Learn the Difference between the most popular Buzzwords in today's tech. World — AI, Machine Learning and Deep Learning

**In this article, we are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning.**

**Furthermore, we will address the question of why Deep Learning as a young emerging field is far superior to traditional Machine Learning.**

Artificial Intelligence, Machine Learning, and Deep Learning are popular buzzwords that everyone seems to use nowadays.

But still, there is a big misconception among many people about the meaning of these terms.

In the worst case, one may think that these terms describe the same thing — which is simply false.

A large number of companies claim nowadays to incorporate some kind of “ *Artificial Intelligence*” (AI) in their applications or services.

But artificial intelligence is only a broader term that describes applications when a machine mimics “ *cognitive* “ functions that humans associate with other human minds, such as “learning” and “problem-solving”.

On a lower level, an AI can be only a programmed rule that determines the machine to behave in a certain way in certain situations. So basically Artificial Intelligence can be nothing more than just a bunch of *if-else* statements.

An *if-else* statement is a simple rule explicitly programmed by a human. Consider a very abstract, simple example of a robot who is moving on a road. A possible programmed rule for that robot could look as follows:

Instead, when speaking of Artificial Intelligence it's only worthwhile to consider two different approaches: **Machine Learning** and **Deep Learning**. Both are subfields of Artificial Intelligence

Now that we now better understand what Artificial Intelligence means we can take a closer look at Machine Learning and Deep Learning and make a clearer distinguishment between these two.

Machine Learning incorporates “ *classical*” algorithms for various kinds of tasks such as clustering, regression or classification. **Machine Learning algorithms must be trained on data**. The more data you provide to your algorithm, the better it gets.

The “training” part of a Machine Learning model means that this model tries to optimize along a certain dimension. In other words, the Machine Learning models try to minimize the error between their predictions and the actual ground truth values.

For this we must define a so-called error function, also called a loss-function or an objective function … because after all the model has an objective. This objective could be for example classification of data into different categories (e.g. cat and dog pictures) or prediction of the expected price of a stock in the near future.

When someone says they are working with a machine-learning algorithm, you can get to the gist of its value by asking: What’s the objective function?

**At this point, you may ask: How do we minimize the error?**

One way would be to compare the prediction of the model with the ground truth value and adjust the parameters of the model in a way so that next time, the error between these two values is smaller. This is repeated again and again and again.

Thousands and millions of times, until the parameters of the model that determine the predictions are so good, that the difference between the predictions of the model and the ground truth labels are as small as possible.

In short machine learning models are optimization algorithms. If you tune them right, they minimize their error by guessing and guessing and guessing again.

Machine Learning is a pretty old field and incorporates methods and algorithms that **have been around for dozens of years**, some of them since as early as the sixties.

Some known methods of classification and prediction are the *Naive Bayes Classifier* and the *Support Vector Machines*. In addition to the classification, there are also clustering algorithms such as the well-known *K-Means* and *tree-based clustering*. To reduce the dimensionality of data to gain more insights about it’ nature methods such as Principal component analysis and *tSNE* are used.

Deep Learning, on the other hand, is a very young field of Artificial Intelligence **that is powered by artificial neural networks**.

It can be viewed again as a subfield of Machine Learning since Deep Learning algorithms also require data in order to learn to solve tasks. Although methods of Deep Learning are able to perform the same tasks as classic Machine Learning algorithms, it is not the other way round.

Artificial neural networks have unique capabilities that enable **Deep Learning models to solve tasks that Machine Learning models could never solve**.

All recent advances in intelligence are due to Deep Learning. Without Deep Learning we would not have self-driving cars, chatbots or personal assistants like *Alexa* and *Siri*. *Google Translate* app would remain primitive and Netflix would have no idea which movies or TV series we like or dislike.

We can even go so far as to say that the **new industrial revolution is driven by artificial neural networks and Deep Learning**. This is the best and closest approach to true machine intelligence we have so far. The reason is that Deep Learning has two major advantages over Machine Learning.

The first advantage is the needlessness of *Feature Extraction*. What do I mean by this?

Well if you want to use a Machine Learning model to determine whether a given picture shows a car or not, we as humans, must first program the unique features of a car (shape, size, windows, wheels etc.) into the algorithm. This way the algorithm would know what to look after in the given pictures.

In the case of a Deep Learning model, is step is completely unnecessary. The model would recognize all the unique characteristics of a car by itself and make correct predictions.

