This article was published as part of the Data Science Blogathon
This article aims to explain deep learning and some supervised deep learning algorithms.. This article will discuss the following topics
- Deep Learning Definition
- How the deep learning algorithm works.
- Types of Supervised Deep Learning Algorithms
- The 5 main applications of deep learning algorithms
Deep Learning Definition
Deep learning is a subset of a machine learning algorithm that uses multiple layers of neural networks to perform data processing and calculations on a large amount of data.. Deep learning algorithm is based on the function and functioning of the human brain.
Deep learning algorithm is capable of learning without human supervision and can be used for structured and unstructured data types. Deep learning can be used in various industries such as healthcare, the finances, to the bank, electronic commerce, etc.
How the deep learning algorithm works
The operation of deep learning algorithms depends on the neural network, just like the human brain calculates information using millions of neurons.
Let's analyze the type of layers:
- Input layer: the input layer has input features and a data set that we know.
- Hidden cloak: hidden layer, just like we need to train the brain through hidden neurons.
- Output layer – value we want to classify
We get the input feature from the observation and put it on a layer. That layer creates an output that becomes the input for the next layer which is known as the hidden layer.. This happens until we get the final result.
We further separate the network and add many hidden layers depending on the complexity of the problem and we connect everything just like the human brain interconnects everything and that's how the input values are processed through all the hidden layers and then we have the output. That is why this learning process is known as deep learning because a lot of calculations are done between the input and output layers.
Types of deep learning algorithms:
Here is the classification of Deep Learning algorithms:
Basically, we can classify deep learning into two types and then go deeper into each type in various deep learning algorithms.
Here, in this article, we will discuss supervised deep learning algorithms.
- Red neuronal artificial
- Red neuronal convolucional
- Red neuronal recurrente
Now, let's analyze these 3 algorithms in brief:
1. Red neuronal artificial:
An artificial neural network is the component of a computer system designed in such a way that the human brain analyzes and makes a decision. Ann is the cornerstone of deep learning and solves the problem that seems impossible or very difficult for humans.
Artificial neural networks work like a human brain. The human brain has billions of neurons and each neuron is made up of a cell body that is responsible for calculating information by carrying the information to the hidden neurons and providing the final output..
ANN initially in the training phase learns to identify patterns based on inputs given to the input layer. During this phase, Ann's output is compared to the actual output, and the difference between these two is known as an error.
The goal is to minimize the error by adjusting the weight and bias of the interconnect, what is known as backpropagation. With the process of backpropagation, the difference between the desired output and the actual output produces the smallest error.
2. Red neuronal convolucional
CNN is a supervised type of deep learning, most preferably used in image recognition and computer vision.
CNN has multiple layers that process and extract important features from the image.. There are mainly 4 CNN operation steps
Paso: 1 Convolution operation with Relu activation function
The goal of the convolution operation is to find features in the image using feature detectors to preserve the special relationship between pixels.. The Relu activation function is used to break linearity and you want to increase nonlinearity because the images themselves are highly nonlinear.
Paso: 2 grouping
Binning is a downsampling operation that reduces dimensions and computation, reduces overfitting as there are fewer parameters and the model is tolerant to variation and distortion.
Paso: 3 flattening
Flattening is used to put the output of the pool into a one-dimensional array before further processing..
Paso: 4 fully connected layer
A fully connected layer is formed when the flattening output is fed to a neural network that further classifies and recognizes the images..
3. Recurrent neural networks (RNN)
RNN is a type of supervised deep learning where the output of the previous step is fed as input to the current step. RNN's deep learning algorithm is best suited for sequential data. The RNN is most preferably used in image captions, time series analysis, natural language processing, handwriting recognition and machine translation.
The most important feature of RNN is the Hidden state, that memorizes certain information about a sequence. There are mainly 4 steps of how RNN works.
- The output of the hidden state at t-1 was fed into the input at time t.
- In the same way, the output at time t was fed into the input at time t + 1.
- RNN can process input of any considerable length
- RNN computation depends on historical sequence data and model size does not increase with input size.
This way, RNN converts independent activations to dependent activations, thus reducing the complexity of incrementing parameters and remembering each previous output by giving each output as input to the next hidden layer.
The 5 main applications of deep learning algorithms
Then, shows some ways in which deep learning is used in various industries.
1. Computer vision
Computer vision relies primarily on image processing methods. Before deep learning, the best computer vision algorithm based on conventional machine learning and image processing yielded an error rate of 25%. But, when a deep neural network was used for image processing, the error rate was reduced to 16 percent, and now with the advancement in deep learning algorithms, the error rate was reduced to less than 4%.
2. Analysis and comprehension of texts
Text analysis consists of the classification of documents, sentiment analysis, automatic translation, etc. Recurrent Neural Networks are the most useful deep learning algorithm here, due to the sequential type of textual data.
3. Speech recognition
Speech recognition allows computers to process human speech into text.. Traditionally, speech recognition relies heavily on an important feature extraction process, but deep learning is working directly on raw data and training done on a large audio recording dataset.
4. Pattern recognition
Pattern recognition is the automated identification of patterns and regularities in data.. The data type can range from text, images to sounds or audio.
PayPal uses deep learning through H2O, a predictive analytics platform, to help prevent fraudulent purchase and payment transactions and
5. autonomous vehicles
The autonomous vehicle managed to collect data about its environment from various sensors, explain it and, based on the explanation, choose what actions should be taken. Deep learning allows us to learn to do work as efficiently as humans.
Thank you for reading! In my next article, I will explain various activation functions with applications.
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