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PyTorch & TensorFlow Both of these are open-source deep learning frameworks that have been extremely popular in the ML and AI field. Each has its own benefits and can be used by people with different preferences. For example, TensorFlow was developed by Google (2015), while PyTorch was developed by Facebook (their AI research lab known as FAIR)(2016). TensorFlow is larger with more of a community, while PyTorch has been growing in popularity due to its library of resources currently being developed. Another aspect of why PyTorch has been on the rise is its ease of debugging, where the original TensorFlow was much harder to debug (now easier with version 2.0). Although when it comes to overall performance, TensorFlow is the faster of the two and can utilize performance optimizations. This isn't to say PyTorch is slow, as it's more than capable, but when comparing the two, Google has them beat. At the end of the day, trying both options for yourself will help you better under...

PyTorch Pytorch is an open-source ML library associated with fast.ai and developed with Facebook's AI research lab, FAIR. Tensors are the building blocks that store multi-dimensional arrays of numerical data, similar to NumPy but leveraged with graphics processing power (GPU). To create a tensor, use the torch.tensor() function. Using Google Colab, it would look something like this:

NPL Natural Language Processing (NLP) is a branch of AI that focuses on understanding how humans communicate—hence the name. NLP is particularly valuable in industries that rely heavily on qualitative analysis. For instance, in my current role in market research, we conduct interviews with foodservice professionals, such as chefs and food scientists, to gather their feedback on various products. Capturing and analyzing all of that information manually is time-consuming and often overwhelming, but NLP significantly streamlines the process. We use a program called TurboScribe to transcribe each interview within minutes, though there are many similar tools available. One feature we appreciate most about TurboScribe is its ability to identify individual speakers and organize the transcript into clear sections, rather than producing a continuous block of text. It also allows us to click on any part of the transcript and hear the corresponding audio, perfectly synced, which makes verifying a...

YouTube TV Problem & AI Fix I was able to use AI to fix a real-world problem with my YouTube TV subscription. The problem I was having was that every time I would open YouTube TV, there would be two scrollbars on the right-hand side of the screen. Anyone familiar with the application understands that it's used to navigate to different content, etc. Except, the problem would persist when I enter full-screen mode. As the owner of an OLED monitor, I’m very aware of the risk of image burn-in, a well-known issue with OLED technology. Burn-in, also known as image retention, occurs when a static image remains on the screen for an extended period, causing certain pixels to degrade faster than others. This happens because OLED displays use individually lit pixels to produce images, unlike traditional LCD screens that rely on a uniform backlight. While OLEDs are capable of delivering stunning visuals with deep blacks and extremely high contrast ratios, making them ideal for watching movi...