Trending February 2024 # Top 10 Essential Prerequisites For Deep Learning Projects # Suggested March 2024 # Top 3 Popular

You are reading the article Top 10 Essential Prerequisites For Deep Learning Projects updated in February 2024 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested March 2024 Top 10 Essential Prerequisites For Deep Learning Projects

The deep learning projects are used across industries ranging from medical to e-commerce

Deep learning is clearly the technology of the future and is one of the most sought-after innovations of our day. You should be aware of the requirements for DL if you’re interested in learning it. You can choose a better job path with the aid of deep learning projects prerequisite.

Deep learning is an interdisciplinary area of computer science and mathematics with the goal of teaching to carry out cognitive tasks in a manner that is similar to that of humans. Prerequisites for deep learning projects are a process through which computers collect input data, and study or analyze it. Different methods are used by deep learning prerequisites systems to automatically identify patterns in datasets that may contain structured data, quantitative data, textual data, visual data, etc. We’ll talk about the top requirements for deep learning projects in this section to help you prepare for learning its more complex ideas.

1. Programming

Deep learning requires programming as a core component. Deep learning demands the use of a programming language. Python or R are the programming languages of choice for deep learning experts due to their functionality and efficiency. You must study programming and become proficient in one of these two well-known programming languages before you can study the numerous deep learning topics.

2. Statistics

The study of utilizing data and its visualization is known as statistics. It aids in extracting information from your raw data. Data science and the related sciences depend heavily on statistics. You would need to apply statistics to acquire insights from data as a deep learning specialist.

3. Calculus

The foundation of many machine learning algorithms is calculus. Therefore, studying calculus is a requirement for deep learning. You will create models using deep learning based on the features found in your data. You can use such properties and create the model as necessary with the aid of calculus.

4. Linear Algebra

Linear algebra is most likely one of the most crucial requirements for deep learning. Matrix, vector, and linear equations are all topics covered by linear algebra. It focuses on how linear equations are represented in vector spaces. You may design many models (classification, regression, etc.) with the aid of linear algebra, which is also a fundamental building block for many deep-learning ideas.

5. Probability

Mathematics’ field of probability focuses on using numerical data to express how likely or valid an occurrence is to occur. Any event’s probability can range from 0 to 1, with 0 denoting impossibility and 1 denoting complete certainty.

6. Data Science

Data analysis and use are the focus of the field of data science. You must be knowledgeable with a variety of data science principles to construct models that manage data as a deep learning specialist. Understanding deep learning will assist you in using data to achieve the desired results, but mastering data science is a prerequisite for applying deep learning.

7. Work on Projects

While mastering these topics will aid in the development of a solid foundation, you will also need to work on deep learning projects to ensure that you fully comprehend everything. You can apply what you’ve learned and identify your weak areas with the aid of projects. You can easily find a project that interests you because deep learning has applications in many different fields.

8. Neural Networks

The word “neuron,” which is used to describe a single nerve cell, is where the word “neural” originates. That’s correct; a neural network is essentially a network of neurons that carry out routine tasks for us.

A significant portion of the issues we encounter daily is related to pattern recognition, object detection, and intelligence. The reality is that these reactions are challenging to automate even if they are carried out with such simplicity that we don’t even notice it.

9. Clustering Algorithms

The clustering problem is resolved with the most straightforward unsupervised learning approach. The K-means method divides n observations into k clusters, with each observation belonging to the cluster represented by the nearest mean.

10. Regression

You're reading Top 10 Essential Prerequisites For Deep Learning Projects

Top 10 Deep Learning Projects For Engineering Students In 2023

If you are one of them wanting to start a career in deep learning, then you must read these top deep 10 learning projects

Deep learning

is a domain with diverse technologies such as tablets and computers that can learn based on programming and other data. Deep learning is emerging as a futuristic concept that can meet the requirements of people. When we take a look at the speech recognition technology and virtual assistants, they are run using

machine learning


deep learning technologies

. If you are one of them wanting to start a career in deep learning, then you must read this article as this article features current ideas for your upcoming deep learning project. Here is the list of the top 10 deep learning projects to know about in 2023.


