AI revolutionizes daily life, automating businesses and enhancing energy control through smart devices. It mirrors human cognition, training machines in continuous learning, reasoning, and self-improvement, akin to the human mind. The rapid evolution of AI owes much to Machine Learning (ML) advancements, with Deep Learning (DL) as the cornerstone, raising the discussion of Machine Learning vs Deep Learning.
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With the increasing impact of AI and its seamless functionalities are making businesses across all industries rapidly adopting AI-powered technology –
- PwC’s 2022 AI Business Survey shows that companies are unlocking AI for modernizing systems, business transformation, and better decision-making.
- Also, the McKinsey Technology Trends Outlook 2022 Report listed Applied AI and industrializing Machine Learning among the top 14 trends to boost productivity.
AI’s success has ML and Deep Learning as its base. AI operates on ML algorithms, particularly leveraging Artificial Neural Networks (ANNs) in Deep Learning. ANNs train machines akin to human brain neural networks, enabling independent intelligent decision-making. Common real-time applications include:
- Voice Assistants and Speech Processing
- Automotive and Self-Driving Cars
- Image and Facial Recognition
- Text Mining and Translation
- Fraud Detection in BFSI
While both Machine Learning and Deep Learning are integral to AI, Deep Learning functions as the core of Machine Learning, constituting a sub-domain. AI operates on the logical structure of ML algorithms, specifically Artificial Neural Networks (ANNs), which emulate human intelligence.
This article explores the thin-line differences between machine and deep learning, delving into their workings and concepts.
What is Machine Learning?
Machine Learning is a comprehensive study wherein computers learn how to perform day-to-day operations like humans without being instructed. It is competent to self-reform the generated outcomes by learning from previous errors and discrepancies.
ML largely depends on the quality of input data. And it encompasses various statistical standards, algorithms, models, and tools for deducing projections.
The significant applications of ML are in retail, marketing, healthcare, finance, transportation, manufacturing, transcription, IT, cybersecurity, agriculture, and media.
Some business use cases include-
- Anomaly detection in network logs
- Sentiment analysis
- Email monitoring
- Dynamic pricing and product recommendation
- 24X7 customer chatbot support
- Weather forecasting
- Traffic alerts
- Stock market projection
- Social media analysis
- News Classification
How Does Machine Learning Work?
Before understanding its workflow, note that the operation of machine learning is different compared to deep learning. ML majorly performs in the five steps as follows –
- Collects the historical data
It is the foundation step wherein ML inputs the latest updated, reliable, and quality data corresponding to the problem statement.
Further, ML thoroughly analyzes the collected data’s records, observations, and experiences to identify trends and patterns.
- Prepares the data
It mainly involves three operations – data wrangling, data exploration, and data pre-processing for Machine Learning success.
After that, the resultant data is split into training (to learn from) and testing (to check prediction accuracy) datasets.
- Builds the ML model
Based on the problem statement, ML builds the predictive model by classifying the training dataset into the suitable ML type.
Such as Supervised, Unsupervised, Semi-supervised, or Reinforcement Learning.
- Trains and tests the model
The developed model utilizes the appropriate ML algorithm to produce better predictions and improve learning capabilities. This trial and error process iterates until it delivers the desired accuracy level.
Once the model is successfully trained, it runs through the testing dataset. Then, different test cases are applied to the model to check its precision level.
- Deploys model for practical applications
Before the final deployment in the real world, ML evaluates the scope of refining the model by hyperparameter tuning.
Eventually, when the model’s performance reaches optimal accuracy, it is set up in a concurrent environment to make predictions.
What is Deep Learning?
Deep learning functions on the neural network architecture to closely simulate human brainpower end-to-end in computer systems. Though it is the principal part of ML, the core program of Deep learning differs from it.
It works on a large amount of quality data to generate high-precision results, considering the following prime goals –
- Achieve the intended purpose of the application
- Foster continual innovation to increase learning capabilities
- Optimize outcomes to enhance the accuracy level
DL has strengthened the following ML implications –
- Data analytics solutions for profitable decision-making
- Operations and sales for customer success
- Supply chain management in retail and eCommerce
- Cybersecurity intelligence in aerospace and defense
- Yield production for estimating agriculture metrics
- Medical imaging in the healthcare system
- Risk identification in BFSI
- Automated alerts for quality assurance in manufacturing
- Drug discovery and monitoring in pharmaceuticals
- Disaster, pandemic, and emergency management
How Does Deep Learning Work?
The projection strategy of Deep Learning does not require any feature extraction process. Here, the input data directly undergoes different layers of ANN and predicts the outcome. Let’s understand DL’s 5-step process –
- Defines the suitable network architecture type
The neural network architecture encompasses prominently three layers – an input layer, numerous hidden layers, and an output layer.
Corresponding to the problem case allocated and tasks to be performed, DL defines the best-suited algorithm, such as –
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Sequence to Sequence Models
- Feed-Forward Neural Networks (FFNNs)
- Radial Basis Function Networks (RBFNs)
- Modular Neural Networks (MNNs)
- Long Short Term Memory Networks (LSTMs)
- Generative Adversarial Networks (GANs)
- Multilayer Perceptrons (MLPs)
- Self Organizing Maps (SOMs)
- Deep Belief Networks (DBNs)
- Restricted Boltzmann Machines( RBMs)
- Autoencoders
- Configures the model for training
This is the step in which Machine Learning is involved, as DL does not prepare or classify the training data set.
Instead, it configures the Deep Learning Framework to train the model. And specify the crucial training elements for estimating the model’s accuracy.