In fact, the needlessness of feature extraction applies to any other task for a deep learning model. You simply give the neural network the raw data, the rest is done by the model. While for a machine learning model, you would need to perform additional steps, such as the already mentioned extraction of the features of the given data.

The second huge advantage of Deep Learning and a key part in understanding why it’s becoming so popular is that it’s powered by massive amounts of data. The “Big Data Era” of technology will provide huge amounts of opportunities for new innovations in deep learning. To quote Andrew Ng, the chief scientist of China’s major search engine Baidu and one of the leaders of the Google Brain Project:

“ *The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.* “

Deep Learning models tend to increase their accuracy with the increasing amount of training data, where’s traditional machine learning models such as SVM and Naive Bayes classifier stop improving after a saturation point.

Artificial Intelligence vs. Machine Learning vs. Deep Learning. We are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning

We are going to discuss we difference between Artificial Intelligence, Machine Learning, and Deep Learning

Furthermore, we will address the question of why Deep Learning as a young emerging field is far superior to traditional Machine Learning

Artificial Intelligence, Machine Learning, and Deep Learning are popular buzzwords that everyone seems to use nowadays.

But still, there is a big misconception among many people about the meaning of these terms.

In the worst case, one may think that these terms describe the same thing — which is simply false.

A large number of companies claim nowadays to incorporate some kind of “ *Artificial Intelligence*” (AI) in their applications or services.

But artificial intelligence is only a broader term that describes applications when a machine mimics “ *cognitive* “ functions that humans associate with other human minds, such as “learning” and “problem-solving”.

On a lower level, an AI can be only a programmed rule that determines the machine to behave in a certain way in certain situations. So basically Artificial Intelligence can be nothing more than just a bunch of *if-else* statements.

An *if-else* statement is a simple rule explicitly programmed by a human. Consider a very abstract, simple example of a robot who is moving on a road. A possible programmed rule for that robot could look as follows:

Instead, when speaking of Artificial Intelligence it's only worthwhile to consider two different approaches: **Machine Learning** and **Deep Learning**. Both are subfields of Artificial Intelligence

Now that we now better understand what Artificial Intelligence means we can take a closer look at Machine Learning and Deep Learning and make a clearer distinguishment between these two.

Machine Learning incorporates “ *classical*” algorithms for various kinds of tasks such as clustering, regression or classification. **Machine Learning algorithms must be trained on data**. The more data you provide to your algorithm, the better it gets.

The “training” part of a Machine Learning model means that this model tries to optimize along a certain dimension. In other words, the Machine Learning models try to minimize the error between their predictions and the actual ground truth values.

For this we must define a so-called error function, also called a loss-function or an objective function … because after all the model has an objective. This objective could be for example classification of data into different categories (e.g. cat and dog pictures) or prediction of the expected price of a stock in the near future.

When someone says they are working with a machine-learning algorithm, you can get to the gist of its value by asking: What’s the objective function?

**At this point, you may ask: How do we minimize the error?**

One way would be to compare the prediction of the model with the ground truth value and adjust the parameters of the model in a way so that next time, the error between these two values is smaller. This is repeated again and again and again.

Thousands and millions of times, until the parameters of the model that determine the predictions are so good, that the difference between the predictions of the model and the ground truth labels are as small as possible.

In short machine learning models are optimization algorithms. If you tune them right, they minimize their error by guessing and guessing and guessing again.

The basic definition of machine learning is:

*Algorithms that analyze data, learn from it and make informed decisions based on the learned insights.*

Machine learning leads to a variety of automated tasks. It affects virtually every industry — from IT security malware search, to weather forecasting, to stockbrokers looking for cheap trades. Machine learning requires complex math and a lot of coding to finally get the desired functions and results.

Machine learning algorithms need to be trained on large amounts of data.

The more data you provide for your algorithm, the better it gets.

Machine Learning is a pretty old field and incorporates methods and algorithms that **have been around for dozens of years**, some of them since as early as the sixties.

These classic algorithms include algorithms such as the so-called *Naive Bayes Classifier* and the *Support Vector Machines*. Both are often used in the classification of data.

In addition to the classification, there are also cluster analysis algorithms such as the well-known *K-Means* and the *tree-based clustering.* To reduce the dimensionality of data and gain more insight into its nature, machine learning uses methods such as principal component analysis and *tSNE*.

