What Is The Difference Between Deep Learning and Machine Learning?

Deep Learning vs Machine Learning

Deep Learning is a subsidiary of machine learning that uses a hierarchical level of artificial neural networks to carry out the process of machine learning. Artificial neural networks mimic the functions of the human brain with many nodes connected together like a web. The hierarchical functions of deep learning systems allow the machines to process data with a nonlinear approach.

What is machine learning?

Machine learning is a domain of artificial intelligence that equips the system with the ability to automatically learn from experience without being explicitly programmed to do so. Machine learning emphasizes on the development of programs that can access data and use it to learn and improve the systems.

The automated process begins with observation. This might include examples, experience, or instructions. The algorithm can also look for patterns in the data and detect its future occurrences. The fundamental operating principle is to allow the automatic learning of computers without any human intervention whatsoever.

Type of Machine Learning methods:

Supervised Algorithms: These algorithms can apply learned patterns in the past to new data using the labeled samples to predict forthcoming events. The predicted output is compared with the correct one to correct the parameters.

Unsupervised Algorithms: Such algorithms are used when the information used to train the predictive model is neither labeled nor classified. Unsupervised algorithms have the ability to uncover a framework from unlabeled data.

Reinforcement Algorithms: These algorithms determine the error and rewards by interacting with the environment. Software Engineers can determine the ideal behavior within a context to optimize performance.

Basic Machine Learning Algorithms

Support Vector Machine (SVM) : The support vector machine (SVM) algorithm is a linear model which classifies (categorizes) using a straight line. It creates a line or a hyperplane which separates the data into classes. A simple example would be to arrange apples and oranges into classes using SVM.

K Nearest Neighbor (KNN) : It is one of the simplest classification algorithms. It uses a database that comprises several data points separated into classes. KNN uses these points to predict a new sample point. The number of samples used to predict is represented by K.

K Means Clustering : It is an unsupervised learning algorithm. Clustering is a technique for finding similarities in data. We do not have predefined labels or class values. You’ll define a target number k, which refers to the number of centroids you need in the dataset. K-means algorithm identifies k number of centroids, and then allocates every data point to the nearest cluster while keeping the centroids as small as possible.

Introduction to Deep Learning

Deep Learning is a subdivision of machine learning that deals with algorithms aimed at mimicking the function of the human brain. Deep Learning creates artificial neural networks to achieve this purpose.

The reason for the massive success of deep learning is its development side-by-side with the data expansion. The massive amount of data known as ‘big data, comes from various sources like social media, ecommerce, etc. This data is widely available and anyone can use it to improve the performance of their models.

The vast amount of data is greatly unstructured and virtually impossible for humans to comprehend. Organizations realize the great potential that might come from extracting this data. This is why companies are using AI for support.

Let us now discuss basic deep learning techniques and algorithms…

Deep Neural Networks (DNN) : At the basic level, a neural network with some level of complexity at least two layers qualifies as a deep neural network. It is known as a deep net in short. Deep nets process data in a sophisticated mechanism by using complex mathematical functions. All modern-day complex algorithms are known as deep neural networks.

Recurrent Neural Networks (RNN) : In a neural network, we assume that all inputs and outputs are independent of each other. However, in many cases, neural networks are not suitable such as predicting the next word in a sequence. In such a case we need the context of the previous word in the sentence.

Recurrent Neural Networks use information coming in a sequence. We call them recurrent because the same operation for every element in the sequence is performed with the output dependent upon the previous computations. They have a memory that stores whatever has been calculated so far. They differ from neural networks in the sense that they do not use feed-forward rather internal state memory to process inputs. RNNs have a limitation about short-term memory that is overcome using Long Short Term Memory (LSTM) that can remember information for a long period of time.

Convolutional Neural Network (CNN): In deep learning, convolutional neural networks are used to analyze and process visual imagery. They are known as shift invariant or space invariant artificial neural networks based upon the shared weight architecture. They are used in image and video recommendation systems, recommender systems, image classification, image segmentation, natural language processing, etc.

CNN use regularized versions of a multi-layer perceptron. CNN automatically and adaptively learns the hierarchy of features through backpropagation and passes through the layers of convolution, pooling, and fully connected layers.

A Birds Eye View of CNNs

CNN's are a model for deep learning that is useful for processing data with a grid pattern. They are particularly useful for images due to their inherent grid pattern.

CNN's consist of three layers known as convolution, pooling, and fully connected layers. Convolution and pooling layers perform the extraction of useful features whereas the final layer connects the extracted features with the output that could be classified.

The convolution layer consists of mathematical operations such as a linear operator known as convolution or reduction in the dimensionality of the image. Since images are stored as an array of pixels, and a small grid of parameters known as the kernel, a feature extractor can be applied at each position in the image-making CNN's highly effective.

The output of one layer serves as the input to the next layer so that the extracted features are processed in a hierarchy becoming more complex with progression. Training minimizes the difference between the true labels and the model output by using mathematical algorithms called backpropagation, gradient descent, etc.

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Deep learning is a subset of machine learning that performs better when the data sets are large and/or complex. If your business relies on a lot of complicated datasets, deep-learning algorithms may be worth exploring to see if they can help improve your bottom line. This blog post has been an introduction to what deep learning is and how it differs from other types of machine learning methods out there. We hope this helps you get started with understanding the basics of these technologies so you can make more informed decisions about which tools will work best for your company's needs! Let us know in the comments below - do you think deeply-learning machines could help increase profits at your organization?

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