Deep Learning vs Machine Learning - Overview & Differences

Deep Learning vs Machine Learning - Overview & Differences

Machine learning and deep learning are two subsets of artificial intelligence which have garnered a lot of attention over the past two years. If you’re here looking to understand both the terms in the simplest way possible, there’s no better place to be..

The two areas of Artificial Intelligence, namely machine learning and deep learning, raise more questions than an entire field combined, mainly because these two areas are often mixed up and used interchangeably when referring to statistical modeling of data; however, the techniques used in each are different and you need to understand the distinctions between these data modeling paradigms in order to refer to them by their corresponding name. In this article, we’ll explain the definitions of artificial intelligence,** machine learning**, deep learning, and neural networks, briefly overview each of those categories, explain how they work, and finish with an explicit comparison of machine learning vs deep learning.

What is Artificial Intelligence?

Artificial Intelligence (hereafter referred to as AI) is the intelligence demonstrated by machines as opposed to the natural intelligence of humans. AI can be further classified into three different systems: analytical, human-inspired, and humanized artificial intelligence. Analytical AI generates the cognitive representation of the world through learning that’s based on past experiences to predict future decisions. Human-inspired AI has elements from cognitive and emotional intelligence, thus in order to predict any future decision, human-inspired AI attempts to understand human emotions in addition to cognitive elements. Humanized AI uses all types of competencies including but not limited to cognitive, emotional, social intelligence, as well as attempts to be self-conscious and self-aware of the interactions. Machine Learning and Deep Learning are two different subsets of Artificial Intelligence, which we’ll break down further below.

What is Machine Learning?

According to Wikipedia, “Machine Learning (hereafter referred to as ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without instructions, relying on patterns and inferences instead.” To put it simply, ML involves the creation of algorithms that can modify itself without human assistance to produce the desired output. Again, to simplify further: ML is a method of training algorithms so they can make decisions on their own. The intention behind ML is just as simple — enable machines to learn on their own when provided with data to make future predictions.

For example, in order to predict a storm, we have to gather the data of all the storms that have occurred in the past along with the concurrent weather conditions for a designated period of time, say six months. The task at hand: to determine which weather conditions almost certainly lead up to an upcoming storm. The results will be based on the input data that we supplied to the system. Then those results are analyzed versus the real-time conditions and the occurrence of the said storm, which can further lead to a reiteration of inputting data to polish future predictions.

How does machine learning work?

For the ML to work, special considerations need to be accounted for before applying ML to a problem. Among those are: there’s a clear pattern that would help a machine to arrive at a conclusion; there must be data readily available; the behavior can be described by a mathematical expression through a structured inference learning.

For example, the data could be multiple product reviews of one or several items, the output is the decision of whether the item is worth purchasing based on product reviews. The structured learning component is then performed by an ML algorithm to arrive at the pattern of the supplied data. The expression that the ML formulates is called the “mapping function,” which is used to learn the “target function,” that is — an expression maps the input data to output and arrives at a decision: buy an item or don’t.

So, ML performs a learning task where it makes predictions of the future (Y) based on the new given inputs (x).

Y = f(x)

To arrive at the target function (f), the prediction needs to be learned from the supplied examples (x); in our example, it tries to capture the representation of product reviews by mapping each type of review input to the output. To arrive faster to the conclusion, the algorithm considers certain assumptions about the target function and starts the estimation of that function from a hypothesis. Iterations of the hypothesis are done several times to estimate the best output.

Machine Learning Overview: summary of how ML works

The objective of ML algorithms is to estimate a predictive model that best generalizes to a set of data. For ML to be super-efficient, one needs to supply a large amount of data for the learning algorithm to understand the system’s behavior and generate similar predictions when supplied with new data.

Video Machine Learning:

Machine Learning Architecture & Development Life Cycle

Machine Learning programs operate under a lifecycle that’s different from a general software development lifecycle, meaning that the well-known app development methodologies such as agile and waterfall doesn’t work well enough with machine learning development.

The standard app development (left) vs Machine Learning development (right)

The reason for building a separate life cycle for machine learning is primarily to support the highly iterative building, testing, and deployment of ML models. While the process of planning, creating, testing, and deploying ML models might seem similar to any other application development cycle, still there would be a lot of adaptation in order to focus on ML model evaluation and fine-tuning. The following figure represents the suggested ML adapted life cycle that guides the development professionals in implementing a continuous model deployment and control framework for automating the process of developing, testing, deploying and monitoring ML models:

Continuous ML Model and Control Framework by Gartner

A lot of machine learning frameworks offer their own reference architectures that simplify the implementation of machine learning solutions. For example, TensorFlow’s system architecture is described in detail here, Azure ML architecture, concepts, and workflow — here.

