What is The Difference Between Deep Learning, Machine Learning and AI?
There is abnormal state of confusion around those terms. This post should help to understand the distinctions and connections of those fields. Let’s get started with the following picture. It explains the three terms artificial intelligence, machine learning, and deep learning:
The simplest and most straightforward approach to think about their relationship is to imagine them as concentric circles with — the likelihood that began things out i.e AI the biggest circle , — at that point machine learning which bloomed later, — lastly Deep learning which is driving the present AI blast — fitting inside both.
Artificial Intelligence VS Machine Learning Vs Deep Learning?
Watch these below 3 Videos to be clear.
To make it short:
Artificial Intelligence — “Human Intelligence Exhibited by Machines”
Machine Learning —“An Approach to Achieve Artificial Intelligence”
Deep Learning — “A Technique for Implementing Machine Learning”
In Long:
Artificial Intelligence :
Artificial Intelligence, or simply AI is the expansive umbrella term portraying computer systems endeavoring to impersonate human-like insight. John McCarthy, who instituted the term in 1956, characterizes it as “the science and building of making intelligent machines.”
Today, the areas of AI innovation are fundamentally Robotics, Machine Learning, Machine Vision and Natural Language Processing. However, the key advantage of AI in business is Predictive Analytics. Using AI algorithms, organizations can develop exponentially to increase noteworthy upper hand over their companions. General AI machines have stayed in the movies and sci-fi books in light of current circumstances; we can’t pull it off, at any rate not yet.
Popular AI industries today: Autonomous cars, Medical / Healthcare and much more.
To know more about AI click here.
Machine Learning:
The subfield of AI called Machine Learning (ML) uses algorithms to find patterns in data, and then uses a model that recognizes those patterns to make predictions on new data. It concentrates on developing algorithms that can enable computer systems to learn automatically, without being expressly programmed.
To finish this assignment, an extensive variety of algorithms have been developed such as Linear Regression, Logistic Regression, Support Vector Machines (SVM), K-Means, Decision Trees, Random Forests, Naive Bayes, PCA and lastly, Artificial Neural Networks (ANN). Also see 15 Algorithms Every Machine Learning Engineer Must need to know.
In general, machine learning may be broken down into two types: supervised, unsupervised, and in between those two. Supervised learning algorithms use labeled data, unsupervised learning algorithms find patterns in unlabeled data.
Semi-supervised learning uses a mixture of labeled and unlabeled data. Reinforcement learning trains algorithms to maximize rewards based on feedback.
Popular ML industries today: More prevalent in online businesses where you consumed stuff online, social media, stock markets and investment banking.
To know more about ML click here.
Deep Learning:
However, today, the new trendy ruling the market is Deep Learning (DL), and this procedure was conceived out of ANNs. Is it madly prevalent, as well as it is gradually wiping out every single other method of ML.
Deep Learning utilizes multi-layered neural nets and learns by crunching a lot of information.
In spite of the fact that the center thought was exhibited in the 60’s, it is just today with the accessibility of information and intense Graphical Processing Units (GPUs) that it demonstrated fruitfully.
Graphical Processing Units (GPUs) have sped up multi-core servers for parallel processing. A GPU has a massively parallel architecture consisting of thousands of smaller, more efficient cores designed for handling multiple tasks simultaneously.
There are different variations of Deep Learning Algorithms like:
a.) Deep Neural Networks for Improved Traditional Algorithms
b.) Convolutional Neural Networks for images
c.) Recurrent Neural Networks for sequenced data
The current achievements of DL were in the field of Machine Vision, Machine Translation, Speech Recognition, Automated Game Playing, Self Driving Vehicles and much more.
Popular ML industries today: Retail, ECommerce, and now in Transportation and medicine.
To know more about DL click here.
Different Research Areas and methods for AI ML and DL :
Artificial Intelligence:
Machine Learning,
natural language understanding,
language synthesis,
computer vision,
robotics, sensor analysis,
optimization & simulation,
Algorithmic game theory and computational social choice
and much more.
Machine Learning:
Deep Learning,
support vector machines,
decision trees,
Bayes learning,
k-means clustering,
association rule learning,
regression, and much more.
Deep Learning:
Artificial neural networks,
convolutional neural networks,
recursive neural networks,
long short-term memory,
deep belief networks, and much more.
Conclusion
So fundamentally they are branching of a similar tree of computer intelligence where high super computing, access on/to the web and thus accessibility of immense large video, audio and content information will make more things conceivable later on.
Goal of AI : Giving a machine the ability to think, reason, learn etc.
Goal of ML: Giving a machine the ability to learn stuff.
Goal of DL: Giving machines an ability to learn by various approaches quickly.
Current models and algorithms are essentially soaked and just new tech or flood of new data, yes, your voice and vision data which you will immerse your autonomous cars, your Alexa/HomePods and others will make more things possible.
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