Features of Tensorflow
Below, we are discussing some important TensorFlow Features.
Responsive Construct
With TensorFlow, we can easily visualize each and every part of the graph, which is not an option while using Numpy or SciKit.
Articles
What is Logic Programming?
Logic programming is a programming paradigm that sees computation as automatic reasoning over a database of knowledge made of facts and rules. It is a way of programming and is based on formal logic. A program in such a language is a set of sentences, in logical form, one that expresses facts and rules about a problem domain. Among others, Datalog is one such major logic programming language family.
Structure
Let’s talk about facts and rules. Facts are true statements — say, Bucharest is the capital of Romania. Rules are constraints that lead us to conclusions about the problem domain. These are logical clauses that express facts. We use the following syntax to write a rule (as a clause):
Introduction to Machine Learning Algorithms
There are two ways to categorize Machine Learning algorithms you may come across in the field.
- The first is a grouping of algorithms by the learning style.
- The second is a grouping of algorithms by a similarity in form or function.
Generally, both approaches are useful. However, we will focus in on the grouping of algorithms by similarity and go on a tour of a variety of different algorithm types.
Comparison Between Data Science, AI, ML, and Deep Learning
What Is Data Science?
R Data science includes data analysis. It is an important component of the skill set required for many jobs in this area. But it’s not the only necessary skill. They play active roles in the design and implementation work of four related areas:
- Data architecture
- In data acquisition
- Data analysis
- In data archiving
Learn more about Data Science.
In this post, you will learn about how metamorphic testing could be used for performing quality control checks/testing on Machine Learning models. It is primarily meant for data science specialists to plan the test cases to test the Machine Learning (ML) model implementation from a QA perspective.
Testing Machine Learning models from a quality assurance perspective is different from testing Machine Learning models for accuracy/performance.
After my article, “Role of Project Manager in Data Science”, a couple of program managers suggested me to elaborate the use case on meeting release commitments. We are going to explore simulation, one of the amazing concepts in Artificial Intelligence. Quantitative analytic techniques, such as the Monte Carlo simulation, helps program managers in decision making through probabilistic distributions of potential outcomes.
Monte Carlo relies heavily on the randomness of key variables in solving the problem. Along with key parameters, we also need to understand the relationship between them and sufficient data to analyze further. The five steps listed in “Forecasting the future: Let’s rewind to the basics” are essential to building an accurate model.
What Is a Classification Problem?
Classification is an important and central topic in ML, which has to do with training machines how to group together data by particular criteria. Classification is the process where computers group data together based on predetermined characteristics — this is called supervised learning. There is an unsupervised version of classification, called clustering where computers find shared characteristics by which to group data when categories are not specified.
For example:
Data science is all about capturing data in an insightful way, whereas Machine Learning is a key area of it. Data science is a fantastic blend of advanced statistics, problem-solving, mathematics expertise, data inference, business acumen, algorithm development, and real-world programming ability. And Machine Learning is a set of algorithms that enable software applications to become more precise in predicting outcomes or take actions to separate it without being explicitly programmed.
The distinction between data science and Machine Learning is a bit fluid, but the main idea is that data science emphasizes statistical inference and interpretability, while Machine Learning prioritizes predictive accuracy over model interpretability. And for both data science and Machine Learning, open source has become almost the de facto license for innovative new tools.