Due to their skillful handling of a profusion of customer queries and messages without any issue, Chatbots play a significant role for industries. They are designed to lessen the customer service workload by automating the hefty part of the process. Nonetheless, chatbots execute this by utilizing their promising methods supported by technologies like machine learning, artificial intelligence, and deep learning. Therefore, creating a chatbot for your final deep learning project will be a great idea.

Forest Fire Prediction

Creating a forest fire prediction system is one of the best deep learning projects and it will be another considerable utilization of the abilities provided by deep learning. Forest fire is an uncontrolled fire in a forest causing a hefty amount of damage to not only nature but the animal habitat, and human property as well. To control the chaotic nature of forest fires and even predict them, you can create a deep learning project utilizing k-means massing to comprehend major fire hotspots and their intensity.

Digit Recognition System

This project involves developing a digit recognition system that can classify digits based on the set tenets. The project aims to create a recognition system that can classify digits ranging from 0 to 9 using a combination of shallow network and deep neural network and by implementing logistic regression. Softmax Regression or Multinomial Logistic Regression is the ideal choice for this project. Since this technique is a generalization of logistic regression, it is apt for multi-class classification, assuming that all the classes are mutually exclusive.

Image Caption Generator Project in Python

This is one of the most interesting deep learning projects. It is easy for humans to describe what is in an image but for computers, an image is just a bunch of numbers that represent the color value of each pixel. This project utilizes deep learning methods where you implement a convolutional neural network (CNN) with a Recurrent Neural Network (LSTM) to build the image caption generator.

Traffic Signs Recognition

Traffic signs and rules are crucial that every driver must obey to prevent accidents. To follow the rule, one must first understand what the traffic sign looks like. In the Traffic signs recognition project, you will learn how a program can identify the type of traffic sign by taking an image as input. For a final-year engineering student, it is one of the best deep learning projects to try.

Credit Card Fraud Detection

With the increase in online transactions, credit card frauds have also increased. Banks are trying to handle this issue using deep learning techniques. In this deep learning project, you can use python to create a classification problem to detect credit card fraud by analyzing the previously available data. 

Customer Segmentation

This is one of the most popular deep learning projects every student should try. Before running any campaign companies create different groups of customers. Customer segmentation is a popular application of unsupervised learning. Using clustering, companies identify segments of customers to target the potential user base.

Movie Recommendation System

In this deep learning project, you have to utilize R to perform a movie recommendation through technologies like Machine Learning and

Artificial Intelligence

. A recommendation system sends out suggestions to users through a filtering process based on other users’ preferences and browsing history. If A and B like Home Alone and B likes Mean Girls, it can be suggested to A – they might like it too. This keeps customers engaged with the platform.

Visual tracking system

A visual tracking system is designed to track and locate moving object(s) in a given time frame via a camera. It is a handy tool that has numerous applications such as security and surveillance, medical imaging, augmented reality, traffic control, video editing and communication, and human-computer interaction.

Drowsiness detection system

Machine Learning With Python: Top 10 Projects For Freshers To Pursue

With source program in Python, check these top 10 Machine Learning with Python projects for freshers

Machine learning is same as how it sounds. It is the idea that multiple types of technology, such as computers and tablets, can learn something from programming and other data. It appears to be an abstract idea. However, this type of technology is used by several people each day. Speech identification is a good example of this. Virtual assistants including Siri and Alexa use technology to present messages, answer questions and respond to instructions.

In this tutorial, you will find top 10 machine learning project ideas for freshers, intermediates, and professionals to gain real-world experience of this developing technology in 2023. These machine learning project ideas will assist you in learning all the practicalities that you want to with prevailing in your profession and to make you employable in the business.

1.Movie Recommendations from Movielens Dataset

Many individuals currently use technology to stream TV and film shows. Although choosing the next stream to watch can be complex and time-consuming, recommendations are generally built based on customer habits and history. This is accomplished by machine learning and is a great and simple task for beginners to tackle. Starting developers can learn by writing program utilizing one of the two languages, Python and R, and using data from Movielens Dataset. Movielens has over 6000 people make it currently involves more than 1 million film valuations of 3900 movies.

