For most people, Deep Learning, Machine Learning, and Artificial Intelligence mean the same, often using these terms almost interchangeably. However, the fact is far from being true.
The fact is that both Machine Learning and Deep Learning are sub-sets of Artificial Intelligence. So, before we go deeper into understanding the differences between Deep Learning and Machine Learning, let us understand the two terms better.
What is Machine Learning?
A part of Artificial Intelligence (abbreviated as ML), Machine learning is the study of computer algorithms that can improve upon themselves through experience and by using data. First, these algorithms build a model based on a sample dataset called 'training data'. Then, the algorithm studies this data and identifies patterns to make predictive models without programming.
The broader applicability of Machine learning algorithms makes it very useful for being used in various applications, including image recognition, speech recognition, product recommendations, self-driven cars, and many more. In addition, ML is beneficial in cases where developers find it difficult or unfeasible to develop conventional algorithms to arrive at crucial decisions.
Machine Learning Algorithms:
There are different ways to train machine learning algorithms, each with its own pros and cons. Some machine learning algorithms that define the types of Machine Learning are listed below.
Supervised Learning:
In this process, the ML algorithm is fed with a small training dataset, which is quite similar to the final dataset. This test data studies the trends and establishes a cause and effect relationship between the input and the output. Once this relation is established, the system uses this relationship on the actual data to generate the required output. The best part of this algorithm is that it keeps discovering new patterns and relationships and improving itself as further information is provided.
Unsupervised Learning:
This algorithm works without any human intervention in labelling the data and making it machine-readable. It automatically perceives the relationship between data points in an abstract manner and creates hidden structures. The algorithm adapts to the data by dynamically changing hidden structures through a defined and set problem statement.
Reinforcement Learning:
This algorithm is directly inspired by how human beings use data. The algorithm uses a hit and trial method to find favourable and unfavourable outputs and thus learn from them. The unfavourable outcomes are rejected or 'punished', and the algorithm is asked to repeat the process till a favourable outcome is reached. But the favourable outcomes are sent to an interpreter. This interpreter reinforces the solution by rewarding the algorithm.
How Machine Learning Works?
Machine Learning is one of the fascinating sub-sets of Artificial Intelligence. It enables a machine to learn from data without getting any specific inputs. To understand how ML can be helpful to us in the future, let's know how it works. The step-by-step process is given below:
- The process begins with collating training data fed into the selected algorithm.
- The machine learning process starts with inputting training data into the selected algorithm. This training data affects the algorithm, and the concept is covered momentarily.
- New input data is fed into the machine to test whether the algorithm works correctly. The prediction and results are then tallied with each other to see their accuracy.
- If the prediction and results do not tally, the algorithm is trained again to give the desired outcome. This enables the algorithm to learn continually and produce the optimal answer, every time enhancing its accuracy.
What is Deep Learning?
Just as ML is a subset of AI, Deep Learning is often considered a sub-set of Machine Learning. Many people even consider it to be the next milestone of Machine Learning. For example, Google, Netflix, and many song apps use Deep Learning to make recommendations to users basis their previous searches.
In simple words, Deep Learning is teaching computers to learn just the way humans learn naturally – learn by example. The computer learns and performs classification tasks directly from images, text, or sound. This model delivers state-of-the-art accuracy, sometimes even surpassing human performance. The computer models are fed a large amount of labelled data and neural network architectures that contain many layers. This helps the computers get trained and perform with great accuracy consistently.
Deep Learning Models:
Deep Learning models or algorithms are trained by using a large amount of labeled data and neural network architectures. Then, these algorithms learn directly from the data without any manual intervention.
These models extract high-level abstract features from the data to improve performance over traditional models. There are several Deep Learning Models. Some of these models are listed below:
- Feedforward neural network
- Radial basis function neural networks
- Multi-layer perceptron
- Convolution neural network (CNN)
- Recurrent neural network
- Modular neural network
- Sequence-to-sequence models
Most deep learning methods use neural network architectures. That is why; they are sometimes referred to as deep neural networks. The neural networks contain hidden layers. Because of these hidden layers only, it got the name 'Deep'. Traditional neural networks only contain 2-3 hidden layers, while deep networks can have as many as 150.
Out of all these models mentioned above, the most popular model is Convolutional Neural Network (CNN or ConvNet).
How Deep Learning Works?
- Deep Learning uses a Neural Network to mimic human intelligence. The neural network contains three layers - the Input Layer, the Hidden Layer(s), and the Output Layer. Deep Learning, more or less, works like a human brain. The step-by-step process is given below.
- Much the way the human brain is designed, the neural networks are layers of nodes. These nodes within a layer are connected to adjacent layers. The more the no. of layers, the deeper is the network.
- In the human brain, a single neuron receives thousands of signals from other neurons. Similarly, in an artificial neural network, a single search generates a signal received by the first layer. This signal then travels between nodes and assign corresponding weights.
- The outermost layer is called the input layer; the innermost layer is the output layer. The middle layers are called hidden layers because their values aren't observable in the training set. In simple terms, hidden layers are calculated values the network uses to do its "magic".