Hereinbelow is the sample of machine learning architecture, which covers the following infrastructure areas for functions needed to execute the machine learning process:

  • Data acquisition, where data is collected
  • Data processing, including preprocessing, sample selection, training of data sets
  • Data modeling, including data model designs and ML algorithms used in ML data processing
  • Execution, where the processed and trained data is forwarded for use in the execution of ML routines such as experimentation, testing, fine-tuning
  • Deployment is the representation of business-usable results of the ML process — models are deployed to enterprise apps, systems, data stores.

Machine Learning Architecture Example by Gartner

What is deep learning?

Deep learning, on the other hand, is a subset of machine learning, which is inspired by the information processing patterns found in the human brain. The brain deciphers the information, labels it, and assigns it into different categories. When confronted with new information, the brain compares it with the existing information and arrives at the conclusion that spurs future action based on this analysis. Deep learning is based on numerous layers of algorithms (artificial neural networks) each providing a different interpretation of the data that’s been fed to them.

How does deep learning work?

Before we tackle the question of “how it works,” let’s briefly define a few other necessary terms.

Supervised learning is using labeled data sets that have inputs and expected outputs. Unsupervised learning is using data sets with no specified structure.

In the case of supervised learning, a user is expected to train the AI to make the right decision: the user gives the machine the input and the expected output, if the output of AI is wrong, it will readjust its calculations; the iterative process goes on until the AI makes no more mistakes. Among the popular supervised algorithms are linear regression, logistic regression, decision trees, support vector machines, and non-parametric models such as k-Nearest Neighbors. In the case of unsupervised learning, the user lets the AI make logical classifications from the data. Here, algorithms such as hierarchical clustering, k-Means, Gaussian mixture models attempt to learn meaningful structures in the data.

Deep Learning operates without strict rules as the ML algorithms should extract the trends and patterns from the vast sets of unstructured data after accomplishing the process of either supervised or unsupervised learning. To proceed further, we’ll need to define neural networks.

Deep Learning vs Neural Network

The Deep Learning underlying algorithm is neural networks — the more layers, the deeper the network. A layer consists of computational nodes, “neurons,” every one of which connects to all of the neurons in the underlying layer. There are three types of layers:

  • Data acquisition, where data is collected
  • Data processing, including preprocessing, sample selection, training of data sets
  • Data modeling, including data model designs and ML algorithms used in ML data processing
  • Execution, where the processed and trained data is forwarded for use in the execution of ML routines such as experimentation, testing, fine-tuning
  • Deployment is the representation of business-usable results of the ML process — models are deployed to enterprise apps, systems, data stores.

Neural Network

By adding more hidden layers into the network, the researchers enable more in-depth calculations; however, the more layers — the more computational power is needed to deploy such a network.

Each connection has its weight and importance, the initial values of which are assigned randomly or according to their perceived importance for the ML model training dataset creator. The activation function for every neuron evaluates the way the signal should be taken, and if the data analyzed differs from the expected, the weight values are configured anew and the iteration begins. The difference between the yielded results and the expected is called the loss function, which we need to be as close to zero as possible. Gradient Descent is a function that describes how changing connection importance affects output accuracy. After each iteration, we adjust the weights of the nodes in small increments and find out the direction to reach the set minimum. After several of said iterations, the trained Deep Learning model is expected to produce relatively accurate results and can be deployed to production, however, some tweaking and adjustments can be necessary if the weight of the factors change over time.

Deep learning Learning Overview: summary of how DL works

Deep Learning is one of the ways of implementing Machine Learning through artificial neural networks, algorithms that mimic the structure of the human brain. Basically, DL algorithms use multiple layers to progressively extract higher-level features from the raw input. In DL, each level learns to transform its input data into more abstract representation, more importantly, a deep learning process can learn which features to optimally place in which level on its own, without human interaction. DL is both applicable for supervised and unsupervised learning tasks, where for supervised tasks DL methods eliminate feature engineering and derive layered structures that remove redundancy in representation; DL structures that can be used in an unsupervised manner are deep belief networks and neural history compressors.

Video Deep Learning:

Deep Learning Applications

Now, let’s look at some of the top applications of deep learning, which will help you better understand DL and how it works, besides some of those offer fantastic tutorials and source code detailing how to implement those algorithms.