2.Music Recommendation System ML Project

This is one of the most popular machine learning projects and can be used across multiple domains. You should be very familiar with a recommendation system if you have utilized any E-commerce site or Movie/Music website. In some E-commerce sites such as Amazon, at the time of checkout, the system will recommend elements that can be added to the cart.

3.BigMart Sales Prediction ML Project

As a fresher, you should work on multiple machine learning projects ideas to expand your skillset. Therefore, we have added a project that will learn unsupervised machine learning algorithms to us by utilizing the business dataset of a grocery supermarket store.


This open-source artificial intelligence library is a best place for fresher to enhance their machine learning skills. With TensorFlow, they can use the library to make data flow graphs, projects utilizing Java, and an array of applications. It also involves APIs for Java.

5.Iris Classification

This is one of the simplest machine learning projects with Iris Flowers being the elementary machine learning datasets in classification writing. This machine learning problem is defined as the “Hello World” of machine learning. The dataset has numeric characteristics and ML freshers need to figure out how to load and handle information. The iris dataset is small which simply fits into the memory and does not need any specific transformations or scaling, to start with.

6.Sales Forecasting with Walmart

While predicting future sales efficiently may not be applicable, businesses can come near to machine learning. For example, Walmart supports datasets for 98 products across 45 outlets so programmer can access data on weekly sales by locations and branch. The main objective of this project is to create better data-driven decisions in channel optimization and stock planning. 

7.Stock Price Predictions

It is same as sales forecasting, forecasts of prices for stocks can be changed from the data of previous prices, indexes of volatility, and different fundamental indicators. For freshers, it is possible to start with a concept like this and create use of stock industry data to create predictions over the recent months. It is a best way to get familiar with making predictions utilizing huge data sets.

8.Breast Cancer Prediction

This project uses machine learning to make data that helps decide whether the tumour in the breast is mild or deadly. There are multiple factors considered, including the thickness of the lump, the number of bare nuclei, and mitosis. It is also a best method for a new expert in machine learning to get familiar with using R.

9.Sorting of Specific Tweets on Twitter

In an optimal world, quickly filtering tweets with definite words and elements would be best. There’s a huge fresher-level machine-learning project which enables programmers to develop an algorithm that takes scraped tweets processed by an artificial language processor to recognize which tweets are more likely to be associated to specific topics or talk about specific individuals, etc.

10.Making Handwritten Documents Digital Versions

Deep Learning For Image Super

This article was published as a part of the Data Science Blogathon


(SR) is the process of recovering high-resolution (HR) images from low-resolution (LR) images. It is an important class of image processing techniques in computer vision and image processing and enjoys a wide range of real-world applications, such as medical imaging, satellite imaging, surveillance and security, astronomical imaging, amongst others.


Image sup -resolution (SR) problem, particularly single image super-resolution (SISR), has gained a lot of attention in the research community. SISR aims to reconstruct a high-resolution image ISR from a single low-resolution image ILR. Generally, the relationship between ILR and the original high-resolution image IHR can vary depending on the situation. Many studies assume that ILR is a bicubic downsampled version of IHR, but other degrading factors such as blur, decimation, or noise can also be considered for practical applications.

In this article, we would be focusing on supervised learning methods for super-resolution tasks. By using HR images as target and LR images as input, we can treat this problem as a supervised learning problem.

Exhaustive table of topics in Supervised Image Super-Resolution

Upsampling Methods

Before understanding the rest of the theory behind the super-resolution, we need to understand upsampling (Increasing the spatial resolution of images or simply increasing the number of pixel rows/columns or both in the image) and its various methods.

1. Interpolation-based methods – Image interpolation (image scaling), refers to resizing digital images and is widely used by image-related applications. The traditional methods include nearest-neighbor interpolation, linear, bilinear, bicubic interpolation, etc.

