Imagine you’re trying to plan a trip to Hawaii. You’ve got a few pictures of beautiful beaches, a list of things you want to see, and a rough budget in mind. How do you pull it all together? You might browse travel blogs, compare prices, and even watch videos of the islands. You’re using different kinds of information – pictures, text, and video – to make sense of your trip.

Do you read a lot? No? Well, let’s say you’re at a library looking for information on a specific topic. Instead of just browsing through every book on the shelves, you ask the librarian for help.

How do we trust that AI is making good decisions? How do we affirm the decisions of our typical deep neural networks? How can AI explain itself?

Machine learning has become increasingly prevalent in many areas of our lives, from recommending products to us on shopping websites, to determining who gets hired for a job.

Sometime last year, I stumbled upon a paper while I was trying to come up with a really basic way to implement a budget and expenditure planner using an RL agent.

TensorFlow is one of the most widely and well known tools for machine learning and with its various features, it maintains its versatility to operate in different use cases.

Google Cloud AutoML is pretty cool, in that it allows developers to easily train custom machine learning models for vision, natural language, and translation without writing model code.

The idea of one-hot encoding labels in supervised learning isn’t really new. The need for encoding categorical data birthed out of a necessity for data science and machine learning algorithms to understand categorical data.

TensorFlow is a machine learning library used to implement deep learning algorithms in Python, and is very popular for being the most generally used machine learning framework by researchers and industry experts.

In this final and 4th Part of our brief look into Linear Algebra, we’ll talk about the Transpose and Inverse of matrices.

Here’s the Part 3 of our brief look into Linear Algebra, and we’ll learn about matrix-vector multiplication, matrix-matrix multiplication, as well as some essential matrix multiplication properties to note.

In this Part 2 of our brief look into Linear Algebra, we’ll learn about matrix addition and subtraction, as well as matrix-scalar multiplication.

The goal for “The Flow of Tensors” series is to allow us understand how to build machine learning apps using the popular TensorFlow library. But the journey of every traveller always has a beginning. And for this journey, our beginning is a small part of mathematics known as Linear Algebra.

Machine Learning in itself is a set of methodologies and techniques that take datasets and turn them into (smart) software called models.

When the last Face Detection library came out with the actual Mobile Vision API in Android, it was said to be designed to detect faces even at different orientations, at specific landmarks such as the eyes, the nose, and the edges of the lips.

Up until this very moment, nothing in the world has sparked my interest as much as Artificial Intelligence had, and for as long as I can remember, I’ve desperately wanted to plug myself into the journey of bridging the gap between human intelligence and computer intelligence.

Machine learning is an interesting technology, and it is rapidly becoming an integral part of (almost) all AI systems.