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Visualizing Model Insights: A Guide to Grad-CAM in Deep Learning

Analytics Vidhya

Introduction Gradient-weighted Class Activation Mapping is a technique used in deep learning to visualize and understand the decisions made by a CNN. This groundbreaking technique unveils the hidden decisions made by CNNs, transforming them from opaque models into transparent storytellers.

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Visualize Deep Learning Models using Visualkeras

Analytics Vidhya

Image Source: Author Introduction Deep learning, a subset of machine learning, is undoubtedly gaining popularity due to big data. Startups and commercial organizations alike are competing to use their valuable data for business growth and customer satisfaction with the help of deep learning […].

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Train PyTorch Models Scikit-learn Style with Skorch

Analytics Vidhya

Introduction Embark on a thrilling journey into the domain of Convolutional Neural Networks (CNNs) and Skorch, a revolutionary fusion of PyTorch’s deep learning prowess and the simplicity of scikit-learn. Join us […] The post Train PyTorch Models Scikit-learn Style with Skorch appeared first on Analytics Vidhya.

Modeling 274
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FPGA vs. GPU: Which is better for deep learning?

IBM Big Data Hub

Underpinning most artificial intelligence (AI) deep learning is a subset of machine learning that uses multi-layered neural networks to simulate the complex decision-making power of the human brain. Deep learning requires a tremendous amount of computing power.

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Why you should care about debugging machine learning models

O'Reilly on Data

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. Not least is the broadening realization that ML models can fail. And that’s why model debugging, the art and science of understanding and fixing problems in ML models, is so critical to the future of ML.

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Rapidminer Platform Supports Entire Data Science Lifecycle

David Menninger's Analyst Perspectives

Rapidminer is a visual enterprise data science platform that includes data extraction, data mining, deep learning, artificial intelligence and machine learning (AI/ML) and predictive analytics. It can support AI/ML processes with data preparation, model validation, results visualization and model optimization.

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Understanding Overfitting in ConvNets

Analytics Vidhya

Introduction Overfitting in ConvNets is a challenge in deep learning and neural networks, where a model learns too much from training data, leading to poor performance on new data. This phenomenon is especially prevalent in complex neural architectures, which can model intricate relationships.