Introduction Machine learning, a subfield of artificial intelligence, has emerged as a powerful tool for tackling a wide range of complex problems. By harnessing the ability to learn from data, machine learning models offer unique advantages over traditional approaches, enabling efficient solutions to be found in scenarios where conventional methods fall short. In this blog, we will explore the domains in which machine learning excels, providing simplified code, superior performance, adaptability to fluctuating environments, and unparalleled insights into complex problems and vast data sets.
In our previous post we introduced deep learning and discussed why it matters now than beofre. We also defined Artifical Intelligence, Machine Learning and Deep Learning.
In this post we will delve more into the building blocks of deep learning, these are
Perceptron Activation functions Fully connected (dense) Layer Deep Neural Netweork The Perceptron Imagine you have a bunch of apples and oranges, and you want a computer program to decide whether a fruit is an apple or an orange based on its weight and size.
In recent years, there has been a surge of interest in deep learning due to its ability to solve complex problems, especially in the field of natural language processing (NLP). From chatbots to language models like ChatGPT and others, deep learning has shown its potential to revolutionize the way we communicate and interact with technology.
Chatbots and language models are perhaps the most visible examples of deep learning in action today.
Hello world! Yes, Today is the day i become a better writer. Let me tell you why.
I have always belived i can write, the problem being that i have had no evidence to show for this. The plan was then to fix this problem. And as any other software engineer it starts with spining up a new shinny blog. So i did purchase samphiltech.com domain name back in 2012 and since then i have been busy building this blog.
