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Getting started with Deep Learning? Here’s a quick guide explaining everything at a place!

Analytics Vidhya

In this blog, I’ll provide a brief rundown of. The post Getting started with Deep Learning? Here’s a quick guide explaining everything at a place! ArticleVideo Book This article was published as a part of the Data Science Blogathon. appeared first on Analytics Vidhya.

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AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the difference?

IBM Journey to AI blog

While artificial intelligence (AI), machine learning (ML), deep learning and neural networks are related technologies, the terms are often used interchangeably, which frequently leads to confusion about their differences. This blog post will clarify some of the ambiguity. Machine learning is a subset of AI.

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How to Explain Black-Box Deep Learning Models in Computer Vision and NLP

Towards AI

Explaining a black box Deep learning model is an essential but difficult task for engineers in an AI project. Image by author When the first computer, Alan Turings machine, appeared in the 1940s, humans started to struggle in explaining how it encrypts and decrypts messages. This member-only story is on us.

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Training a CNN from Scratch using Data Augmentation

Analytics Vidhya

Introduction My last blog discussed the “Training of a convolutional neural network from scratch using the custom dataset.” ” In that blog, I have explained: how to create a dataset directory, train, test and validation dataset splitting, and training from scratch. This blog is […].

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Generative AI vs. predictive AI: What’s the difference?

IBM Journey to AI blog

Most generative AI models start with a foundation model , a type of deep learning model that “learns” to generate statistically probable outputs when prompted. Conversely, predictive AI estimates are more explainable because they’re grounded on numbers and statistics. appeared first on IBM Blog.

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How to Visualize Deep Learning Models

The MLOps Blog

Deep learning models are typically highly complex. While many traditional machine learning models make do with just a couple of hundreds of parameters, deep learning models have millions or billions of parameters. The reasons for this range from wrongly connected model components to misconfigured optimizers.

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Photogrammetry Explained: From Multi-View Stereo to Structure from Motion

PyImageSearch

This blog post is the 1st of a 3-part series on 3D Reconstruction: Photogrammetry Explained: From Multi-View Stereo to Structure from Motion (this blog post) 3D Reconstruction: Have NeRFs Removed the Need for Photogrammetry? To learn about 3D Reconstruction, just keep reading. So how does that work? Have you felt it?