The most well-known application of deep learning is a recommendation engine that’s supposed to enhance the user experience and provide a better service to its users. There are two types of recommendation engines: content-based and collaborative filtering. Until you have a sizable user-base, it’s best recommended to start with the content-based engine first. Example: Keras Code for Collaborative Filtering

Natural Language Processing and Recurrent Neural Networks are used in the text to extract higher level information, also known as text sentiment analysis. Example: Twitter Sentiment Analysis

Another popular application is chatbots that can be trained with samples of dialogs and recurrent neural networks. Example: Chatbot with TensorFlow

Another popular application of DL models is image retrieval and classification using recognition models to sort images into different categories or using auto-encoders to retrieve images based on visual similarity. Example: Hacking Image Recognition

Machine Learning vs Deep Learning: comparison

One of the most important differences is in the scalability of deep learning versus older machine learning algorithms: when data is small, deep learning doesn’t perform well, but as the amount of data increases, deep learning skyrockets in understanding and performing on that data; conversely, traditional algorithms don’t depend on the amount of data as much.

Scaling with Amount of Data

Another important distinction which ensues directly from the first difference is the deep learning hardware dependency: Dl algorithms depend on high-end machines and GPUs, because they do a large amount of matrix multiplication operations, whereas older machine learning algorithms can work on low-end machines perfectly well.

In machine learning, most of the applied features need to be identified by a machine learning expert, who then hand-copies them as per domain and data type. The input values (or features) can be anything from pixel values, shapes, textures, etc. The performance of the older ML algorithm will thus depend largely on how well and accurately the features were inputted, identified, extracted. Deep Learning learns high-level features from data, this is a major shift from traditional ML since it reduces the task of developing new feature extractor for every problem, in turn, DL will learn low-features in early layers of the neural network and then high-level as it goes deeper into the said network.

Again, because of the large amount of data that needs to be learned from, deep learning algorithms take quite a lot of time to train, sometimes as long as several weeks, comparatively, machine learning takes much less time to train to range from a second to a few hours. However, during the testing time, deep learning takes less time to run than an average machine learning algorithm.

Also, interpretability is a factor for comparison. With deep learning algorithms, sometimes it’s impossible to interpret the results, that’s exactly why some industries have had slow adoptions of DL. Nevertheless, DL models can still achieve high accuracy but at the cost of higher abstraction. To elaborate on this a little further, let’s get back to the weights in a neural network (NN), which essentially indicates a measure of how strong each connection is between each neuron. So by looking at the first layer, you can tell how strong is the connection between the inputs and the first layer’s neurons. But at the second level, you’ll lose the relationship, because the one-to-many relationship has turned into many-to-many relationships, exactly because of the high complexity of the NN nature: a neuron in one layer can be related to some other neurons which are far away from the first layer, deep into the network. Again, weights tell the story about the input but that information is compressed after the application of the activation functions making it near impossible to decode. On the other hand, machine learning algorithms like decision trees give explicit rules as to why it chose what it chose and thus, they are easier to interpret.

Originally published by Marina Vorontsova at* ***


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Deep Learning Tutorial with Python | Machine Learning with Neural Networks

Deep Learning Tutorial with Python | Machine Learning with Neural Networks

In this video, Deep Learning Tutorial with Python | Machine Learning with Neural Networks Explained, Frank Kane helps de-mystify the world of deep learning and artificial neural networks with Python!

Explore the full course on Udemy (special discount included in the link):

In less than 3 hours, you can understand the theory behind modern artificial intelligence, and apply it with several hands-on examples. This is machine learning on steroids! Find out why everyone’s so excited about it and how it really works – and what modern AI can and cannot really do.

In this course, we will cover:

•    Deep Learning Pre-requistes (gradient descent, autodiff, softmax)

•    The History of Artificial Neural Networks

•    Deep Learning in the Tensorflow Playground

•    Deep Learning Details

•    Introducing Tensorflow

•    Using Tensorflow

•    Introducing Keras

•    Using Keras to Predict Political Parties

•    Convolutional Neural Networks (CNNs)

•    Using CNNs for Handwriting Recognition

•    Recurrent Neural Networks (RNNs)

•    Using a RNN for Sentiment Analysis

•    The Ethics of Deep Learning

•    Learning More about Deep Learning

At the end, you will have a final challenge to create your own deep learning / machine learning system to predict whether real mammogram results are benign or malignant, using your own artificial neural network you have learned to code from scratch with Python.

Separate the reality of modern AI from the hype – by learning about deep learning, well, deeply. You will need some familiarity with Python and linear algebra to follow along, but if you have that experience, you will find that neural networks are not as complicated as they sound. And how they actually work is quite elegant!

