What is the Difference between Deep Learning ,Machine Learning and Artificial Intelligence

What is the Difference between Deep Learning ,Machine Learning and Artificial Intelligence

What is the Difference between Deep Learning ,Machine Learning and Artificial Intelligence   Ans: Machine Learning is all about algorithms that analyses data, learn from the data, and then apply what they’ve learned to make informed decisions. Deep Learning is a type of machine learning that is inspired by the structure of the human brain. It effectively works well in feature detection. The main difference between deep learning and machine learning is, machine learning models become well progressively but the model still needs some guidance. If a machine learning model returns an inaccurate or wrong prediction then the programmer needs…

Machine learning algorithms for Healthcare industry

Machine learning algorithms for Healthcare industry

Machine learning algorithms for Healthcare industry As per the changing technology, AI has been in to many sectors. One of them is Health care sector, where human physicians can be replaced by AI. By improving technology there is more scope of innovations in the field of health care department. Where the treatment is designed in such a way that the individual feels lesser pain and more comfort. Hence risk assessment plays a major role in the Field of AI. AI helps the physician to provide up to date medical information through journals, textbooks for proper patient care… AI can extract…

Machine Learning or Deep Learning model must be in balanced state

Machine Learning or Deep Learning model must be in balanced state

Machine Learning or Deep Learning model must be in balanced state   If you ever built a supervised Machine Learning model on some real-time data, it is impossible that it will perform well both on train set and test set in a first evaluation attempt. Real-time data is so noisy, of course as part of model building activity you might have performed enough cleanup and did efficient feature engineering, though usually the model either will tend to overfit or underfit the training data.   How do you detect if the model is underfit (Bias Problem) or overfit (Variance Problem)? Usually…

Ridge Regression in Machine Learning

Ridge Regression in Machine Learning

Ridge Regression in Machine Learning The Ridge Regression is a regularization technique or in simple words it is a variation of Linear Regression. This is one of the method of regularization technique which the data suffers from multicollinearity. In this multicollinearity ,the least squares are unbiased and the variance is large and which deviates the predicted value from the actual value. In this equation also have an error term.                                                              …

Loss Functions in Machine Learning

Loss Functions in Machine Learning

Loss Functions in Machine Learning In this article we will learn the Loss Functions, types and its applications. Loss function is very simple, which is used to evaluate how well our algorithm works.if our predictions are totally deviate too much from the actual values the loss function would large number and vice versa. By using an optimization function the loss function learns to reduce the error in our prediction values. Generally In machine learning models, we are going to predict a value given a set of inputs. The model has a set of weights and biases that you can tune…

The Key To Flexibility And Computational Measurement Is Distributed Machine Learning

The Key To Flexibility And Computational Measurement Is Distributed Machine Learning

The Key To Flexibility And Computational Measurement Is Distributed Machine Learning Distributed machine learning is emerging with the concept of “big data.” Before there was big data, there was a lot of research work to make the machine learning algorithm faster and more than multiple processors. This type of work is often referred to as “parallel computing” or “parallel machine learning,” and its core goal is to disassemble computing tasks into multiple small tasks that are distributed across multiple processors for computation. Distributed computing or distributed machine learning, in addition to distributing computing tasks across multiple processors, more importantly distributes…

Now Machine Learning Can Identify Insect Vector Nourishment

Now Machine Learning Can Identify Insect Vector Nourishment

Now Machine Learning Can Identify Insect Vector Nourishment Researchers have utilized machine learning calculations to encourage PCs to perceive the creepy crawly bolstering examples associated with pathogen transmission. The investigation, distributed in PLOS Computational Biology, additionally reveals plant qualities that may prompt the interruption of pathogen transmission and empower propels in horticulture, domesticated animals and human well being. Bugs that feed by ingesting plant and creature liquids cause annihilating harm to people, domesticated animals, and agribusiness around the world, basically by transmitting pathogens of plants and creatures. These bug vectors can obtain and transmit pathogens causing irresistible ailments, for example,…

The Time-Obstructed interaction in cells is discovered By Machine Learning Algorithm

The Time-Obstructed interaction in cells is discovered By Machine Learning Algorithm

The Time-Obstructed interaction in cells is discovered By Machine Learning Algorithm Neda Bagheri told that “We need to see how cells decide, so we can manage the choices they make. The smallest unit of namely  life cell may choose to isolate wildly, or, in other words with tumor. In the event that we see how cells settle on that choice, at that point we can plan procedures to intercede.” Inside cells strange communication takes place, Bagheri himself and there team members have composed another machine learning calculation that can assist draw an obvious conclusion among the qualities’ connections inside cell…

Machine learning model accurately predicts mental illness

Machine learning model accurately predicts mental illness

Machine learning model accurately predicts mental illness Multi-mode, multi-site machine learning examination demonstrated that the predictive model distinguished up to 83% of clinically high-risk mental patients and 70% of introductory depressive patients dependent on 1-year social capacity results. Past investigations have demonstrated that clinical, neurocognitive, neurophysiological and MRI information can be utilized to anticipate psychosis in individual patients at high danger of clinical results, from Nikolaos Koutsouleris, MD, from the Department of Psychiatry and Psychotherapy at Ludwig-Maximilian University, Germany, and associates. JAMA Psychiatry clarifies that the utilization of machine learning fortifies these discoveries, demonstrating that clinical standard information might be…

The Facts Of Acquiring Machine Learning At Big companies

The Facts Of Acquiring Machine Learning At Big companies

The Facts Of Acquiring Machine Learning At Big companies The success of machine learning projects in industry is determined by many factors, the consideration of which will allow to optimize the distribution of resources and exclude projects that do not bring economic benefits at the early stages. Today, more and more companies are using developing tools for big data research in order to initiate the transition to a new digital technological mode in communications, decision making, situation assessment and business processes. Special hopes are placed on mathematical methods and algorithms of artificial intelligence, in some cases, allowing us to understand…

1 2 3 5