Never in the same shape
Inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data.
CHAID (Chi-squared Automatic Interaction Detector) is an algorithm used for building decision trees, in particular for splitting variables based on their interactions with target variables. Unlike traditional decision trees, which rely primarily on the Gini index or entropy to choose splits, CHAID uses chi-square tests to automatically determine optimal splits.
A Decision Tree is a machine learning model that represents a series of logical decisions made based on attribute values. It is a tree structure which is used to make decisions or make predictions from the input data.
Random Forest is a machine learning algorithm that is based on building a set of decision trees. Each tree is built using a random subset of the training data and produces a prediction. Finally, the predictions from all the trees are combined through majority voting to get the final prediction.
The Gradient Boosting algorithm is a machine learning technique that builds a predictive model by combining several weaker models (usually decision trees) together into a single strong structure. The main goal of Gradient Boosting is to progressively improve the weaknesses of weak models, allowing you to create a stronger and more adaptable model.
Deep learning is a computational technique that allows you to extract and transform data from sources such as human speech or image classification, using multiple layers of neural networks. Each of these layers takes its inputs from the previous layers and refines them, so progressively. The layers are trained by algorithms that minimize their errors and improve their accuracy. In this way the networks learn to perform specific tasks.
Manage the analysis, the programming, and the visualization of the results in an interactive way and at the same time produce readable reports all at the same time is now possible thanks to interactive notebooks. In particular, for those who work with JavaScript libraries, the interactive Notebook is available for free.
The progress of this last year regarding Deep Learning is truly exceptional. Many steps forward have been made in many fields of technology thanks to neural networks and among these there is the synthetic voice, or rather the Text-To-Speech (TTS) that is, that series of technologies able to simulate the human way of speaking by reading a text. Among the models realized, therefore, there is WaveNet, a highly innovative model that has revolutionized the way of doing Text-To-Speech making them jump really forward
2017 was a special year for Deep Learning. In addition to the great experimental results obtained thanks to the algorithms developed, the Deep Learning this year has seen its glory in the release of many frameworks. These are very useful tools for developing numerous projects. In the article you will see an overview of many new frameworks that have been proposed as excellent tools for the development of Deep Learning projects.
In recent years many new professions are emerging, some of which you probably barely know. These new professional activities will play an important role in the years to come. One of these figures is precisely the Data Scientist. In this article you will see in more detail what is the work of the Data Scientist, what should be his skills and what activities he must perform.
Personally I make use of Aptana IDE as a development environment and use WAMP (for Windows) and LAMP (for Linux).. But generally during the various tutorials, articles and presentations I prefer to refer to Jsfiddle: An online environment where you can test and save in real time the JavaScript code also defining its CSS declarations and HTML tags.