I prefer Option 2 and take that approach to learn any new topic. I might not be able to tell you the entire math behind an algorithm, but I can tell you the intuition. I can tell you the best scenarios to apply an algorithm based on my experiments and understanding. In my interactions...
This is the neuron model behind perceptron layers (also called dense layers), which are present in the majority of neural networks. In this post, we explain the mathematics of the perceptron neuron model: Perceptron elements. Neuron parameters. Combination function. Activation function. Output ...
While materials synthesis methods have seen advancements in speed and efficiency, characterization techniques have lagged behind. Here, authors design automated computer vision algorithms to accurately characterize hundreds of materials in minutes. Alexander E. Siemenn , Eunice Aissi & Tonio Buonassisi Ar...
Deep neural networks Summary Regularization The need for regularization Norm penalties L2 regularization L1 regularization Early stopping Parameter tying and sharing Dataset augmentation Dropout Adversarial training Summary Convolutional Neural Networks The inspiration behind ConvNets Types of data used in ConvNet...
Predictive machine learning models, while powerful, are often seen as black boxes. Here, the authors introduce a thermodynamics-inspired approach for generating rationale behind their explanations across diverse domains based on the proposed concept of interpretation entropy. ...
Deep brain stimulation modulates the dynamics of resting-state networks in patients with Parkinson's Disease Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is increasingly used for the treatment of Parkinson's Disease (PD), but despite its success, the neural mechanisms behind this...
Behind recent advances in machine learning, data science and artificial intelligence are fundamental statistical principles. The purpose of this class is to develop and understand these core ideas on firm mathematical grounds starting from the construction of estimators and tests, as well as an ...
This free book will teach you the core concepts behind neural networks and deep learning. Neural networks and deep learning currently provide the best solutions to many problems in image recognition, speech recognition, and natural language processing. -
Deep neural networks Summary Regularization The need for regularization Norm penalties L2 regularization L1 regularization Early stopping Parameter tying and sharing Dataset augmentation Dropout Adversarial training Summary Convolutional Neural Networks The inspiration behind ConvNets Types of...
In his analysis, Kirchhoff made two assumptions about the values of the complex amplitude (U) and its normal derivative (∂ U / ∂n) immediately behind the z =0 plane. These assumptions, known as Kirchhoff's assumptions, are (1) for the points on the aperture [i.e., (x1, y1) ...