Deep Learning

What is deep learning in MATLAB? 

You've heard of AI (artificial intelligence), but without a doubt, you've never heard of deep learning. Deep learning has recently become the standard in AI research and application. It has also been described as "the next big thing" in tech, and for a good reason.It is a great new way to train machines on many tasks, from video analyses to playing games. In this blog post, we come up with the best solution of what deep learning is in MATLAB.

Deep learning

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. To explain Deep Learning, we must first ask ourselves what machines can't do — things like understanding the context of an image, recognizing objects in an image, understanding speech, and so on. While these functions are still beyond our reach — at least for now — Deep Learning overcomes the limitations of traditional techniques and tries new approaches.

Deep learning in terms of MATLAB

At the heart of deep learning are algorithms that enable computers to analyze data, learn from experience, and make predictions based on this analysis. MATLAB makes it easy to build these algorithms by providing tools and functions for managing large data sets and specialized toolboxes for working with machine learning. MATLAB is the ideal tool for building and deploying deep learning models.

Whether you want to train or understand a neural network, analyze computer vision data, or drive an autonomous vehicle, it’s easy to use MATLAB. You can quickly build and deploy models on disk, save and load files in memory, access powerful functions like neural net training and computer vision inference, and create mathematical expressions to visualize your results quickly.

Critical features of deep learning

• Maintain workflow

• Labeling of images

• Easy to work


10 best algorithms of machine learning for beginners

There are several ways to segment data. Some algorithms allow you to separate the data into clusters, and others can give you a comparison between two sets of data.

Machine Learning Algorithms are the most sophisticated mathematical functions used to discover hidden patterns from data.


Machine learning broadly classified into 4 types

• Supervised learning

• Unsupervised learning

• Semi-supervised learning

• Reinforcement learning


These types of ML logarithms are further classified into the following logarithms;

• Linear regression

• Logistic regression

• Decision tree

• SVM  algorithm

• Naïve bayes algorithm

• KNN algorithm

• K-means

• Random forest algorithm

• Dimensionality reduction algorithm

• Gradient boosting algorithm

• adaBoosting algorithm

linear regression

in this te of algorithm, a simple relationship is formed between a dependent and an independent variable 

dependent variable

those variables that are under the influence of any other factor 

independent variable

the variable that affects the other variable

• it involves the visual analysis

• it is represented as 

• X = a*P+b


• X = dependent variable as its value depends on the value of P, a and b

• P = independent variable as it affects the value of X 

• A = slop of graph

Logistic regression

• It uses the discrete values we use in binary ( 0 and 1 )

• It helps us to predict the probability of any event


Decision tree

• It is used to classify the data in the form of tables or hierarchy 

• It is used to split the data into two or more two groups

SVM logarithm

This algorithm draws the plot using raw data in several dimensional spaces.

Naïve bayes algorithm

• It tells that the presence of any particular feature in data is unrelated to other segments of the same data.

• It is the most sophisticated type of algorithm in machine learning.

KNN algorithm

• This can be classified in both regression and classification cases.

• But it is mainly used to solve the classification process. 

• We can use this log to compare the data with other data sets.

K means

• It is a type of unsupervised learning.

• It is most probably used to classify the cluster data. 

• It solves the problem by forming the k clusters. 

• First, it is select any  number from the cluster that is known centroid

• Now each data set to form the group to the closest point to the centroid

Random forest algorithm

• A Random Forest is a decision tree ensemble built by randomly splitting a given case (singleton variable) into sub-trees, each of which has its value of one or more attributes. 

• Each split yields an estimate with some degree of certainty; this estimate is then averaged with all other calculations in the forest. 

• In essence, the Random Forest overcomes the curse of dimensionality. 

• Because it finds numerous splits for your problem, it can handle more possible solutions than any particular algorithm can operate on its own.

Dimensionality reduction algorithm

Dimensionality Reduction is the process of reducing dimensions in a dataset, i.e., reducing the number of variables used to describe an object from a large set of variables into a smaller number of critical features from which useful information can be extracted.

Gradient boosting algorithm and AdaBoosting algorithm

Gradient boosting and AdaBoosting are two of the most popular instance-based classification algorithms. These algorithms are typically used when massive loads of data have to be handled to make predictions with high accuracy.

Boosting is an ensemble learning algorithm that combines the predictive power of several base estimators to improve robustness.

Benefits of deep learning toolbox

• Enables us to use CNNs and LSTM to perform regression and classification

• We can build GANs and Siamese networks by using a toolbox

• We can use automated differentiation, custom training loops, and shard weight

• We can design, analyze and train network graphically

• We can manage multiple deep learning experiments and can compare codes with different experiments

• We can keep track of training parameters, and analyze results

• We can visualize the layer activation by deep learning


Last words

Using MATLAB, the Deep Learning Toolbox provides a familiar and easy-to-use interface for manipulating and visualizing deep neural networks (DNNs). The toolbox enables you to write applications that connect to or even extend the toolbox functions. In short, this toolbox will help you leverage MATLAB to build and apply DNNs, which are at the vanguard of modern deep learning methodology.


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