificial Intelligence for 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 much 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 Theorist”. This was written in 1955
- The market for artificial intelligence is expected to swell to over USD 5 billion by 2020
Seems interesting right?
Now let’s see the difference between two subsets of Artificial Intelligence; machine learning solution and deep learning and how each of these technologies can help improve the opportunities for businesses.
Deep learning & Machine learning
Machine learning is a powerful derivative technology of artificial intelligence which is primarily associated with the development of self-learning algorithms. The machine learning algorithms can be trained using sample data, which then can perform any designated tasks automatically without the need for human intervention.
Deep learning is yet another derivative of artificial intelligence technology. Deep learning is also associated with the development of algorithms, however, in this case, the algorithms are created at multiple levels, each level 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 human brain.
Let’s understand it better with an example:
Suppose you have a collection of dogs and cats photos and now you want to identify each of the images separately in the categories “dogs” and “cats” using deep learning neural network and machine learning algorithms. Let’s see how each of these technologies would complete the task.
Working on Machine Learning
As mentioned earlier, machine learning algorithms need to be trained for a specific work task before it can take over the task and deliver it automatically. Here’s how you will have to train the algorithm;
To be able to train the machine learning algorithm, you will need a massive volume of the structured data, which in this case means that a large number (possibly thousands) of cats and dogs images. At first step, you will classify dogs and cats images for the machine to train the algorithm about the characteristics of each animal, the key here is the volume of the data. The larger the training data the better results you can expect from the algorithm. Once, the algorithm is trained for the datasets, it will continue to classify the images automatically using the characteristics it had developed during the training module.
Working of Deep learning:
Unlike machine learning, the deep learning neural network will take a different approach to come up with the solution for the given problem. For starter, 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 sent through the different levels of the neural networks, where each hierarchy will determine the characteristics of the image.
Again, the neural network will function quite similar to our brain; running queries across the different levels to find the answer. After processes the data across all the levels of the neural network, the system will come up with the best solution; in this case, the classification of the images.
Now, as can be taken by this example, 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 hierarchy levels to find the best possible solution.
Differences between deep learner and machine learning:
- The primary difference between deep learning and machine learning technologies is the way they process the datasets. Where machine learning requires structured data for training, deep learning neural networks can automatically process the data with its hierarchy based algorithm levels.
- Machine learning as a service algorithm is developed to “learn” from the training data, to be able to produce results with new datasets. The larger the training data, the better results you can expect from the machine learning algorithms
- Deep learning artificial neural networks “ANN” don’t require any human intervention for training. The multi-level algorithms in the ANN system are to self-process the data and learn by themselves eventually after making mistakes.