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4 days ago · A neural network is a type of deep learning model within the broader field of machine learning (ML) that simulates the human brain. It processes data through interconnected nodes or neurons arranged in layers—input, hidden, and output.
3 days ago · Deep neural networks complete a feature extraction task by propagating the inputs through multiple modules. However, how the representations evolve with the gradient-based optimization remains unknown. Here we leverage the intrinsic dimension of the representations to study the learning dynamics and find that the training process undergoes a phase transition from expansion to compression under disparate training regimes. Surprisingly, this phenomenon is ubiquitous across a wide variety of ...
5 days ago · 4. Rapid Prototyping. Choosing the correct learning method or the algorithm are signs of a machine learning engineer’s good prototyping skills. These skills would be a great saviour in real time as they would show a huge impact on budget and time taken for successfully completing a machine learning project.
5 days ago · More classical Machine Learning (ML) models like tree-based ensemble ones usually perform better. This paper presents a novel DL model using Graph Neural Network (GNN) more specifically Interaction Network (IN), for contextual embedding and modeling interactions among tabular features. Its results outperform those of a recently published survey ...
