Deep Learning on DataThrillz

A Machine Learning Engineer’s Top 5 Predictions for the Future of Generative AI

Wed, 25 Jan 2023 12:30:00 +0000

What is GenAI?

Generative AI (GenAI) empowers end-users to generate content, such as images and text, quickly and easily. Entrepreneurs are taking advantage of this technology to create a growing number of startups that utilize GenAI models for various aspects of content creation. In the coming year, we can expect to see a proliferation of new products that build on GenAI models like titans GPT-3 and Stable Diffusion. The GenAI renaissance is just beginning and the recent boom in niche end-user applications for this technology is just the tip of the iceberg. These models will serve as the foundation for many future applications ushering in a new GenAI-economy replete with add-ons to existing software and entirely new offerings for end-users. With GenAI, the possibilities for content creation are endless and entrepreneurs are poised to capitalize on this powerful technology to revolutionize the way we create and consume media.

Software Engineering as a Data Scientist

Sat, 20 Feb 2021 17:37:51 +0000

Many of us in Data Science come from math, biology, chemistry or engineering or other non-Computer Science backgrounds, which may mean that we don’t have much experience writing and maintaining large code bases. Recently, I found myself getting frustrated with the structure of some of my code and searching for a better way to structure my code base. Let’s explore some ways that we can improve our Software Engineering skills as Data Scientists!

Building Recommendations Systems

Mon, 07 Sep 2020 18:00:48 +0000

Recommendations systems are good for matching users to their favorite products and are incredibly popular. In fact you have likely used a recommendation system at least once in your life. For example, Amazon uses recommendation systems to suggest new exciting products to purchase based on users’ previous purchase patterns and those similar users. Netflix also utilizes recommendation systems to suggest new TV Shows and movies.

Before we get into recommendation systems, it is important to briefly cover two general-purpose approaches for identifying target customer groups and making product recommendations. These two approaches are called Clustering and Association Rules.

Recurrent Neural Networks in PyTorch

Mon, 07 Sep 2020 17:13:17 +0000

Feed forward networks cannot learn from the past, but Recurrent Neural Networks (RNNs) can learn by accepting data in a sequence. Examples of applications for RNNs include the text autocomplete feature on your phone and performing language translations.

Recurrent Neurons (RNs) act as the building blocks of RNNs. The difference between RNs and feed forward neurons is that RNs accept input x, at time t, as well as a hidden state or output from time t-1 from another RN. The output of a RN is a vector, unlike for a feed forward neuron. RNNs are trained (i.e. their weights are calculated) using backpropagation via Gradient Descent Optimization in time. Output from a single RNN layer at time instance,t is an input to the next layer. Each layer in the RNN represents an instance in time.

Fastai’s Practical Deep Learning for Coders Course Release - 2020 Update!

Thu, 03 Sep 2020 17:48:01 +0000

On Aug 21st, 2020, fastai released a new version of their Practical Deep Learning for Coders -Part 1 course. This course is a must-take for new and intermediate deep learning practitioners. It is well done and teaches you intuition without drowning you in theory. The only prerequisites are some high-school math, and a year of coding experience (preferably in Python). This course is free and can be done without any installation, by taking advantage of the Colab and/or Gradient platforms, which provide free, GPU-powered Python notebooks. Go here to learn more about the course.

Regression & Classification Models in PyTorch

Tue, 05 May 2020 23:41:00 +0000

The purpose of this article is to share what I learned from a recent PyTorch course. We will share general machine learning tips as well as insights specific to deep learning library PyTorch.

PyTorch is a deep learning library for Python and was created by Facebook in 2016. PyTorch is good for deep learning beginners. There are several other popular deep learning frameworks such as TensorFlow, Keras, Chainer, and ONNX. TensorFlow was developed by Google and now includes Keras (previously a separate framework). I chose to deepen my knowledge in PyTorch because it is easy to learn and is commonly used for deep learning. Fastai is another deep learning open source library that is really awesome and intuitive to use. While using Fastai, I found myself curious about the inner workings so it’s a plus that fastai wraps PyTorch as I can gain a better understanding of both libraries at once. Two birds, one stone!