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14 Σεπ 2021 · In the present work we study a broad collection of deep learning and probabilistic based models in the light of password guessing: attention-based deep neural networks, autoencoding mechanisms and generative adversarial networks. We provide novel generative deep-learning models in terms of variational autoencoders exhibiting state-of-art ...
7 Σεπ 2021 · In the present work we study a broad collection of deep learning and probabilistic based models in the light of password guessing: attention-based deep neural networks, autoencoding mechanisms and generative adversarial networks. We provide novel generative deep-learning models in terms of variational autoencoders exhibiting state-of-art ...
In these tutorials, I cover topics such as forward propagation, activation functions, loss functions, and backpropagation. I also walk you through the process of building a neural network from scratch and demonstrate how to train and make predictions with it.
7 Σεπ 2021 · In the present work we study a broad collection of deep learning and probabilistic based models in the light of password guessing: attention-based deep neural networks, autoencoding...
The scope of this teaching package is to make a brief induction to Artificial Neural Networks (ANNs) for people who have no previous knowledge of them. We first make a brief introduction to models of networks, for then describing in general terms ANNs.
31 Μαρ 1996 · Artificial neural nets (ANNs) are massively parallel systems with large numbers of interconnected simple processors. The article discusses the motivations behind the development of ANNs and describes the basic biological neuron and the artificial computational model.
If you want to start tinkering with code, feel free to pick up from the intro tutorial and teach a neural network how to detect handwritten digits. You should also continue your education by learning the theoretical and mathematical underpinnings of the concepts we discussed here.
