5-minutes explainer: Machine Learning Vs Deep Learning

A simple explanation with a simple example

Hello Everyone, this is a simple blog, that I would like to make a case study regarding Machine Learning and Deep Learning. I hope this blog will be very helpful!

First, Machine Learning and Deep Learning are a part of Artificial Intelligence(AI). However, each has a distinction, let see what it is!

Machine Learning and Deep Learning became two of the most well-liked evolving technologies of the twenty-first century. It’s gaining a lot of buzzes, and it’s become a reality. Tech giants like Amazon, Facebook, Google are using Machine Learning and Deep Learning to ease human tasks and modify something and make everything doable.

Deep Learning could be a set of Machine Learning. So, you wish to grasp Machine Learning before you jump into Deep Learning. Machine Learning, you’ll handle massive volumes of structured information. To know, however, the algorithms on Machine Learning work, you wish to possess sensible information about statistics and chance.

You can begin implementing Machine Learning by creating some prediction analysis on continuous values such as predicting the full sale on things supported by previous sales trends or predicting the earnings of someone supported this age, and work expertise, etc. You’ll use rectilinear regression to hold out any prediction analysis wherever the output could be a numeric variable.

Then you can begin implementing algorithms for predicting categorical values like analyzing whether someone can purchase an automobile or not supported his earnings, and also the category of financial gain it belongs to. You can conjointly predict the category to that a specific flower belongs (an example may be Indian flower species — Marigold, Lotus, or Jasmine). To perform such analysis, you have to use provision regression, call tree, k nearest neighbours, etc.

Now bound sets of algorithms are in Machine Learning that had unlabeled information. This returns below unattended Learning Algorithms and is used for pattern recognition. These algorithms are used for client segmentation and targeted promoting in E-Commerce firms. Samples of such algorithms are K means that cluster, Apriori algorithmic program, etc.

If you actually wish to be told Deep Learning, then you need to have an honest understanding of how Deep Learning is connected to Artificial Neural Networks and Artificial Nodes Perform Operations. Deep Learning algorithms typically modify massive volumes of unstructured information like pictures, videos, audio, etc. You wish to possess a deeper understanding of however weights, biases, activation functions, backpropagation, value operate, etc work. You might have to be compelled to have information on a minimum of one Deep Learning library (TensorFlow, Keras, PyTorch, Theano, etc).

Machine Learning:

Machine learning could be a set of AI. It offers the power of the system to be told and improve from expertise while not being programmed to its level. Machine Learning uses the information to coach a model and realizes the correct results of the item. Machine learning focuses on the event of a computer virus that accesses the information and uses it to be told from themselves.

Machine learning could be a branch that mixes statistics and engineering science to assist a laptop to learn from the information. Neural networks are one variety of machine learning rule largely supported neural networks inside the brain. The essential plan is to try a series of mappings between layers of the neural network, optimized to suit the predictor matrix to AN outcome vector.

1. Machine Learning is a superset of Deep Learning
2. Machine learning is a field wherever the aim is to relinquish laptop systems the power to “learn” with information, while not being expressly programmed.
3. The information delineated in Machine Learning is kind of totally different as compared to Deep Learning because it uses structured data
4. Machine Learning is the Evolution of Artificial Intelligence(AI)
5. Machine learning will use several tools. Support Vector Machines, Bayesian classifiers, Call Tree Learning, and so on.
6. Machine learning consists of thousands of knowledge points.
7. Outputs: Numerical price, just like the classification of score
8. Uses varied kinds of machine-driven algorithms that intercommunicate model functions, and predict future action from the information.
9. Algorithms are detected by information analysts to look at specific variables in information sets.
10. Machine Learning is extremely wont to keep within the competition and learn new things.

Example — Machine Learning

Here is a simple example of machine learning, I mean face detection. This code will help to understand machine learning

https://gist.github.com/karthick965938/27c9b291628886f10818ca027591b215

import numpy as np
import cv2

# multiple cascades: https://github.com/Itseez/opencv/tree/master/data/haarcascades
faceCascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')

cap = cv2.VideoCapture(0)
cap.set(3,640) # set Width
cap.set(4,480) # set Height

while True:
    ret, img = cap.read()
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    faces = faceCascade.detectMultiScale(
        gray,
        scaleFactor=1.2,
        minNeighbors=5,     
        minSize=(20, 20)
    )

    for (x,y,w,h) in faces:
        cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)
        roi_gray = gray[y:y+h, x:x+w]
        roi_color = img[y:y+h, x:x+w]
        

    cv2.imshow('video',img)

    k = cv2.waitKey(30) & 0xff
    if k == 27: # press 'ESC' to quit
        break

cap.release()
cv2.destroyAllWindows()

https://github.com/karthick965938/Face-detection

Deep Learning:

Deep learning (DL) was born as a subfield of machine learning. This was inspired by the arrival of ever many powerful computers, and far faster ways to access larger volumes of knowledge. The algorithms square measure created specifically a bit like machine learning however it consists of many levels of algorithms.

All these networks of the algorithmic program square measure along referred to as the factitious neural network. In abundant easier terms, it replicates a bit like the human brain as all the neural networks square measure connected within the brain, specifically is the conception of deep learning. It solves all the advanced issues with the assistance of algorithms and their method.

Machine learning may be a technique of knowledge analysis that automates analytical model building. It’s a branch of computing that supported the thought that systems will learn from information, establish patterns, and create choices with tokenish human intervention.

1. Deep Learning learns multiple levels of illustration of the info.
2. The data illustration is employed in Deep Learning is kind of completely different because it uses neural networks(ANN).
3. Deep Learning is an associate degree in the evolution of Machine Learning. Basically, it is however deep in machine learning.
4. Deep Learning uses multiple layers, every applying/learning his transformation.
5. Big Data: A lot of information points.
6. Anything from numerical values to free-form parts, like free text and sound.
7. Uses neural network that passes information through process layers to interpret information options and relations.
8. Algorithms square measure for the most part self-depicted on information analysis once they are placed into production.
9. Deep Learning Solves advanced machine learning problems.

Deep learning may be an explicit quite machine learning that achieves country and adaptability by learning to represent the planet as a nested hierarchy of ideas, with every conception outlined in relevancy easier ideas, and many abstract representations computed regarding less abstract ones.

Example — Deep Learning

Here is a simple example of deep learning, I mean create a deep learning object detection model. This code will help to understand deep learning.

Click the below link to get the Google Colab version

https://gist.github.com/karthick965938/ad204df98a8da4954fde7ffd161d53a4

Conclusion:

So, my conclusion is to first learn Machine Learning and then jump into Deep Learning to understand it completely. But there is no such compulsion to follow this process.

Any questions please feel free to comment out below!