Gradio vs Streamlit vs Dash vs Flask
IntroductionMachine learning models are exciting and powerful, but they aren’t very useful by themselves. Once a model is complete, it likely has to be deployed before it can deliver any sort of value. As well, being able to deploy a preliminary model or a prototype to get feedback from other stakeholders is extremely useful. Recently, there has been an emergence of several tools that Data Scienti
ML Ops: Machine Learning as an Engineering Discipline
So, your company decided to invest in machine learning. You have a talented team of Data Scientists churning out models to solve important problems that were out of reach just a few years ago. All performance metrics are looking great, the demos cause jaws to drop and executives to ask how soon you can have a model in production. It should be pretty quick, you think. After all, you already solved
Simple Transformers — Named Entity Recognition with Transformer Models
PrefaceThe Simple Transformers library was conceived to make Transformer models easy to use. Transformers are incredibly powerful (not to mention huge) deep learning models which have been hugely successful at tackling a wide variety of Natural Language Processing tasks. Simple Transformers enabled the application of Transformer models to Sequence Classification tasks (binary classification initia
Building a Question-Answering System from Scratch— Part 1
First part of the series focusses on Facebook Sentence Embedding As my Masters is coming to an end, I wanted to work on an interesting NLP project where I can use all the techniques(not exactly) I have learned at USF. With the help of my professors and discussions with the batch mates, I decided to build a question-answering model from scratch. I am using the Stanford Question Answering Dataset (S
Dealing with Imbalanced Classes in Machine Learning
IntroductionMost real-world classification problems display some level of class imbalance, which is when each class does not make up an equal portion of your data-set. It is important to properly adjust your metrics and methods to adjust for your goals. If this is not done, you may end up optimizing for a meaningless metric in the context of your use case. For example, suppose you have two classes
Understanding Learning Rates and How It Improves Performance in Deep Learning
This post is an attempt to document my understanding on the following topic: What is the learning rate? What is it’s significance?How does one systematically arrive at a good learning rate?Why do we change the learning rate during training?How do we deal with learning rates when using pretrained model?Much of this post are based on the stuff written by past fast.ai fellows [1], [2], [5] and [3] .
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