Reinforcement Learning is a major area of interest within the field of machine learning. Its ability to learn without input data is opening new opportunities in different areas such as robotics and gaming. What is Reinforcement Learning? Reinforcement Learning is a type of Machine Learning technique based on Learning by feedback. Reinforcement Learning is a …
Improving the decision-making of the company is the ultimate goal of data science. It drives decision-making in every part of this modern society By suggesting what advertisement you watch, books you read, and Music you listen to. By recommending friends on social media, and emails that are filtered to spam on your mailbox. This massive …
Deep learning works better than traditional machine learning algorithms in many cases. Difference between deep learning and machine learning: Machine learning (ML) is a subfield of AI that consists of imitating human intelligence and learning from an experience without being previously programmed. ML algorithms include traditional methods such as Linear Regression, Logistic Regression, Decision Tree, …
When to Choose Neural Networks over Traditional Machine Learning Read More »
Beginners can confuse cost functions with Performance metrics. Some techniques are used When creating a deep learning model to help design the optimum neural networks architecture such as cost functions and performance/evaluation metrics. Many mistake those terms and use them interchangeably! In this article, we will explain the difference between cost functions and Performance metrics, …
Cost Function and Performance Metrics in Deep Learning Read More »
The term AI and Machine learning get more popular recently because of the huge amount of daily data produced on the internet, besides the high computational power of computer systems. Every time you interacts with a platform such as social networks or e-commerce website, your data are stored on the cloud. Imagine millions of internet …
An Overview Of Machine Learning Definition and Types Read More »
You created and validated your machine learning model and now it is ready to create value for your business customers. The next step now is to integrate your machine learning model into production and make it accessible and available to users and other systems. In other words to deploy your machine learning model into the cloud. …
