Adit Deshpande
Gregory Piatetsky
Los Angeles, CA, United States
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Past articles by Adit:
Deep Learning Research Review: Natural Language Processing
Introduction to Natural Language Processing Introduction Natural language processing (NLP) is all about creating systems that process or “understand” language in order to perform certain tasks. These tasks could include * Question Answering (What Siri, Alexa, and Cortana do) * Sentiment Analysis (Determining whether a sentence has a positive or negative connotation) * Image to Text Mappings… → Read More
A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1
Introduction Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but these networks have been some of the most influential innovations in the field of computer vision. 2012 was the first year that neural nets grew to prominence as Alex Krizhevsky used them to win that year’s ImageNet competition (basically, the annual Olympics of… → Read More
Deep Learning Research Review: Natural Language Processing
Introduction to Natural Language Processing Introduction Natural language processing (NLP) is all about creating systems that process or “understand” language in order to perform certain tasks. These tasks could include * Question Answering (What Siri, Alexa, and Cortana do) * Sentiment Analysis (Determining whether a sentence has a positive or negative connotation) * Image to Text Mappings… → Read More
Deep Learning Research Review: Reinforcement Learning
This is the 2nd installment of a new series called Deep Learning Research Review. Every couple weeks or so, I’ll be summarizing and explaining research papers in specific subfields of deep learning. This week focuses on Reinforcement Learning. Last time was Generative Adversarial Networks ICYMI Introduction to Reinforcement Learning 3 Categories of Machine Learning Before getting into the… → Read More
9 Key Deep Learning Papers, Explained
Introduction In this post, we’ll go into summarizing a lot of the new and important developments in the field of computer vision and convolutional neural networks. We’ll look at some of the most important papers that have been published over the last 5 years and discuss why they’re so important. The first half of the list (AlexNet to ResNet) deals with advancements in general network… → Read More
How I Used Deep Learning To Train A Chatbot To Talk Like Me
Introduction Chatbots are “computer programs which conduct conversation through auditory or textual methods”. Apple’s Siri, Microsoft’s Cortana, Google Assistant, and Amazon’s Alexa are four of the most popular conversational agents today. They can help you get directions, check the scores of sports games, call people in your address book, and can accidently make you order a $170 dollhouse.… → Read More
Perform sentiment analysis with LSTMs, using TensorFlow
Explore a highly effective deep learning approach to sentiment analysis using TensorFlow and LSTM networks. → Read More
Generative Adversarial Networks for Beginners
Build a neural network that learns to generate handwritten digits. → Read More
A Beginner’s Guide To Understanding Convolutional Neural Networks Part 1
Introduction Convolutional neural networks. Sounds like a weird combination of biology and math with a little CS sprinkled in, but these networks have been some of the most influential innovations in the field of computer vision. 2012 was the first year that neural nets grew to prominence as Alex Krizhevsky used them to win that year’s ImageNet competition (basically, the annual Olympics of… → Read More