Category Archives: Uncategorized

An OpenCV tutorial in Python Previously, I discussed color spaces and processes used to enhance or restore images. In most real world scenarios, data is rarely perfect, and that goes for images as well. Whether it’s lighting, pixelation or some other visual anomoly, these instances must be accounted for in the preprocessing stages. I will,Continue reading “Blur & Mask Processes”

Image Restoration & Enhancement with Python using OpenCV & Numpy Machine learning and AI have come a long way with regards to processing images. From visualization, pattern recognition, image restoration, graphics sharpening and search retrieval, it’s become a part of every day life for most primates with a smart phone. For those who directly implementContinue reading “Dive Into Digital Image Preprocessing Techniques”

Digital Image Preprocessing for AI & Machine Learning with OpenCV & Python OpenCV reads processes images in BGR(blue, green, red) format rather than RGB(red, green, blue), which is how computers process images. This is due to the use of BGR in DSLR cameras at the time. This ‘process’ is called sub pixel layout, and theContinue reading “OpenCV: Color Space”

Images, whether it be a photo, drawing, design element or frame of video in an infinite array of formats(codecs, color maps, sizes, compressions, etc.), well, up to the bounds of technology, of course, but the potential is there. Frequently, alterations or manipulations are used to change the visual perception of the image. Adobe has madeContinue reading “Visual Perception with OpenCV & Python”

OpenCV aka Open Source Computer Vision Library is written in C++, but includes bindings for Python, Java and MATLAB. Additional wrappers are available from third parties to utilize the broad spectrum of tools in multiple languages. OpenCV is used for image and video processing, analysis, and manipulation. OpenCV contains tools for segmentation, object detection, facialContinue reading “OpenCV & Understanding Image Data”

Word2Vec models can be either a Continuous Bag of Words model, or a Skipgram model. The models differ in their methods though. CBOW models are quicker to train and they predict a target word based on all of the surrounding words. The context vectors from neighboring words are used to predict the target word. TheContinue reading “Gensim Word2Vec Models SkipGram & Continuous Bag of Words”

Once the text has been scrubbed, tokenized and stemmed, there is additional information worth extracting. Bigrams are recurring pairs of words that occur in the same order in a dataset, which can be a general corpus, like Text8Corpus or a built-in NLTK corpus. When I model my data, I will use a binary classification method,Continue reading “Bigrams from Word2Vec”

Working on an NLP task, there are times when you need to be able to visualize representation of tweets. This can be super useful in classification tasks. Using Gensim’s Doc2Vec, and the following packages: I will be creating word vectors, training them and then plotting the word vector as an image. I am using aContinue reading “Visualize Tweets”

Bag of Words to Term Frequency-Inverse Document Frequency and how it’s used in Natural Language Processing tasks. When working on a dataset composed of words, first, the data is cleaned up, which makes up roughly 80 percent of the time spent on a given project. Every dataset is different as far as what steps areContinue reading “BoW to TF-IDF”

Let me begin by saying, yes, the above spelling error is intentional, I know that ‘wrods’ is not how you spell ‘words’. The point of this is that it is very easy to mistype or incorrectly spell words when entering data, or when the data itself is subject to misspellings through user error. This isContinue reading “NLP: Misspelled Wrods”