Typeset in Times New Roman by Radiant Productions\nDedication\nTo all those deep learning algorithms helping humanity Preface Deep learning is an artificially intelligent entity that teaches itself to make predictions following a training phase through an intensive data driven algorithm. Deep learning, ...
The choice of algorithm depends on the nature of the data. Many algorithms and techniques aren't limited to a single type of ML; they can be adapted to multiple types depending on the problem and data set. For instance, deep learning algorithms such asconvolutional and recurrent neural network...
Demonstrates use of the dl4j transfer learning API which allows users to construct a model based off an existing model by modifying the architecture, freezing certain parts selectively and then fine tuning parameters. Read the documentation for the Transfer Learning API athttps://deeplearning4j.kondu...
More information on the gradient sharing algorithm can be found here GradientSharingVGG16TinyImageNet.java Gradient sharing with VGG16 on TinyImageNet ADVANCED ImdbReviewClassificationRNN.java A multiple gpus version of the example of the same name in the dl4j-examples repo here This example also ...
While all of the above is good and great, is it enough? For those who want to know more, you can get a little more technical, while still using the previous tips as a foundation. For instance, as mentioned, machine learning is all about training an algorithm. But, to go further, in...
Artificial Intelligence vs. Machine Learning vs. Deep Learning Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)are often used interchangeably, but they have distinct characteristics and applications. The table below provides a comprehensive comparison of these technologies, hig...
What is YOLO architecture and how does it work? Learn about different YOLO algorithm versions and start training your own YOLO object detection models.
Use a white-box algorithm like the fast gradient sign to generate adversarial examples for the substitute model. Many of them are going to transfer successfully and become adversarial examples for the target model as well. A successful application of this strategy against a commercial Machine learni...
Reinforcement Learning ML - Reinforcement Learning Algorithms ML - Exploitation & Exploration ML - Q-Learning ML - REINFORCE Algorithm ML - SARSA Reinforcement Learning ML - Actor-critic Method ML - Monte Carlo Methods ML - Temporal Difference Deep Reinforcement Learning ML - Deep Reinforcement Learnin...
of additional hyperparameters, such as example mining schedules and regularization hyperparameters. In contrast to past reweighting methods, which typically consist of functions of the cost value of each example, in this work we propose a novel meta-learning algorithm that learns to assign weights ...