Machine learning vs Deep Learning
Machine learning vs Deep Learning
Machine learning and deep learning are often confusing terms for many beginners in artificial intelligence. This blog post would let you understand clearly the terms rotating around artificial intelligence and talks in detail about the difference between machine learning and deep learning.
Generally speaking, the following terminologies are oft heard when it comes to artificial intelligence. Here’s a sneak peek of those terms and what they mean on a whole:
It involves creating machines that can mimic humans in all aspects.
Machine Learning (ML)
It focuses on learning of machines and using that knowledge for similar scenarios.
It involves software that mimics the neurons of human brain.
Artificial neural networks (ANN)
They are for the computers just like how neural networks and brain is for humans.
In this post, let us delve deep into machine learning and deep learning and understand the difference in their concepts and processes.
Concept of Machine Learning
Machine learning talks about how machines learn. Here we get few questions – how do machines learn? Can they learn at all?
The answer is Yes. Machines learn the way humans do – through training, past experiences and feedback.
Machine learning focuses on training the machine rigorously and imparting it with maximum knowledge possible. Then, the machine is expected to use this knowledge in situations demanded and predict the correct output with ‘accuracy’. Observe that accuracy is important in machine learning.
There are many ways in which machines learn. Leaping back to our analogy with how humans learn – through teachers, through introspection, by analysing the current situation comparing with the past experiences and through others’ feedback and improvising the learning process – machines also learn in the following methods:
Here a teacher is present and learning outcomes are known at prior.
Here machine learns in the absence of teacher, just using its own experiences.
This is a mix of both supervised and unsupervised learning.
The machine learns and improvises through the feedback given to it.
In machine learning, various algorithms and techniques are involved:
- Neural networks
- Decision trees
- Random forests
- Support vector machines
- Nearest-neighbor mapping
- k-means clustering
- Self-organizing maps
- Expectation maximization
- Bayesian networks
- Kernel density estimation
- Principal component analysis
Concept of Deep Learning
When machine learning is all about machines learning, deep learning involves the technicality of how machines learn. It means deep learning is a subset of machine learning.
The term ‘deep’ in deep learning implies that the machine learns deep from many layers of artificial neural networks. Hence, the basic structure that is involved in deep learning is a neural network and the depth of the deep learning model is implied by the number of layers involved in it.
The basic working of a deep learning model is as shown above. Here we are considering a four-layer model for our understanding.
- Every input that the user gives goes into the neuron where it gets multiplied by its weight.
- This multiplied input is passes on to the next layer and is repeated for every layer of the entire network till the final layer – output is reached.
- The final layer contains the actual value of regression and the probability of classification tasks.
The accuracy of a deep learning model depends on the training it receives. A neural network is said to be fully trained if the weights are so adjusted that the output is very close to the desired output.
Prerequisites to learn Machine Learning and Deep Learning
Machine learning is a subset of artificial intelligence, while deep learning is a subset of machine learning. It means to learn deep learning and machine learning, the skill set required would be almost the same, except with few libraries.
- Programming language: R and Python; Python is mostly used and preferred.
- IDE: Jupyter and Google CoLab; Jupyter is mostly preferred and Google CoLab is budding in state.
- Libraries: Scikit-Learn, TesnorFlow, Pandas, NumPy and SciPy, Matplotlib, PyTorch and Keras.
Note that the libraries PyTorch and Keras are designed exclusively for deep learning and are extensively used only for deep learning.
Besides, you should be math lover as you have to deal with computations and algorithms that involve statistics and complex calculations. Not to mention, you should also have a lot of patience and attention to detail to get the tasks done.
Comparison of Machine learning and Deep learning
|Machine learning||Deep learning|
|A method to train machines.||A technique to implement machine learning.|
|Fits well on small sized data set.||Exclusive for working with big datasets.|
|Gives output in few minutes to hours depending on the task.||Output execution may take weeks owing to the number of weights and the computations involved in each layer.|
|Requires the specifications and features of data pertaining to training and testing.||Needs no features or specifications in particular.|
|Learner friendliness varies from easy to tough.||Deep learning algorithms are difficult to learn and implement.|
|A user can work on a normal system with good CPU capacity and speed.||Deep learning needs powerful computer with graphics processing unit to suit the needs of multiple computations involved.|
|You can choose features in ML.||Feature selection is not possible in DL.|
|Comprises many learning algorithms.||Comprises few, but complex algorithms.|
|Needs shorter training spans.||Needs longer training spans.|
Real time applications of Machine learning and Deep learning
As we have seen so far, machine learning and deep learning are inseparable. While machine learning has already penetrated into many of our day to day life business, it is the deep learning that is responsible for making possible some tasks that were never even imaginable a decade ago. Here are some industries and the specific applications where machine learning and deep learning find their applications.
- Banks and Financial Services: To detect fraud, and make trend analysis.
- Facial recognition: In airports, cameras, Google Photos, and to change pixels and colours of pictures.
- Voice recognition: In IoT devices, automotive and telecom industries.
- Text analysis to detect threats: In government applications and social media platforms.
Machine learning has been increasingly finding many applications over the past decade. However, it is due to deep learning that specific functionalities such as product recommendations, machine translations, advanced business intelligence and network security are finding a new face and a stronger skeleton.
A survey of LinkedIn shows that there are currently more than 1,829 openings as of today in India, but sadly not many professionals match the technical skills demanded from the industry. Experts are finding that the demand for deep learning engineers is surpassing that of data scientists, and together more than 78,000 jobs are lying vacant in India.
Be it machine learning or data science , all the professions have to work collaboratively. If you are looking for a promising career that would both be rewarding and challenging, this would be right choice for you.
Want to know more on making a career in Machine learning and data science? Get in touch with the career guidance experts at GangBoard, the top rated IT and Software Certifications Online Training .