- The item searched more often generates more signals, and a higher weightage is assigned to it. A signal with a heavier weight exerts more effect on the next layer of nodes.
- The final layer compiles all the weighted inputs to produce an output.
- Deep learning systems deal with a large amount of data. Hence, they require powerful hardware to handle the huge amount of data and complex mathematical calculations. Due to this, deep learning training computations can take a long time, even weeks.
Key comparison between Machine Learning and Deep Learning:
|
S. No. |
Machine Learning |
Deep Learning |
|
1 |
Technology that enables a machine to simulate human behavior. |
A subset of AI that allows a machine to learn from past data without programming explicitly or automatically. |
|
2 |
Make a smart computer system like humans to solve complex problems. |
Allow machines to learn from data so that they can give accurate output. |
|
3 |
AI has an extensive scope. |
Machine learning has a limited scope. |
|
4 |
AI is working to create an intelligent system that can perform various complex tasks. |
Machine learning is working to create machines that can perform only those specific tasks they are trained in. |
|
5 |
AI system is concerned with maximizing the chances of success. |
Machine learning is mainly concerned with accuracy and patterns. |
|
6 |
Main applications include Siri, customer support using chatbots, Expert System, Online game playing, the intelligent humanoid robot, etc. |
Main applications include an Online recommender system, Google search algorithms, Facebook auto friend tagging suggestions, etc. |
|
7 |
Based on capabilities, AI can be divided into four types - Reactive Machines, Limited Memory, Theory of Mind, and Self-awareness. |
Machine learning can also be divided into three types - Supervised Learning, Unsupervised Learning, and Reinforcement Learning. |
|
8 |
It includes learning, reasoning, and self-correction. |
It includes learning and self-correction when introduced to new data. |
|
9 |
AI completely deals with Structured, semi-structured, and unstructured data. |
Machine learning deals with Structured and semi-structured data. |
Courses in Machine Learning and Deep Learning:
Professionals interested in making a career in Machine Learning and Deep Learning can choose one of the FutureSkills Prime certifications, depending upon his area of interest and career goals. In addition, a learner may select any of these online courses offered by SkillUp Online for Machine Learning.
Machine Learning with Python - A Practical Introduction:
This introductory course offered by SkillUp Online helps enrollees learn the basics of ML using Python. You can discover how to identify hidden insights, predict future trends, and create prototypes using popular algorithms and models.
Data Science and Machine Learning Capstone Project:
This advanced-level course offered by SkillUp Online gives the learners a deep understanding of machine learning. The learners also get a chance to complete a hands-on capstone project which they can highlight on their resume.
Machine Learning with Python:
This course dives into the basics of ML using Python. The learners learn the difference between Supervised and Unsupervised Learning and compare Statistical Modeling vis-a-vis Machine Learning. While looking at several real-life examples, the learners explore several algorithms and popular models.
Machine Learning with Apache SystemML:
This course offered by SkillUp Online adopts a holistic approach towards big data. The course focuses on using Big Data as a platform to handle the variety, velocity, and volume of data by using a family of components that require integration and data governance. In addition, the course aims to use big data to generate insights that help businesses provide better customer service and be more profitable.
Similarly, a professional looking to make a career in Deep Learning may choose any of the Deep Learning courses:
Deep Learning Fundamentals: This course aims to give beginners an understanding of Deep Learning fundamentals and their use cases. You get to learn about convolutional neural networks and also about what makes Deep Learning so powerful.
Deep Learning with TensorFlow: In this course, the enrollees learn about TensorFlow, one of the best libraries to implement Deep Learning. You will learn TensorFlow's primary functions, operations, and execution pipeline. Starting with basic examples, you can witness how it can be used in curve fitting. The course also covers various deep architectures like Convolutional Networks, Recurrent Networks, and Autoencoders.
Conclusion:
As the blog highlights, both Machine Learning and Deep Learning have endless opportunities in the near future. Be it domestic or business environments, the use of 'robots' will improve our everyday lives in several ways. It will help us save money and valuable lives without making any errors.
Given this fact, Machine Learning and Deep Learning are the future careers. This is the right time for young professionals to get into this and reap the desired benefits. This is an exciting field, with excellent salaries and unlimited challenges.
If you want to be a part of this challenging industry and make a career for yourself, check out SkillUp Online courses in Machine Learning and Deep Learning, and see your career touch the heights of success.
FAQs:
Q.1: What are the limitations of deep learning?
Ans: Deep Learning involves a lot of data and complex mathematical calculations. This requires more powerful hardware to handle this data. Also, it requires more time to train the system.
Q.2: Which one is better, machine learning or deep learning?
Ans: Though a sub-set of Machine Learning, Deep Learning is a better and more advanced version of Machine Learning.
Q.3: Why deep learning is so important in machine learning nowadays?
Ans: With a small data size, Machine Learning works fine. But if the data size is large or the use cases are complex, Deep Learning is required.
Q.4: What are the advantages of deep learning over machine learning?
Ans: The main advantages of deep learning over machine learning are that it can execute feature engineering independently without any human intervention. Also, Deep Learning can easily handle large and complex data, which Machine Learning cannot.