Nearest-neighbor interpolation with the scale of 2

Nearest-neighbor Interpolation – The nearest-neighbor interpolation is a simple and intuitive algorithm. It selects the value of the nearest pixel for each position to be interpolated regardless of any other pixels.

Bilinear Interpolation – The bilinear interpolation (BLI) first performs linear interpolation on one axis of the image and then performs on the other axis. Since it results in a quadratic interpolation with a receptive field-sized 2 × 2, it shows much better performance than nearest-neighbor interpolation while keeping a relatively fast speed.

Bicubic Interpolation – Similarly, the bicubic interpolation (BCI) performs cubic interpolation on each of the two axes Compared to BLI, the BCI takes 4 × 4 pixels into account, and results in smoother results with fewer artifacts but much lower speed. Refer to this for a detailed discussion.

Shortcomings – Interpolation-based methods often introduce some side effects such as computational complexity, noise amplification, blurring results, etc.

2. Learning-based upsampling – To overcome the shortcomings of interpolation-based methods and learn upsampling in an end-to-end manner, transposed convolution layer and sub-pixel layer are introduced into the SR field.

and the green boxes indicate the kernel and the convolution output.

Transposed convolution: layer, a.k.a. deconvolution layer, tries to perform transformation opposite a normal convolution, i.e., predicting the possible input based on feature maps sized like convolution output. Specifically, it increases the image resolution by expanding the image by inserting zeros and performing convolution.

Sub-pixel layer – The blue boxes denote the input and the boxes with other colors indicate different convolution operations and different output feature maps.

s2 times channels, where s is the scaling factor. Assuming the input size is h × w × c, the output size will be h×w×s2c. After that, the reshaping operation is performed to produce outputs with size sh × sw × c

Super-resolution Frameworks

Since image super-resolution is an ill-posed problem, how to perform upsampling (i.e., generating HR output from LR input) is the key problem. There are mainly four model frameworks based on the employed upsampling operations and their locations in the model (refer to the table above).

1. Pre-upsampling Super-resolution –

We don’t do a direct mapping of LR images to HR images since it is considered to be a difficult task. We utilize traditional upsampling algorithms to obtain higher resolution images and then refining them using deep neural networks is a straightforward solution. For example – LR images are upsampled to coarse HR images with the desired size using bicubic interpolation. Then deep CNNs are applied to these images for reconstructing high-quality images.

2. Post-upsampling Super-resolution –

To improve the computational efficiency and make full use of deep learning technology to increase resolution automatically, researchers propose to perform most computation in low-dimensional space by replacing the predefined upsampling with end-to-end learnable layers integrated at the end of the models. In the pioneer works of this framework, namely post-upsampling SR, the LR input images are fed into deep CNNs without increasing resolution, and end-to-end learnable upsampling layers are applied at the end of the network.

Learning Strategies

error and producing more realistic and higher-quality results.

Pixelwise L1 loss – Absolute difference between pixels of ground truth HR image and the generated one.

Pixelwise L2 loss – Mean squared difference between pixels of ground truth HR image and the generated one.

Content loss – the content loss is indicated as the Euclidean distance between high-level representations of the output image and the target image. High-level features are obtained by passing through pre-trained CNNs like VGG and ResNet.

Adversarial loss – Based on GAN where we treat the SR model as a generator, and define an extra discriminator to judge whether the input image is generated or not.

PSNR – Peak Signal-to-Noise Ratio (PSNR) is a commonly used objective metric to measure the reconstruction quality of a lossy transformation. PSNR is inversely proportional to the logarithm of the Mean Squared Error (MSE) between the ground truth image and the generated image.

In MSE, I is a noise-free m×n monochrome image (ground truth)  and K is the generated image (noisy approximation). In PSNR, MAXI represents the maximum possible pixel value of the image.

Network Design

Various network designs in super-resolution architecture

Enough of the basics! Let’s discuss some of the state-of-art super-resolution methods –

Super-Resolution methods

Super-Resolution Generative Adversarial Network (SRGAN) – Uses the idea of GAN for super-resolution task i.e. generator will try to produce an image from noise which will be judged by the discriminator. Both will keep training so that generator can generate images that can match the true training data.

