Implementing Retrieval Augmented Generation (RAG) is like having an AI assistant with a vast library of knowledge at its fingertips. It seamlessly blends the precision of retrieval-based models with the creativity of generative models. Picture this: instead of conjuring content out of thin air, RAG sifts through a treasure trove of data to craft insightful, contextually relevant outputs.

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.

Continued from part one, where we’ve completed pre-processing (cleaning, formatting, etc) the dataset. Let’s now go ahead to build our TensorFlow model to help suggest near-perfect used car prices.

You’ve got a used car you’d like to sell. You know the acquired price, but how do you measure how much a fair sale price would be based on how much it has been used? Let’s build a TensorFlow model to help suggest near-perfect used car prices.

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.

We showed in the last post what tensors are, what the ‘flow’ means, and how they are represented in TensorFlow. Here, we’ll do something much more exciting.

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.

For me, it usually is a drag writing about experiences, but being a GCI Mentor for TensorFlow is an experience I want to pin for keep and make timely reference to.

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.

I spent the final days of 2019 scrapping all Cowrywise’s tweets from January through December in 2019, taking special consideration for such high trending hashtagged tweets as #CowrywiseRoundUp, #SimplifiedbyCowrywise, and #Cowrywise itself.

Personally, the musical movie, “The Greatest Showman”, led by Hugh Jackman, Zac Efron, Zendaya, and Michelle Williams, and released in December 2017, is one of the best movies I have ever seen.