This is hands-on tutorial with real code you can download, study, and run yourself.

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Machine Learning Full Course - Learn Machine Learning

Machine Learning Full Course - Learn Machine Learning

This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning.

Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial

It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let's get started with Machine Learning.

Below topics are explained in this Machine Learning course for beginners:

  1. Basics of Machine Learning - 01:46

  2. Why Machine Learning - 09:18

  3. What is Machine Learning - 13:25

  4. Types of Machine Learning - 18:32

  5. Supervised Learning - 18:44

  6. Reinforcement Learning - 21:06

  7. Supervised VS Unsupervised - 22:26

  8. Linear Regression - 23:38

  9. Introduction to Machine Learning - 25:08

  10. Application of Linear Regression - 26:40

  11. Understanding Linear Regression - 27:19

  12. Regression Equation - 28:00

  13. Multiple Linear Regression - 35:57

  14. Logistic Regression - 55:45

  15. What is Logistic Regression - 56:04

  16. What is Linear Regression - 59:35

  17. Comparing Linear & Logistic Regression - 01:05:28

  18. What is K-Means Clustering - 01:26:20

  19. How does K-Means Clustering work - 01:38:00

  20. What is Decision Tree - 02:15:15

  21. How does Decision Tree work - 02:25:15 

  22. Random Forest Tutorial - 02:39:56

  23. Why Random Forest - 02:41:52

  24. What is Random Forest - 02:43:21

  25. How does Decision Tree work- 02:52:02

  26. K-Nearest Neighbors Algorithm Tutorial - 03:22:02

  27. Why KNN - 03:24:11

  28. What is KNN - 03:24:24

  29. How do we choose 'K' - 03:25:38

  30. When do we use KNN - 03:27:37

  31. Applications of Support Vector Machine - 03:48:31

  32. Why Support Vector Machine - 03:48:55

  33. What Support Vector Machine - 03:50:34

  34. Advantages of Support Vector Machine - 03:54:54

  35. What is Naive Bayes - 04:13:06

  36. Where is Naive Bayes used - 04:17:45

  37. Top 10 Application of Machine Learning - 04:54:48

  38. How to become a Machine Learning Engineer - 04:59:46

  39. Machine Learning Interview Questions - 05:09:03

Machine Learning vs Deep Learning: What's the Difference?

Machine Learning vs Deep Learning: What's the Difference?

In this post talks about the differences and relationship between Artificial intelligence (AI), Machine Learning (ML) and Deep Learning (DL)

In this post talks about the differences and relationship between Artificial intelligence (AI), Machine Learning (ML) and Deep Learning (DL)

**In this tutorial, you will learn: **

  • What is Artificial intelligence (AI)?
  • What is Machine Learning (ML)?
  • What is Deep Learning (DL)?
  • Machine Learning Process
  • Deep Learning Process
  • Automate Feature Extraction using DL
  • Difference between Machine Learning and Deep Learning
  • When to use ML or DL?
What is AI?

Artificial intelligence is imparting a cognitive ability to a machine. The benchmark for **AI **is the human intelligence regarding reasoning, speech, and vision. This benchmark is far off in the future.

AI has three different levels:

  1. Narrow AI: A Artificial intelligence is said to be narrow when the machine can perform a specific task better than a human. The current research of AI is here now
  2. General AI: An artificial intelligence reaches the general state when it can perform any intellectual task with the same accuracy level as a human would
  3. Active AI: An AI is active when it can beat humans in many tasks

Early AI systems used pattern matching and expert systems.

What is ML?

Machine learning is the best tool so far to analyze, understand and identify a pattern in the data. One of the main ideas behind Machine Learning is that the computer can be trained to automate tasks that would be exhaustive or impossible for a human being. The clear breach from the traditional analysis is that machine learning can take decisions with minimal human intervention.

Machine learning uses data to feed an algorithm that can understand the relationship between the input and the output. When the machine finished learning, it can predict the value or the class of new data point.

What is Deep Learning?

Deep learning is a computer software that mimics the network of neurons in a brain. It is a subset of machine learning and is called deep learning because it makes use of deep neural networks. The machine uses *different *layers to *learn *from the data. The depth of the model is represented by the number of layers in the model. Deep learning is the new state of the art in term of AI. In deep learning, the learning phase is done through a neural network. A neural network is an architecture where the layers are stacked on top of each other

Machine Learning Process

Imagine you are meant to build a program that recognizes objects. To train the model, you will use a classifier. A classifier uses the features of an object to try identifying the class it belongs to.