- Fits the model on the given training dataset
DL utilizes the fit function to compute the input data, the final prediction, and iterations performed on the training dataset.
Here, the fit function continually evaluates the model’s preciseness by mapping its execution on a fixed number of iterations. With this, it downsizes the possibility of overfitting.
- Estimates model’s precision
Machine Learning and Deep Learning determine the accuracy level similarly.
DL applies variable, random, and un-trained testing datasets to validate the trained model’s effectiveness and deliverability.
- Sets up the model in a real-time environment
The final deployment step, wherein the fully trained model operates on multiple business applications and self-reforms based on concurrent experiences.
Learn from AnalytixLabs
As ML and DL are the base of AI, expertise in them is the need of the hour. Before going forward, you can check out our exclusive courses on Deep Learning with Python learning module and machine learning certification course, or book a demo with us.
Difference Between Machine Learning & Deep Learning
Let’s quickly look into the below-mentioned 13 key factors that explicitly brief the difference between ML and DL –
- Subject to AI
The progression in AI is directly proportional to the Machine Learning capabilities. Whereas ML innovations entirely rely on Deep Learning, mainly ANN functionalities. Specifically, ML is the subdomain of AI and superset of DL.
- Type of training dataset
Machine Learning demands structured training datasets due to dependency on multiple data operations. On the contrary, Deep Learning is comfortable with structured and non-structured training datasets.
- Handle data volume
Machine Learning operates on both small and large input data. However, Deep Learning primarily depends on Big Data, i.e., the millions of data points.
- Training and testing time
When comparing ML vs. Deep Learning, the training and testing times differ. ML takes much less time to train the model, but the testing duration is prolonged. On the other hand, DL’s scenario is vice-versa. It takes a long period to train the model. However, the testing phase is short.
- Hardware dependency
Due to low data volume, Machine Learning applications are easily executable on standard CPU-based computer systems. In comparison, Deep Learning applications demand GPU and robust hardware resources.
- Feature extraction
Machine Learning requires feature engineering by domain experts (human support). However, since Deep Learning functions on ANNs, it automatically learns high-level feature extraction on its own.
- Troubleshooting strategy
The phenomenon is different in Machine Learning Vs. Deep Learning. ML breaks down the entire problem into sub-parts. And after resolving each sub-part, it produces the outcome. On the contrary, Deep Learning deduces the result end-to-end.
- Utilize algorithms
Overall, Machine Learning utilizes automated and statistical algorithms to generate predictions. In contrast, Deep Learning uses neural network algorithms to make accurate projections.
- Outcome interpretation
With Machine Learning, domain experts can effortlessly set up, run, and interpret (cause and effect) the results generated. However, its effectiveness may be limited.
In comparison, Deep Learning requires a dedicated setup and produces quality results immediately. However, it is hard to explain the interpretation.
- Output delivery
Machine Learning delivers the output in numerical form. On the other hand, Deep learning provides output in almost all formats, like numbers, texts, sound, etc.
- Data analysis mode
The data analysis approach in ML needs data analyst experts to determine and examine algorithms. Whereas Deep Learning self-defines everything based on its real-time experiences.
- Solve problem cases
Machine Learning best suits simple, straightforward, and less complex problem statements. In contrast, Deep Learning competently takes charge of complex and challenging cases.
- Human support
Machine Learning demands domain expert involvement in crucial steps. At the same time, Deep Learning needs human support in the setup. And the rest of the production line runs with negligible staffing.
Also read: Data Science vs. Machine Learning vs. AI Deep Learning
Types of Machine Learning
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Supervised Machine Learning:
When a machine is provided with labeled data, input and ideal outputs are given for the model to learn, known as supervised learning.
To understand deep learning vs machine learning, know how a machine is made intelligent with supervised learning in ML. The labeled data works as a supervisor to set the parameter for the new dataset to learn and produce valid outputs. Based on the output, you can understand if the training data suits the prototype and gradually get classified data for a particular survey.
For example, you can prepare training data with some symptoms as input and diagnosed disease as the output for a labeled set. The ML model will succeed if the real-time output matches the ideal symptom disease.
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Unsupervised Machine Learning:
Unlike supervised learning, unsupervised learning only has some inputs. Based on the primary properties of the input, the machine tries to generate a vogue idea and group similar data points into a category. This process is clustering.
Gradually, the model gets refined with a k-means clustering algorithm. Most machines follow this unsupervised learning in real time. One key advantage of this method is its flexibility. Unsupervised machine learning can adapt to different situations efficiently because no strictly labeled data exists. Both machine learning and deep learning require this back-and-forth training to reach maximum accuracy.
For example, online shopping portals repeatedly display similar products based on your past shopping trends and recent product searches.
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Semi-Supervised Machine Learning:
Both supervised and unsupervised learning have some drawbacks. Supervised learning would be expensive and time-consuming for large data, whereas unsupervised learning has a limited application spectrum.
That’s why semi-supervised learning is introduced. Both labeled and unsupervised data are inserted into the input, letting the ML model learn the properties faster. So, the new data can follow the basic properties of a category while developing its intuitive learning.
From this concept, you can note the difference between ML and DL. At first, one type of learning is selected, and then the ML network adapts any suitable deep learning approach to train the model better.
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Reinforcement Learning:
The method can be simply termed a trial and error-approach. Here, the agent (or the input data) performs something in an environment. Based on the performance, either it receives some rewards or some penalty.
Just like a game, the agent would train itself with the right and wrong assumptions based on those received rewards and penalties. O-learning is a popular algorithm to use for reinforcement learning in machine learning.
As you get a basic idea of the primary AI learning methods, it’s time to learn about deep learning.