Introduction to Deep Learning: Machine Learning vs Deep Learning



deep learning and machine learning both offer ways to train models and classify data this video compares the two and it offers ways to help you decide which one to use let's start by discussing the classic example of cats versus dogs now in this picture do you see a cat or a dog how are you able to answer that chances are you've seen many cats and dogs over time and so you've learned how to identify them this is essentially what we're trying to get a computer to do learn from and recognize examples also keep in mind that sometimes even humans can get identification wrong so we might expect a computer to make similar errors to have a computer do classification using a standard machine learning approach we'd manually select the relevant features of an image such as edges or corners in order to train the machine learning model the model then references those features when analyzing and classifying new objects this is an example of object recognition however these techniques can also be used for scene recognition and object detection when solving a machine learning problem you follow a specific workflow you start with an image and then you extract relevant features from it then you create a model that describes or predicts the object on the other hand with deep learning you skip the manual step of extracting features from images instead you feed images directly into the deep learning algorithm which then predicts the object so deep learning is a subtype of machine learning it deals directly with images and is often more complex for the rest of the video when I mention machine learning I mean anything not in the deep learning category when choosing between machine learning and deep learning you should ask yourself whether you have a high-performance GPU and lots of label data if you don't have either of these things you'll have better luck using machine learning over deep learning this is because deep learning is generally more complex so you'll need at least a few thousand images to get reliable results you'll also need a high performance GPU so the model spends less time analyzing those images if you choose machine learning you have the option to train your model in many different classifiers you may also know which features to extract that will produce the best results plus with machine learning you have the flexibility to choose a combination of approaches use different classifiers and features to see which arrangement works best for your data you can use MATLAB to try these combinations quickly also keep in mind that if you are looking to do things like base detection you can use out-of-the-box MATLAB examples as I mentioned before you need less data with machine learning than with deep learning and you can get to a trained model faster too however deep learning has become very popular recently because it is highly accurate you don't have to understand which features are the best representation of the object these are learned for you but in a deep learning model you need a large amount of data which means the model can take a long time to train you are also responsible for many of the parameters and because the model is a black box if something isn't working correctly it may be hard to debug so in summary the choice between machine learning and deep learning depends on your data and the problem you're trying to solve MATLAB can help you with both of these techniques either separately or as a combined approach to find out more visit mathworks comm slash deep learning you

22 thoughts on “Introduction to Deep Learning: Machine Learning vs Deep Learning”

  1. Deep learning models are widely used in different fields due to its capability to handle large and complex datasets and produce the desired results with more accuracy at a greater speed. In Deep learning models, features are selected automatically through the iterative process wherein the model learns the features by going deep into the dataset and selects the features to be modeled. In the traditional models the features of the dataset needs to be specified in advance. The Deep Learning algorithms are derived from Artificial Neural Network concepts and it is a part of broader Machine Learning Models.

    This book intends to provide an overview of Deep Learning models, its application in the areas of image recognition & classification, sentiment analysis, natural language processing, stock market prediction using R statistical software package, an open source software package.

    The book also includes an introduction to python software package which is also open source software for the benefit of the users.

    This books is a second book in series after the author’s first book- Machine Learning: An Overview with the Help of R Software https://www.amazon.com/dp/B07KQSN447

    Editor

    International Journal of Statistics and Medical Informatics

    www.ijsmi.com/book.php

    Amazon Link

    https://www.amazon.com/dp/B07NJMM6LR

  2. Need your suggestion.
    I am new in MachineLearning/ DeepLearning and working on a project which has a large number of videos (Its requirement is to process all videos, detect objects, extract data from those and perform analysis).
    According to you, what should I start to learn first? Deep Learning or Machine learning.
    If Deep Learning is the subset of Machine learning, then will it be ok to go with Machine learning. I mean learning Machine learning will cover complete topics of Deep Learning or not.

  3. Does this seem ridiculous to anyone else? Deep learning is a subset of machine learning as far as i know. 'When you are doing deep learning you stop designing features and you need more data'…. uhhh ya…??? 'With deep learning you skip the step of manually extracting features'… uhhh how so? just for image classification or in general? I thought you still need to feed features to networks… do I have a major misunderstanding or is this silly?

  4. I want to classify a training data that doesn't depend on images using deep learning how can this be ?

  5. Deep leaning algorithms are also written by humans then why is it called "Black Box"? Can't we look into it and see how the computer solved it?

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