Now let’s focus on the essential thing that is at stake here. On deep learning.

Deep Learning is a very young field of artificial intelligence based on artificial neural networks.

Again, deep learning can be seen as a part of machine learning because deep learning algorithms also need data to learn how to solve problems. Therefore, the terms of machine learning and deep learning are often treated as the same. However, these systems have different capabilities.

Deep Learning uses a multi-layered structure of algorithms called the neural network:

It can be viewed again as a subfield of Machine Learning since Deep Learning algorithms also require data in order to learn to solve tasks. Although methods of Deep Learning are able to perform the same tasks as classic Machine Learning algorithms, it is not the other way round.

Artificial neural networks have unique capabilities that enable **Deep Learning models to solve tasks that Machine Learning models could never solve**.

All recent advances in intelligence are due to Deep Learning. Without Deep Learning we would not have self-driving cars, chatbots or personal assistants like *Alexa* and *Siri*. *Google Translate* app would remain primitive and Netflix would have no idea which movies or TV series we like or dislike.

We can even go so far as to say that the **new industrial revolution is driven by artificial neural networks and Deep Learning**. This is the best and closest approach to true machine intelligence we have so far. The reason is that Deep Learning has two major advantages over Machine Learning.

The first advantage of Deep Learning over machine learning is the needlessness of the so-called Feature Extraction.

Long before deep learning was used, traditional machine learning methods were popular, such as Decision Trees, SVM, Naïve Bayes Classifier and Logistic Regression. These algorithms are also called “flat algorithms”.

Flat means here that these algorithms can not normally be applied directly to the raw data (such as .csv, images, text, etc.). We require a preprocessing step called *Feature Extraction*.

The result of Feature Extraction is an abstract representation of the given raw data that can now be used by these classic machine learning algorithms to perform a task. For example, the classification of the data into several categories or classes.

Feature Extraction is usually pretty complicated and requires detailed knowledge of the problem domain. This step must be adapted, tested and refined over several iterations for optimal results.

On the other side are the artificial neural networks. These do not require the step of feature extraction. The layers are able to learn an implicit representation of the raw data directly on their own.

Here, a more and more abstract and compressed representation of the raw data is produced over several layers of an artificial neural network. This compressed representation of the input data is then used to produce the result. The result can be, for example, the classification of the input data into different classes.

In other words, we can also say that the feature extraction step is already a part of the process that takes place in an artificial neural network. During the training process, this step is also optimized by the neural network to obtain the best possible abstract representation of the input data. This means that the models of deep learning thus require little to no manual effort to perform and optimize the feature extraction process.

For example, if you want to use a machine learning model to determine whether a particular image shows a car or not, we humans first need to identify the unique features of a car (shape, size, windows, wheels, etc.), extract these features and give them to the algorithm as input data. This way, the machine learning algorithm would perform a classification of the image. That is, in machine learning, a programmer must intervene directly in the classification process.

In the case of a deep learning model, the feature extraction step is completely unnecessary. The model would recognize these unique characteristics of a car and make correct predictions- completely without the help of a human.

In fact, this applies to every other task you’ll ever do with neural networks.

They just give the raw data to the neural network, the rest is done by the model.

The second huge advantage of Deep Learning and a key part in understanding why it’s becoming so popular is that it’s powered by massive amounts of data. The “Big Data Era” of technology will provide huge amounts of opportunities for new innovations in deep learning. To quote Andrew Ng, the chief scientist of China’s major search engine Baidu and one of the leaders of the Google Brain Project:

“ *The analogy to deep learning is that the rocket engine is the deep learning models and the fuel is the huge amounts of data we can feed to these algorithms.* “

Deep Learning models scale better with a larger amount of data

Deep Learning models tend to increase their accuracy with the increasing amount of training data, where’s traditional machine learning models such as SVM and Naive Bayes classifier stop improving after a saturation point.

I am the founder of *DeepLearning Academy*, an advanced Deep Learning education platform. We provide practical state-of-the-art Deep Learning education and mentoring to professionals and beginners.

Among our things we just released a free Introductory Course on Deep Learning with TensorFlow, where you can learn how to implement Neural Networks from Scratch for various use-cases using TensorFlow.

If you are interested in this topic, feel free to check it out ;)

What is Artificial Intelligence (AI)? AI is the ability of a machine to think like human, learn and perform tasks like a human. Know the future of AI, Examples of AI and who provides the course of Artificial Intelligence?

