Machine Learning Vs Deep Learning
Machine Learning Vs Deep Learning
The year 2020 has proved to be a roller coaster ride for all of humanity, at least so far. However, it's also when we have seen an unprecedented hype and interest in new and innovative technologies. The top contenders for this list of exciting technology include Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL). This hype is the application of AI/ML/DL in various fields, especially medical and space research. As you know, medical research is the top priority in 2020.
If you want to get more familiar with the meaning of these terms or revise them before going for an interview, you are in luck and the right place. We'll go through each of them one by one before we actually talk about machine learning training. Let's start with the most common term:
Artificial Intelligence (AI):
Almost anyone using a modern smartphone or smart appliance must have heard of AI. Even the non-techies are familiar with the term AI, thanks to IRON-MAN (We love you 3000!).
The Google Assistant in your Android device or Siri in your Apple devices is the best example of AI. Though AI is much more than your assistant. Let's see the definition of AI:
In simple terms, AI tries to mimic human behavior and actions, especially while solving complex problems like classifying cat vs. dogs based on images, image or face recognition, medical drug research, music creation, etc. To simplify it further, think of "General AI" as systems that possess the same characteristics as that of human intelligence. It is possible because they have all our senses (sometimes even more), they reason like us and think like us. Their best examples are "The Terminator," "C3-P0". As of now, general AI is still in our fantasies due to obvious reasons.
With basics of AI out of the way, let's see three types of learning AI:
- Narrow AI (Weak AI): This is where all the hype and applications of AI currently is. It's an AI system that can perform a specific task the same as or better than humans. They are generally not capable of experiencing consciousness. Eg., AI in games, Face Detectors in social media, Object and shape recognition, etc.
- General AI (Strong AI): A General AI system has reached such a general state at which it can perform any intellectual task with the same accuracy level as humans. They are capable of experiencing consciousness.
- Super AI (Theoretical AI): A Super AI system is something that surpasses human intelligence in all aspects. It can even beat the brightest among us. It's the type that famous faces like Elon Musk, will likely lead to human extinction.
Machine Learning (ML) and Deep Learning (DL):
Machine Learning (ML):
t's a subset of AI used to provide the systems the ability to learn and improve from its experience automatically, that too, without being explicitly programmed to do so. But how does it happen? How machine learning training can give you expertise in ML?
ML relies on a sophisticated set of algorithms as well as structured and cleaned data with labels (mostly). It tries to learn the relations between the features (columns or attributes or essential pieces of data that are the key to the solution of the task) of the data which you have provided to the ML model.
The key idea is to use essential features and their relationship with the label (ground truth) to derive the task's solution. Learning relation identifies patterns, inherent nature of the data, and how their values affect the label (ground truth). Based on what it has learned from parsing the data, it makes informed decisions using various algorithms. The ML model is trained on the training dataset, validated for performance and accuracy on the validation dataset, and then tested on the test dataset.
ML differs from the traditional systems by removing the need to code the logic behind the solution explicitly. For instance, instead of a programmer hand-coding the formula/reasoning for prediction of sales, the model itself learns the philosophy based on the patterns and relations it acquired during the training phase.
Deep Learning (DL):
We'll need to understand Artificial Neurons (AN) before starting with DL. Inspired by the biological neurons in our brains, the Artificial Neurons (AN) mimics the neurons in a brain. A network of such ANs together is called Artificial Neural Network (ANN). ANN is the building block of AI and mimics the network of neurons in a brain.
A single neuron based model, Perceptron, is capable of primary binary classification. However, it cannot solve most of the complex problems. Hence, we need a Multilayer Perceptron Model (MLP). MLP model is a collection of multiple neurons placed in a layered architecture. MLP is the simplest ANN. It too, however, cannot solve most of the modern problems. We need more powerful and deep neural networks to solve them.
Which to use when (ML or DL)?
Machine learning is used when the training data is structured, and you have fewer data points for training a DL model, which can lead to over-fitting—also used when the interpretability of the model is more important than accuracy or performance of the model.
Deep Learning is used when you have unstructured data; you have a vast number of data points, perfect for training a DL model—also used when the interpretability of the model is not more important than the accuracy or performance of the model.
Machine Learning Vs Deep Learning:
- Machine Learning (ML)
- Requires structured data
- Can work with fewer data points
- Feature extraction is done manually
- Model is interpret able
- Generally computationally less expensive
- Deep Learning (DL)
- Can handle and work with unstructured data
- Need a vast number of data points
- The mode extracts feature automatically
- Model is not interpret able
- Generally computationally more expensive
Machine learning training will equip you with all the required capabilities to work in a fast competitive, and AI-driven world. You might feel secure in the fast-growing automated world with machine learning training.