In the example, the classifier will be trained to detect if the image is a:

  • What is Artificial intelligence (AI)?
  • What is Machine Learning (ML)?
  • What is Deep Learning (DL)?
  • Machine Learning Process
  • Deep Learning Process
  • Automate Feature Extraction using DL
  • Difference between Machine Learning and Deep Learning
  • When to use ML or DL?

The four objects above are the class the classifier has to recognize. To construct a classifier, you need to have some data as input and assigns a label to it. The algorithm will take these data, find a pattern and then classify it in the corresponding class.

This task is called supervised learning. In supervised learning, the training data you feed to the algorithm includes a label.

Training an algorithm requires to follow a few standard steps:

  • What is Artificial intelligence (AI)?
  • What is Machine Learning (ML)?
  • What is Deep Learning (DL)?
  • Machine Learning Process
  • Deep Learning Process
  • Automate Feature Extraction using DL
  • Difference between Machine Learning and Deep Learning
  • When to use ML or DL?

The first step is necessary, choosing the right data will make the algorithm success or a failure. The data you choose to train the model is called a feature. In the object example, the features are the pixels of the images.

Each image is a row in the data while each pixel is a column. If your image is a 28x28 size, the dataset contains 784 columns (28x28). In the picture below, each picture has been transformed into a feature vector. The label tells the computer what object is in the image.

The objective is to use these training data to classify the type of object. The first step consists of creating the feature columns. Then, the second step involves choosing an algorithm to train the model. When the training is done, the model will predict what picture corresponds to what object.

After that, it is easy to use the model to predict new images. For each new image feeds into the model, the machine will predict the class it belongs to. For example, an entirely new image without a label is going through the model. For a human being, it is trivial to visualize the image as a car. The machine uses its previous knowledge to predict as well the image is a car.

Deep Learning Process

In deep learning, the *learning *phase is done through a neural network. A neural network is an architecture where the layers are stacked on top of each other.

Consider the same image example above. The training set would be fed to a neural network

Each input goes into a neuron and is multiplied by a weight. The result of the multiplication flows to the next layer and become the input. This process is repeated for each layer of the network. The final layer is named the output layer; it provides an actual value for the regression task and a probability of each class for the classification task. The neural network uses a mathematical algorithm to update the weights of all the neurons. The neural network is fully trained when the value of the weights gives an output close to the reality. For instance, a well-trained neural network can recognize the object on a picture with higher accuracy than the traditional neural net.

Automate Feature Extraction using DL

A dataset can contain a dozen to hundreds of features. The system will learn from the relevance of these features. However, not all features are meaningful for the algorithm. A crucial part of machine learning is to find a relevant set of features to make the system learns something.

One way to perform this part in machine learning is to use feature extraction. Feature extraction combines existing features to create a more relevant set of features. It can be done with PCA, T-SNE or any other dimensionality reduction algorithms.

For example, an image processing, the practitioner needs to extract the feature manually in the image like the eyes, the nose, lips and so on. Those extracted features are feed to the classification model.

Deep learning solves this issue, especially for a convolutional neural network. The first layer of a neural network will learn small details from the picture; the next layers will combine the previous knowledge to make more complex information. In the convolutional neural network, the feature extraction is done with the use of the filter. The network applies a filter to the picture to see if there is a match, i.e., the shape of the feature is identical to a part of the image. If there is a match, the network will use this filter. The process of feature extraction is therefore done automatically.

Difference between Machine Learning and Deep Learning

When to use ML or DL?

In the table below, we summarize ***the difference between machine learning and deep learning. ***

With machine learning, you need fewer data to train the algorithm than deep learning. Deep learning requires an extensive and diverse set of data to identify the underlying structure. Besides, machine learning provides a faster-trained model. Most advanced deep learning architecture can take days to a week to train. The advantage of deep learning over machine learning is it is highly accurate. You do not need to understand what features are the best representation of the data; the neural network learned how to select critical features. In machine learning, you need to choose for yourself what features to include in the model.


Artificial intelligence is imparting a cognitive ability to a machine. Early AI systems used pattern matching and expert systems.

The idea behind machine learning is that the machine can learn without human intervention. The machine needs to find a way to learn how to solve a task given the data.

Deep learning is the breakthrough in the field of artificial intelligence. When there is enough data to train on, **deep learning **achieves impressive results, especially for image recognition and text translation. The main reason is the feature extraction is done automatically in the different layers of the network.