This paper gives a brief review on the different approaches, algorithms used in deep learning techniques from the beginning to the present scenario. The main aim of this study is to reveal the advantages features of the handsome approaches utilized in the deep learning process. Deep learning ...
Learn algorithms for solving classic computer science problems with this concise guide covering everything from fundamental algorithms, such as sorting and searching, to modern algorithms used in machine learning and cryptography Algorithms have always played an important role in both the science and prac...
Some machine learning algorithms are deterministic. Just like the programming that you’re used to. That means, when the algorithm is given the same dataset, it learns the same model every time. An example is a linear regression or logistic regression algorithm. Some algorithms are not determinis...
Reduction of Complexity in Data: Helps reduce the dimensionality of data, making complex data more comprehensible. Feature Discovery: This can be used to find useful features that can improve the performance of supervised learning algorithms. Flexibility: Can handle changes in input data or the envir...
An example of a regression problem would be theBoston house pricesdataset where the inputs are variables that describe a neighborhood and the output is a house price in dollars. Some machine learning algorithms are described as “supervised” machine learning algorithms as they are designed for sup...
In the real world, we usually have to deal with a lot of raw data. This raw data is not readily ingestible by machine learning algorithms. To prepare the data for machine learning, we have to preprocess it before we feed it into various algorithms....
Knowledge-based analysis of genomic expression data by using different machine learning algorithms for the purpose of diagnostic, prognostic or therapeutic applicationBioinformatics Knowledge-based analysis of genomic expression data by using different machine learning algorithms for the purpose of diagnostic...
Learning is always considered an important aspect of intelligence. Machine learning has grown into one of the most active fields in AI, and it gives computers the ability to work without being explicitly programmed with problem-specific skills. Learning algorithms have been used in many applications...
Offline programming, or simulation, is most often used in robotics research to ensure that advanced control algorithms are operating correctly before moving them onto a real robot. However, it is also used in industry to reduce downtime and improve efficiency. It can be a particularly useful meth...
In addition to the reference model, we selected models from four different families of machine learning algorithms. First, we distinguished between single-target and multi-target models. Linear regres- sion (LR) belongs to the former class and requires a fairly low number of calculation steps. ...