Machine Learning vs. Deep Learning
Artificial Intelligence (AI) has two mechanisms of learning: Machine Learning and Deep Learning. As these two phrases are often interchangeable, let’s discover the differences between them in this article.
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Machine learning uses the information to instruct and search for accurate results. Its focal point is creating software that can easily access information and learn from the data. Many algorithms within machine learning support the classification or prediction of outputs given some input data.
Deep learning is a subset of Machine Learning, but artificial neural networks are the primary algorithms for training models. Researchers modeled artificial neural networks after the human brain to connect the different data inputs (aka nodes) through synapses. While in typical neural networks, the number of layers between the input and the output is few, the layers can grow in hundreds and more in deep learning models.
Kinds of Deep Learning Algorithms
Let’s see some common deep learning algorithms.
Recurrent Neural Networks:
The Recurrent Neural Networks use a critical component that is not available in simpler algorithms: memory. The computer can remember past data and decisions in memory and consider them when evaluating current data and introduce the power of context.
Researchers use Recurrent Neural Networks for NLP (natural language processing) work because the computer understands a text better if it can access memory of tone and context.
Convolutional Neural Networks:
Researchers use Convolutional Neural Networks to work with pictures. The term convolutional is for the technique that applies a weight-based filter throughout each detail of an image, assisting the computer to recognize and react to elements in the image itself.
The technique is beneficial when you must scan a high-resolution image for a particular product or feature. A specialized field of studying photo data is called computer vision — and is a growing field.
Difference between Deep Learning and Machine Learning
After learning the basics of the machine and deep learning, here are some crucial points of machine learning vs. deep learning
Feature Engineering is a process of using domain knowledge to build features or inputs for training models. Data scientists use feature engineering to transform existing data to increase the accuracy of their learning models. It is a time-consuming process and requires knowledge of the data and techniques.
In Machine Learning, mainly the applied features must be analyzed by an expert and then hand-coded as per the domain and data type. For instance, the components can be shape, texture, pixel value, orientation, and position. The work of machine learning algorithms depends on how exactly the elements are identified and extracted.
In Deep Learning, the algorithms are trying to learn advanced features from data. It helps lessen the work of creating a new feature extractor for every problem.
A principal distinction between deep learning and machine learning is its overall performance as the size of statics increases.
In Deep learning, if the information quantity is small, it does not perform well because the deep learning method requires a considerable quantity of data to comprehend it accurately.
The Machine Learning methods with their own designed rules will survive in this situation quickly and efficiently.
We use interpretability as a feature for comparison between machine and deep learning.
If we use deep learning to provide self–regulating scores to essays, its performance in scoring is impressive, and it is near-human performance. But there is a difficulty; it does not disclose the behind-the-scenes details or answer questions like why it has given that score. However, mathematically you can discover which nodes of the deep neural network are activated. Still, we don’t know what their neurons were supposed to copy and what these layers of neurons are performing collectively.
The Machine learning algorithms such as decision trees give you crisp rules as to why it is selected, making it easy to interpret the reason behind it. Moreover, the algorithms like decision trees and logistic regression are used in heavily regulated businesses for interpretability.
Deep Learning methods are mainly dependent on exclusive devices compared to old machine learning methods that are worked on backend machines. It occurs because the demand for deep learning algorithms includes GPUs that are an internal process of working.
Deep Learning methods naturally perform a vast quantity of matrix multiplication processes. These functions could effectively optimize by using GPU because GPU is specifically created with this goal.
Usually, deep learning algorithms take longer to train because there are various deep learning algorithms that provide training, which takes longer than usual. State-of-the-art deep learning algorithm ResNet is required two weeks to train entirely from scratch. However, the Machine Learning Algorithms take less time to prepare, ranging from a few minutes to a few hours.
The testing or validation time, however, is the opposite. Deep learning algorithms take less time to perform the testing process. However, suppose you compare it with k – nearest neighbor, a type of machine learning algorithm. In that case, the test time increases with the increasing data size even though it does not apply to all machine learning algorithms, as some take less time to run.
When solving the problem using machine learning algorithms, it is advisable to break them into various parts, resolve them specifically, and mix them to obtain results. In contrast, deep learning advocates solving the difficulty at the backend.
For example, if you have work for dual-target detection, it is to search the target and where it is presented in the photo.
In the Machine learning method, you can split the issue into target detection and target recognition. Firstly you use a complete pack detection method like grab cut to remove images and search required targets. Then all the identified things, you will use object detection methods like SVM with HOG to identify pertinent targets.
In the deep learning method, you would follow the procedure at the backend. In a Yolo net (it is a kind of deep learning method), you will present a photo, and it will provide you with the location and the title of the object.
Deep learning is an advanced machine learning system that relies on often unstructured and enormous quantities of data. Thus, deep learning can cater to a bigger cap of issues with extra ease and efficiency. Technological breakthroughs like Google’s Deep Mind are the epitome of present-day AI, facilitated by deep learning and neurological networks.