A new topic we are investigating with my company is Topic Generation and Classification. This is an extensive experimentation process on Topic Modelling and GPT-3.5/4 for qualitative analysis. I first start with topic modelling, how well can we actually describe topics using a simple BERTopic model, which currently is the state of the art model. We then investigate how strong this model actually is compared to our human experts. After discussing the weaknesses and strengths of this approach, we go ahead and investigate how GPT can help us improve this performence. ...

I recently had the opportunity to develop a sentiment analysis tool for my company. Although I had some prior experience in this area, I quickly realized that I had more to learn. After extensive research and experimentation, we achieved the desired results. In this post, I’ll share my journey, thought process, and the techniques I employed to meet our objectives. Identifying the Issue & Setting the Stage Our startup specializes in delivering top-notch qualitative coding services to businesses, presenting the results on a user-friendly dashboard for our clients. In an effort to better serve their needs, we decided to incorporate sentiment analysis as a key feature. ...

These are my notes on refreshing my object detection knowledge. We will start with bounding boxes for localization and cover everything we need before jumping in to implement YOLO algorithms. This tutorial includes answers to the following questions: What is localization? What are a bounding box and sliding window? How to measure the success of a predicted bounding box: Intersection over the union. How to get rid of extra bounding boxes: Non-max suppression. Evaluation Metric for Object Detection: Mean average precision Check references for addresses of the images. Object Detection Object Detection is finding objects in an image and where they are located in the image. Adding localization or location on detected objects for a classification task will give us object detection. ...

Github Repo | Full-code notebook In this post, I will work my way into basic Sentiment Analysis methods and experiment with some techniques. I will use the data from the IMDB review dataset acquired from Kaggle. We will be examining/going over the following: Data preprocessing for sentiment analysis 2 different feature representations: Sparse vector representation Word frequency counts Comparison using: Logistic regression Naive Bayes Feature Representation Your model will be, at most, as good as your data, and your data will be only as good as you understand them to be, hence the features. I want to see the most useless or naive approaches and agile methods and benchmark them for both measures of prediction success and for training and prediction time. ...

This post includes my notes from the lecture “Makemore Part 3: Activations & Gradients, BatchNorm” by Andrej Karpathy. Link of the video: https://www.youtube.com/watch?v=P6sfmUTpUmc Initialization Fixing the initial Loss: Initial loss must be arranged (the value depends on the question), in our case its a uniform probability. When initializing make sure the numbers do not take extreme values (* .01) Do not initialize to 0 Having 0 and 1 in softmax (a lot of them) is really bad, since the gradient will be 0 (vanishing gradient). This is called saturated tanh. So in summary: What we did is basically just making sure initially we give the network random values such that it is varied, and not making gradients 0 (no dead neurons). In the case of tanh, when we use softmax to squash the values, if the initial values were too broad, we will get a lot of vanishing gradients due to values ending up above 1 or below -1. So we first reduce the initial values and then use softmax on them (and continue training process). Kaiming Init Okay we know how to fix initialization now, but how much should we reduce these numbers? Meaning what is the value we should scale the layers with. Here comes Kaiming init. ...