Architecture of Generative Adversarial Network

There are various ways for super-resolution but there is a problem – how can we recover finer texture details from a low-resolution image so that the image is not distorted?

The results have high PSNR means have high-quality results but they are often lacking high-frequency details.

Check the original papers for detailed information.

Steps –

1. We process the HR (high-resolution images) to get downsampled LR images. Now we have HR and LR images for the training dataset.

2. We pass LR images through a generator that upsamples and gives SR images.

3. We use the discriminator to distinguish HR image and backpropagate GAN loss to train discriminator and generator.

Network architecture of SRGAN


Key features of the method – 

Post upsampling type of framework

Subpixel layer for upsampling

Contains residual blocks

Uses Perceptual loss

Original code of SRGAN

conventional residual networks.

Check the original papers for detailed information.

Some of the key features of the methods – 

Residual blocks – SRGAN successfully applied the ResNet architecture to the super-resolution problem with SRResNet, they further improved the performance by employing a better ResNet structure. In the proposed architecture –

Comparison of the residual blocks

They removed the batch normalization layers from the network as in SRResNets. Since batch normalization layers normalize the features, they get rid of range flexibility from networks by normalizing the features, it is better to remove them.

The architecture of EDSR, MDSR

In MDSR, they proposed a multiscale architecture that shares most of the parameters on different scales. The proposed multiscale model uses significantly fewer parameters than multiple single-scale models but shows comparable performance.

Original code of the methods

So now we have come to the end of the blog! To learn about super-resolution, refer to these survey papers.

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Top 10 Essential Go Interview Questions And Answers {Updated For 2023}

Introduction To Go Interview Questions And Answers

Web development, programming languages, Software testing & others

It supports something called the environment, adapting patterns.

Go as fast as far as its compilation time is concerned.

It has Built concurrency support and lightweight processes via goroutines, channels, and select statements.

Go supports Interfaces and Type embedding.

Now, if you are looking for a job that is related to Go, then you need to prepare for the 2023 Go Interview Questions. Every interview is different from the different job profiles, but still, to clear the interview, you need to have a good and clear knowledge of Go. Here, we have prepared the important Go Interview Questions and Answers, which will help you succeed in your interview.

Below are the 10 important 2023 Go Interview Questions and Answers that are frequently asked in an interview. These questions are divided into parts are as follows:

Part 1 – Go Interview Questions (Basic)

Let us now have a look at the basic Interview Questions and Answers.

Q1.What is the Go language, and what are its benefits?

Benefits: Mentioned in bullets point above in the introduction section.

Q2.Explain what do you understand by static type variable declaration in the Go language?

Static type variable declaration provides confidence to the compiler that there is nothing but at least one variable that exists with the given name of its declared type. This helps the compiler proceeds for further compilation without requiring a variable’s complete detail. Usually, the meaning of a variable in Go is at the time of compilation. At the time of linking of the program, the Go compiler needs a formal variable declaration.

Q3.What are the methods in Go?

Go language supports special types of functions. These are called methods. In method declaration syntax, something called a “receiver” is present, which is used to represent the function container. The above-defined receiver can be used to call a function using an operator who is denoted by “.”.

Q4.Explain what a string is literal?

There are two forms of a string literal in Go language: –

Raw string literals type: In this case, the value of such literals are character sequences which are between backquotes ‘‘.  The value of a string literal is the string consisting of the uninterrupted character between quotes.

Interpreted string literals type: It is denoted between double quotes, which are the standard syntax. The content between the double quotes that may not contain newline characters usually forms the literal value in this case.

Q5. Explain what a package in the Go program is?

All GO programs are made up of nothing but packages. The program that starts running in a package is called the main.

Part 2 – Go Interview Questions (Advanced) Q6. Define what you understand from a workspace in GO Language?

