Deep learning algorithms to learn

Important Deep Learning Algorithms

Before we start studying the Deep Learning algorithms, let’s understand what Deep Learning is all about. We will start with the definition of machine learning first, machine learning is described as a kind of artificial intelligence where computers learn to perform something without being programmed to do it. There are several methods in which machine learning programs can be trained and Deep learning is one of them. From this post, you’ll learn what Deep Learning is about and get knowledge about several Deep Learning algorithms.

What is Deep Learning?

Deep Learning is part of the family of machine learning methods based on learning data representations. It is a technique that trains computers to do what occurs naturally in people: learning through examples. It is the main technology behind autonomous cars, allowing them to identify a stop sign or distinguish a pedestrian lamppost. It’s also the key to voice control in a variety of consumer devices, such as hands-free speakers, TVs, Tablets, and phones.

In Deep Learning Technology, a computer model learns to perform classification tasks directly from text, sound, or graphics. Deep Learning models can achieve State-of-the-art accuracy and sometimes even surpass performance at a human level. Models are trained using neural network architectures and a huge set of labeled data that consists of many layers.

Importance of Deep learning

Accuracy is what separates Deep learning from other technologies. Deep Learning achieves recognition accuracy at a very high level than ever before. This enables consumer electronics to meet the expectations of the user, and it is crucial for applications where safety is a major concern, such as cars without driver. The recent innovations in Deep Learning have improved to the point where in few tasks such as classifying objects in images, Deep learning performs better than people.

Deep Learning Process

Most of the Deep Learning methods utilize neural network architectures. As a result, deep Learning models are commonly referred to as Deep Neural Networks. The term “Deep” generally indicates the number of hidden layers in a neural network. The Deep Networks can have approximately 150 hidden layers, while traditional neural networks contain only 2 to 3 hidden layers. Deep Learning models are trained using neural network architectures and huge sets of labeled data that directly learn functions of the data without manual function extraction.

Neural networks are organized into layers and consist of a set of interconnected nodes. Networks can contain dozens or hundreds of hidden layers.

One of the most popular forms of Deep Neural Networks are CNNs (Convolutional neural Networks). A Convolutional neural network convolves input data with scholarly functions and utilizes 2D Convolutional layers, making this architecture very suitable for processing 2D data, such as images.

Convolutional neural networks eliminate the need for function extraction manually. Therefore, you do not need to detect any features that are used to classify images. The convolutional neural network works by extracting functions directly from images. The relevant features are learned, but not pre-trained while the network trains on a collection of images. This type of automated function extraction ensures that Deep Learning models are very accurate for computer vision tasks such as Object classification.

Deep Learning Algorithms

Deep Learning algorithms automatically learn function representations (usually from unlabeled data), avoiding a large amount of time-consuming engineering. These algorithms depend on developing massive artificial neural networks that were loosely inspired by the brain (cortical) calculations. The following are the types of Deep Learning algorithms:

A Deep-Faith network is a class of Deep Neural Network or a generative graphical model consisting of numerous layers of latent variables (i.e. hidden units) with connections between the layers, but no units within each layer. They can be seen as a compound of unsupervised, simple networks, i.e. sigmoid belief networks + limited Boltzmann machines.

The main advantage of DBNs is the ability of “learning functions”, which is achieved through Layer-by-Layer learning strategies, while the higher-level functions are learned from previous layers. Often they are used to initialize deep discriminative neural Networks (a method known as generative pre-training). They are usually used for pre-training unattended. In this training, the network initially trains a generative model and uses it to initialize a discriminative model.

Generative contradictory Networks (GANs) are a class of artificial intelligence algorithms used in Deep learning. They are performed by a system of 2 neural network models that fight each other in a zero-sum game framework. The 2 neural network models are known as the generator and the discriminator models. The generator model generates samples by taking noise as input.

The discriminator receives samples from both the training data and the generator and must be able to distinguish between the two sources. A continuous game is played by these 2 networks where the generator learns to deliver more realistic monsters, while the discriminator learns to become better at differentiating real data and generated data. These two networks are trained simultaneously and the competition will send the generated samples to indistinguishable from the real data.

Goose can generate photos that look authentic to the observers. Take for example a GAN can be used to make the synthetic photo of a cat that the discriminator has wrong to accept it as a real photograph.

Back propagation is an algorithm used in artificial neural networks to calculate the error contribution of each neuron after processing a batch of data (in image recognition process). The gradient descent optimization generally uses it for adjusting the weight of neurons by calculating the loss function gradient.

The back propagation algorithm is repeatedly rediscovered and corresponds to automatic differentiation in the reverse accumulation mode. For each input value, the back propagation must be a desired and known output. It can therefore be listed as a guided learning method. It can be used with any Optimizer based on gradients such as truncated Newton or L-BFGS. Back propagation is usually used to train Deep Neural Networks (neural networks with more than one hidden layer).

Generalizing logistic regression to the classification problems where we want to handle multiple classes is referred to as Softmax regression (also known as Multi-class logistic regression, maximum entropy classification regression, or Multinomial logistic regression). The labels are binary in logistic regression. It is used for problems such as MNIST digit classification where the goal is to distinguish between ten different numeric digits. Softmax regression allows us to process y (i) ∈ {1,…, K} and K represents the number of classes.

The convolutional Neural Network algorithm is a multi-layered Perceptron that is the special design for identifying the 2-D imaging. It has an input layer, output layer and lots of hidden layers. The hidden layers contain normalization layers, fully connected layers, pooling layers, and convolutional layers.

The convolutional layers apply the convolution operation to the input and pass the result to the next layer. The response of the individual neuron is emulated by the convolution to visual stimuli. The convolutional networks include global or local pooling layers that combine the outputs of the neuron clusters on one layer into one neuron in the next layer.

Fully connected layers are used to connect neurons from different layers. The advantage of CNNs is that they are fairly easy to train and consist of fewer parameters compared to fully connected networks with the same number of hidden units. Explicit function extraction can be avoided in CNN’s.


Deep Learning architectures such as recurring neural networks, Deep belief networks, and Deep Neural networks have been applied to fields such as natural language processing, machine translations, speech recognition, computer vision, and so on. These architectures have generated results in such a way that they are superior to human experts in some cases.

Deep Learning takes machine Learning much closer to the original goal; Artificial intelligence. Deep Learning Algorithms Learn high-level features from data and this is a major step forward on conventional machine Learning. To gain insight into Deep Learning algorithms, there are several sources available on the Internet, such as e-books, websites, and so on. They provide the information regarding Deep Learning methodology and the latest developments in this field.

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