US and China are massively investing in Artificial Intelligence which create a promising career in the field. One of the first steps to a successful artificial Intelligence career is to learn the basics around the domain. Articles and Guides are your opening friends towards a successful AI Career. Read on to know more.

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Big Data

Downloadable PDF of Best AI Cheat Sheets in Super High DefinitionLet’s begin.

Cheat Sheets for AI, Neural Networks, Machine Learning, Deep Learning & Data Science in HD

Neural Networks Cheat Sheets

Neural Networks Basics Cheat Sheet

An Artificial Neuron Network (ANN), popularly known as Neural Network is a computational model based on the structure and functions of biological neural networks. It is like an artificial human nervous system for receiving, processing, and transmitting information in terms of Computer Science.

- Input Layer (All the inputs are fed in the model through this layer)
- Hidden Layers (There can be more than one hidden layers which are used for processing the inputs received from the input layers)
- Output Layer (The data after processing is made available at the output layer)

Neural Networks Graphs Cheat Sheet

Graph data can be used with a lot of learning tasks contain a lot rich relation data among elements. For example, modeling physics system, predicting protein interface, and classifying diseases require that a model learns from graph inputs. Graph reasoning models can also be used for learning from non-structural data like texts and images and reasoning on extracted structures.

Machine Learning Cheat Sheets

Machine Learning with Emojis Cheat Sheet

Scikit Learn Cheat Sheet

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines is a simple and efficient tools for data mining and data analysis. It’s built on NumPy, SciPy, and matplotlib an open source, commercially usable — BSD license

Scikit-learn Algorithm Cheat Sheet

This machine learning cheat sheet will help you find the right estimator for the job which is the most difficult part. The flowchart will help you check the documentation and rough guide of each estimator that will help you to know more about the problems and how to solve it.

If you like these cheat sheets, you can let me know here.### Machine Learning: Scikit-Learn Algorythm for Azure Machine Learning Studios

Scikit-Learn Algorithm for Azure Machine Learning Studios Cheat Sheet

Data Science with Python Cheat Sheets

TensorFlow Cheat Sheet

TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.

If you like these cheat sheets, you can let me know here.### Data Science: Python Basics Cheat Sheet

Python Basics Cheat Sheet

Python is one of the most popular data science tool due to its low and gradual learning curve and the fact that it is a fully fledged programming language.

PySpark RDD Basics Cheat Sheet

“At a high level, every Spark application consists of a *driver program* that runs the user’s `main`

function and executes various *parallel operations* on a cluster. The main abstraction Spark provides is a *resilient distributed dataset* (RDD), which is a collection of elements partitioned across the nodes of the cluster that can be operated on in parallel. RDDs are created by starting with a file in the Hadoop file system (or any other Hadoop-supported file system), or an existing Scala collection in the driver program, and transforming it. Users may also ask Spark to *persist* an RDD in memory, allowing it to be reused efficiently across parallel operations. Finally, RDDs automatically recover from node failures.” via Spark.Aparche.Org

NumPy Basics Cheat Sheet

NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

***If you like these cheat sheets, you can let me know *****here.**

Bokeh Cheat Sheet

“Bokeh is an interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of versatile graphics, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications.” from Bokeh.Pydata.com

Karas Cheat Sheet

Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML. Designed to enable fast experimentation with deep neural networks, it focuses on being user-friendly, modular, and extensible.

Padas Basics Cheat Sheet

Pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. It is free software released under the three-clause BSD license.

If you like these cheat sheets, you can let me know here.### Pandas Cheat Sheet: Data Wrangling in Python

Pandas Cheat Sheet: Data Wrangling in Python

The term “data wrangler” is starting to infiltrate pop culture. In the 2017 movie Kong: Skull Island, one of the characters, played by actor Marc Evan Jackson is introduced as “Steve Woodward, our data wrangler”.