Typically, a workspace is what keeps all of the Go source code. A workspace is a directory on your system hierarchy that contains three additional directories at the root position.

src – this contains GO source files organized into packages

pkg – this contains package objects and

bin – this contains executable commands

src, pkg and bin are folder structure which organizes the source code.

GO compiles very fast.

Go has concurrency support.

Functions are Go’s first-class objects.

GO supports garbage collection.

Strings and Maps are inbuilt into the language.

Let us move to the next Go Interview Questions.

Q8. Explain a routine in GO? What method is used to stop goroutine?

A goroutine is a function that runs with other functions in concurrent mode. To stop go routine, pass the goroutine as a signal channel; this signal channel can be used to push a new value into the program when you want the goroutine to stop. The goroutine polls that channel regularly promptly as it finds a signal; it exists.

Q9. Explain the Syntax For ‘for’ Loop?

Explanation: – The control flow in a for a loop –

If a condition is available, then for loop executes until the condition is true; this step is the same as any other language.

After the main statement of the for loop executes correctly, the program’s flow of control jumps goes back up to the next line, which is an increment statement. This statement does nothing, but it updates any loop control variables. This statement can be left blank if needed if a semicolon comes after the condition. The next condition is now checked again and then evaluated. If a condition is true, the loop runs once more, and the process repeats itself, i.e. the general approach is first to run the body of a loop, then increment step is done, and then again condition is executed. This continues until the condition becomes false and the loop terminates.

If a range is also given, then for loop runs for each value in the range. These are the frequently asked Go interview questions in an interview.

Q10. By how many ways a parameter can be passed to a defined method in the Go language?

When calling a function in Go, there are two ways to pass an argument to a function such as: –

Call by value: This method works by copying an argument’s actual value into the function’s formal parameter. Thus, changes made to the function’s inside parameter do not have an effect on the argument.

Call by reference: This method works by copying the argument address into the formal parameter. The address is used inside the function for accessing the given argument used in the call. It means that parameter changes are made in this way affect the argument.

Recommended Articles

This has been a guide to List Of Go Interview Questions and Answers so that the candidate can crackdown these Interview Questions easily. Here in this post, we have studied top Go Interview Questions, which are often asked in interviews. You may also look at the following articles to learn more –

Ai Vs. Machine Learning Vs. Deep Learning

Since before the dawn of the computer age, scientists have been captivated by the idea of creating machines that could behave like humans. But only in the last decade has technology enabled some forms of artificial intelligence (AI) to become a reality.

Interest in putting AI to work has skyrocketed, with burgeoning array of AI use cases. Many surveys have found upwards of 90 percent of enterprises are either already using AI in their operations today or plan to in the near future.

Eager to capitalize on this trend, software vendors – both established AI companies and AI startups – have rushed to bring AI capabilities to market. Among vendors selling big data analytics and data science tools, two types of artificial intelligence have become particularly popular: machine learning and deep learning.

While many solutions carry the “AI,” “machine learning,” and/or “deep learning” labels, confusion about what these terms really mean persists in the market place. The diagram below provides a visual representation of the relationships among these different technologies:

As the graphic makes clear, machine learning is a subset of artificial intelligence. In other words, all machine learning is AI, but not all AI is machine learning.

Similarly, deep learning is a subset of machine learning. And again, all deep learning is machine learning, but not all machine learning is deep learning.

Also see: Top Machine Learning Companies

AI, machine learning and deep learning are each interrelated, with deep learning nested within ML, which in turn is part of the larger discipline of AI.

Computers excel at mathematics and logical reasoning, but they struggle to master other tasks that humans can perform quite naturally.

For example, human babies learn to recognize and name objects when they are only a few months old, but until recently, machines have found it very difficult to identify items in pictures. While any toddler can easily tell a cat from a dog from a goat, computers find that task much more difficult. In fact, captcha services sometimes use exactly that type of question to make sure that a particular user is a human and not a bot.

