1.4.3 Deep Learning Technique
Machine Learning at its most basic is the practice of using algorithms to parse data, learn from it, and then make a determination or prediction about something in the world. So rather than hand-coding software routines with a specific set of instructions to accomplish a particular task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task [12]. Deep learning is another Machine Learning (ML) algorithm. Deep learning is essentially a set of techniques that help you to parameterize deep neural network structures, neural networks with many, many layers and parameters. Deep Learning breaks down tasks in ways that makes all kinds of machine assists seem possible, even likely. The confusion matrix, in Figure 8 shows that the accuracy of this model is (90.80) with weighted average precision (91.37) greater than recall (91.11) and F1-score (91.24). From the above results, it appears that Deep Learning classifier achieve higher accuracy, precision, recall, and F1-score. Figure 11: Clustering accuracy using Deep Learning Technique
1.5 Results Comparison
Table 2: Performance Measures Comparison
Model Decision Trees Naïve Bays Deep Learning
Domain precision recall precision recall precision recall food 100.00 25.93 58.06 66.67 46.55 100.00 communication 63.77 95.65 88.89 86.96 100.00 100.00 education 83.54 88.26 88.65 88.26 90.22 88.26 medical 61.67 62.71
Dr. Ahrendt noted the huge advancements that have been made over the last decade, but made sure to note that the math behind AI and machine learning is quite old mathematics. “Now that we can compute things so quickly… we can see the bloom of AI and machine learning.”
Artificial Intelligence (AI) is a topic of major controversy in today’s world. When people first hear about this, they may quickly jump to conclusions that can be either positive or negative. On one end of the spectrum, some may think that it could mean the end of humanity. That AI systems might surpass human intelligence, and come to the conclusion that humans are inferior to them, which has several implications on its own. On the other end, some may think that it could be the pinnacle of human innovation. AI can make our lives much easier with everyday tasks such as planning out schedules, or even by just driving people to work. AI can go one of two ways, which is why it is, understandably, a topic of major
Deep Learning (DL) has been shown to outperform most traditional machine learning methods in fields like computer vision, natural language processing, and bioinformatics. DL seeks to model high-level abstractions of data by constructing multiple layers with complex structures, which compose of hundreds millions of parameters to be tuned. For example, a deep learning structure for processing visual and other two-dimensional data, convolutional neural network (CNN) [1], which consists of three convolutional layers and three pooling layers, has more than 130 millions of parameters if the input has 28x28 pixels. While these large neural networks are powerful, we need high amount of training data. DL tasks need considerable data storage and memory bandwidth.
I was honestly almost as happy as her to find a solution. She was grateful, and said she appreciated my patience and thoroughness. Before she left I gave her some tips for backing up her work regularly…and luckily she didn’t need that note.
One of this AI technologies tools is the Artificial Neural Networks which work much like the human brain and have the ability to learn from training
Artificial intelligence is the intelligent behavior by machines, rather than the natural intelligence of humans and other animals. Artificial Intelligence is defined as the study of intelligent agents in computer science. An intelligent agent is any device that perceives its environment and takes actions that maximize its chance of success at some goal. Informally, the term artificial intelligence is applied when a machine mimics cognitive functions that humans associate with other human minds, such as learning and problem solving skills. From SIRI, CORTANA to autopilot cars of Tesla, the Artificial Intelligence is progressing and improving rapidly. While Science fiction movies often portray artificial intelligence as robots with human like characteristics only, the AI can encompass anything from Google’s search algorithms to IBM’s Watson to autonomous weapons. Artificial intelligence today is properly known as narrow AI (or weak AI), in that it is designed to perform a narrow task (e.g. only facial recognition, internet searches, learning or only self-driving cars). However, the long term goal is, of many researchers, to create a strong AI (general AI or AGI). While narrow AI may outperform humans at whatever its specific task is, like solving equations or playing chess, strong AI (AGI) would outperform humans at
Deep Learning which developed as a Machine Learning approach has become very popular nowadays. It helps in dealing with complex problem with a greater understanding. Traditional Machine Learning model used to solve problems successfully where final output was a simple function of input data, whereas Deep Learning can capture composite relations. Deep Learning is basically learning data representations which is all about making things and presenting to real audiences.
The theories around AI and machine learning brings new dangers. Specifically, machine learning frameworks frequently have low " interpretability," implying that people experience problems making sense of how the structures achieved
There are several projects for this research opportunity. I am part of a project called "Better Glass Edu", a project specifically for the first year researchers to get acclimated to the terms of and usage of a neural network using machine learning algorithms. Before going in-depth into the research topic we must first understand what Machine learning is. Machine learning is an application of artificial intelligence that Provides systems the ability to automatically learn and improve from experience. It focuses on the development of computer programs that can access data and use it learn for themselves. A more practical way it is feeding a computer program a set of datapoints. The computer program uses various algorithms to understand the
Deep learning has come with a revolutionary change in the field of machine learning. Accuracy of different datasets jumped after applying deep learning approach.
Deep Learning (or deep structured learning, or hierarchical learning or deep machine learning) is a branch of Machine Learning which is based on a set of algorithms that attempts to model high level abstractions in data by using a deep graph with multiple processing layers which are composed of multiple non-linear and linear transformations.
What is machine learning? Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. You may not know it, but machine learning is all around you.
In the present chapter Feed forward back propagation (FFBP), Layer recurrent and NARX artificial neural network structures with Levenberg – Marquardt training algorithm are suggested for estimation of the radius for a given resonant frequency of a centre feed microstrip patch antenna and it is demonstrated using a circular patch geometry. Through the particular chapter the effect of the variation in the resonant frequency on the radius and vice versa has been analysed using two-layered FFBP, Layer recurrent and NARX ANN models. The analysis model is developed for the determination of radius in this work because in usual practise the synthesis part comprised of finding the radius and the analysis part was about finding the resonant
Just like humans, computers can now learn and adapt, thanks to machine learning, a subfield in AI. With artificial neural networks to mimic those of the human brain, intelligent computers can learn from examples, meaning that no task specific programming is required.