Data Wrangling with Pandas Cheat Sheet

- Although many fundamental data processing functions exist in R, they have been a bit convoluted to date and have lacked consistent coding and the ability to easily
*flow*together → leads to difficult-to-read nested functions and/or*choppy*code. - R Studio is driving a lot of new packages to collate data management tasks and better integrate them with other analysis activities → led by Hadley Wickham & the R Studio team → Garrett Grolemund, Winston Chang, Yihui Xie among others.
- As a result, a lot of data processing tasks are becoming packaged in more cohesive and consistent ways → leads to:
- More efficient code
- Easier to remember syntax
- Easier to read syntax” via Rstudios

Data Wrangling with ddyr and tidyr Cheat Sheet

If you like these cheat sheets, you can let me know here.### Data Science: Scipy Linear Algebra

Scipy Linear Algebra Cheat Sheet

SciPy builds on the NumPy array object and is part of the NumPy stack which includes tools like Matplotlib, pandas and SymPy, and an expanding set of scientific computing libraries. This NumPy stack has similar users to other applications such as MATLAB, GNU Octave, and Scilab. The NumPy stack is also sometimes referred to as the SciPy stack.[3]

Matplotlib Cheat Sheet

Matplotlib is a plotting library for the Python programming language and its numerical mathematics extension NumPy. It provides an object-oriented APIfor embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK+. There is also a procedural “pylab” interface based on a state machine (like OpenGL), designed to closely resemble that of MATLAB, though its use is discouraged. SciPy makes use of matplotlib.

Pyplot is a matplotlib module which provides a MATLAB-like interface matplotlib is designed to be as usable as MATLAB, with the ability to use Python, with the advantage that it is free.

Data Visualization with ggplot2 Cheat Sheet

Big-O Cheat Sheet

Special thanks to DataCamp, Asimov Institute, RStudios and the open source community for their content contributions. You can see originals here:

Big-O Algorithm Cheat Sheet: http://bigocheatsheet.com/

Bokeh Cheat Sheet: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Bokeh_Cheat_Sheet.pdf

Data Science Cheat Sheet: https://www.datacamp.com/community/tutorials/python-data-science-cheat-sheet-basics

Data Wrangling Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf

Data Wrangling: https://en.wikipedia.org/wiki/Data_wrangling

Ggplot Cheat Sheet: https://www.rstudio.com/wp-content/uploads/2015/03/ggplot2-cheatsheet.pdf

Keras Cheat Sheet: https://www.datacamp.com/community/blog/keras-cheat-sheet#gs.DRKeNMs

Keras: https://en.wikipedia.org/wiki/Keras

Machine Learning Cheat Sheet: https://ai.icymi.email/new-machinelearning-cheat-sheet-by-emily-barry-abdsc/

Machine Learning Cheat Sheet: https://docs.microsoft.com/en-in/azure/machine-learning/machine-learning-algorithm-cheat-sheet

ML Cheat Sheet:: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html

Matplotlib Cheat Sheet: https://www.datacamp.com/community/blog/python-matplotlib-cheat-sheet#gs.uEKySpY

Matpotlib: https://en.wikipedia.org/wiki/Matplotlib

Neural Networks Cheat Sheet: http://www.asimovinstitute.org/neural-network-zoo/

Neural Networks Graph Cheat Sheet: http://www.asimovinstitute.org/blog/

Neural Networks: https://www.quora.com/Where-can-find-a-cheat-sheet-for-neural-network

Numpy Cheat Sheet: https://www.datacamp.com/community/blog/python-numpy-cheat-sheet#gs.AK5ZBgE

NumPy: https://en.wikipedia.org/wiki/NumPy

Pandas Cheat Sheet: https://www.datacamp.com/community/blog/python-pandas-cheat-sheet#gs.oundfxM

Pandas: https://en.wikipedia.org/wiki/Pandas_(software)

Pandas Cheat Sheet: https://www.datacamp.com/community/blog/pandas-cheat-sheet-python#gs.HPFoRIc

Pyspark Cheat Sheet: https://www.datacamp.com/community/blog/pyspark-cheat-sheet-python#gs.L=J1zxQ

Scikit Cheat Sheet: https://www.datacamp.com/community/blog/scikit-learn-cheat-sheet

Scikit-learn: https://en.wikipedia.org/wiki/Scikit-learn

Scikit-learn Cheat Sheet: http://peekaboo-vision.blogspot.com/2013/01/machine-learning-cheat-sheet-for-scikit.html

Scipy Cheat Sheet: https://www.datacamp.com/community/blog/python-scipy-cheat-sheet#gs.JDSg3OI

SciPy: https://en.wikipedia.org/wiki/SciPy

TesorFlow Cheat Sheet: https://www.altoros.com/tensorflow-cheat-sheet.html