In the 1950s, scientists began discussing ways to give machines the ability to “think” like humans. The phrase “artificial intelligence” entered the lexicon in 1956, when John McCarthy organized a conference on the topic. Those who attended called for more study of “the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

Critics rightly point out that there is a big difference between an AI system that can tell the difference between cats and dogs and a computer that is truly intelligent in the same way as a human being. Most researchers believe that we are years or even decades away from creating an artificial general intelligence (also called strong AI) that seems to be conscious in the same way that humans beings are — if it will ever be possible to create such a system at all.

If artificial general intelligence does one day become a reality, it seems certain that machine learning will play a major role in the system’s capabilities.

Machine learning is the particular branch of AI concerned with teaching computers to “improve themselves,” as the attendees at that first artificial intelligence conference put it. Another 1950s computer scientist named Arthur Samuel defined machine learning as “the ability to learn without being explicitly programmed.”

In traditional computer programming, a developer tells a computer exactly what to do. Given a set of inputs, the system will return a set of outputs — just as its human programmers told it to.

Machine learning is different because no one tells the machine exactly what to do. Instead, they feed the machine data and allow it to learn on its own.

In general, machine learning takes three different forms: 

Reinforcement learning is one of the oldest types of machine learning, and it is very useful in teaching a computer how to play a game.

For example, Arthur Samuel created one of the first programs that used reinforcement learning. It played checkers against human opponents and learned from its successes and mistakes. Over time, the software became much better at playing checkers.

Reinforcement learning is also useful for applications like autonomous vehicles, where the system can receive feedback about whether it has performed well or poorly and use that data to improve over time.

Supervised learning is particularly useful in classification applications such as teaching a system to tell the difference between pictures of dogs and pictures of cats.

In this case, you would feed the application a whole lot of images that had been previously tagged as either dogs or cats. From that training data, the computer would draw its own conclusions about what distinguishes the two types of animals, and it would be able to apply what it learned to new pictures.

By contrast, unsupervised learning does not rely on human beings to label training data for the system. Instead, the computer uses clustering algorithms or other mathematical techniques to find similarities among groups of data.

Unsupervised machine learning is particularly useful for the type of big data analytics that interests many enterprise leaders. For example, you could use unsupervised learning to spot similarities among groups of customers and better target your marketing or tailor your pricing.

Some recommendation engines rely on unsupervised learning to tell people who like one movie or book what other movies or books they might enjoy. Unsupervised learning can also help identify characteristics that might indicate a person’s credit worthiness or likelihood of filing an insurance claim.

Various AI applications, such as computer vision, natural language processing, facial recognition, text-to-speech, speech-to-text, knowledge engines, emotion recognition, and other types of systems, often make use of machine learning capabilities. Some combine two or more of the main types of machine learning, and in some cases, are said to be “semi-supervised” because they incorporate some of the techniques of supervised learning and some of the techniques of unsupervised learning. And some machine learning techniques — such as deep learning — can be supervised, unsupervised, or both.

The phrase “deep learning” first came into use in the 1980s, making it a much newer idea than either machine learning or artificial intelligence.

Deep learning describes a particular type of architecture that both supervised and unsupervised machine learning systems sometimes use. Specifically, it is a layered architecture where one layer takes an input and generates an output. It then passes that output on to the next layer in the architecture, which uses it to create another output. That output can then become the input for the next layer in the system, and so on. The architecture is said to be “deep” because it has many layers.

To create these layered systems, many researchers have designed computing systems modeled after the human brain. In broad terms, they call these deep learning systems artificial neural networks (ANNs). ANNs come in several different varieties, including deep neural networks, convolutional neural networks, recurrent neural networks and others. These neural networks use nodes that are similar to the neurons in a human brain.

However, those GPUs also excel at the type of calculations necessary for deep learning. As GPU performance has improved and costs have decreased, people have been able to create high-performance systems that can complete deep learning tasks in much less time and for much less cost than would have been the case in the past.

Today, anyone can easily access deep learning capabilities through cloud services like Amazon Web Services, Microsoft Azure, Google Cloud and IBM Cloud.

If you are interested in learning more about AI vs machine learning vs deep learning, Datamation has several resources that can help, including the following:

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