https://pipflow.com/forum/ Tue, 05 May 2026 02:10:13 +0000 MyBB https://pipflow.com/forum/Thread-Machine-learning-with-FPGA Tue, 10 Oct 2017 21:42:29 +0000 waldo]]> https://pipflow.com/forum/Thread-Machine-learning-with-FPGA
  https://www.youtube.com/watch?v=3iCifD8gZ0Q]]>
  https://www.youtube.com/watch?v=3iCifD8gZ0Q]]> https://pipflow.com/forum/Thread-PODRIA-SER-MUYYY-INTERESANTE Fri, 15 Sep 2017 20:54:22 +0000 jenrique42]]> https://pipflow.com/forum/Thread-PODRIA-SER-MUYYY-INTERESANTE
https://nips.cc/Conferences/2017]]>
https://nips.cc/Conferences/2017]]> https://pipflow.com/forum/Thread-Deep-Neural-Networks-Ebook Thu, 18 May 2017 20:13:40 +0000 waldo]]> https://pipflow.com/forum/Thread-Deep-Neural-Networks-Ebook Acknowledgements
Notation

1 Introduction
Part I: Applied Math and Machine Learning Basics
2 Linear Algebra
3 Probability and Information Theory
4 Numerical Computation
5 Machine Learning Basics

Part II: Modern Practical Deep Networks
6 Deep Feedforward Networks
7 Regularization for Deep Learning
8 Optimization for Training Deep Models
9 Convolutional Networks
10 Sequence Modeling: Recurrent and Recursive Nets
11 Practical Methodology
12 Applications


Part III: Deep Learning Research
13 Linear Factor Models
14 Autoencoders
15 Representation Learning
16 Structured Probabilistic Models for Deep Learning
17 Monte Carlo Methods
18 Confronting the Partition Function
19 Approximate Inference
20 Deep Generative Models

Bibliography
Index

http://www.deeplearningbook.org]]> Acknowledgements
Notation

1 Introduction
Part I: Applied Math and Machine Learning Basics
2 Linear Algebra
3 Probability and Information Theory
4 Numerical Computation
5 Machine Learning Basics

Part II: Modern Practical Deep Networks
6 Deep Feedforward Networks
7 Regularization for Deep Learning
8 Optimization for Training Deep Models
9 Convolutional Networks
10 Sequence Modeling: Recurrent and Recursive Nets
11 Practical Methodology
12 Applications


Part III: Deep Learning Research
13 Linear Factor Models
14 Autoencoders
15 Representation Learning
16 Structured Probabilistic Models for Deep Learning
17 Monte Carlo Methods
18 Confronting the Partition Function
19 Approximate Inference
20 Deep Generative Models

Bibliography
Index

http://www.deeplearningbook.org]]> https://pipflow.com/forum/Thread-Differentiable-Neural-Computers-Neural-Networks-with-external-memory Wed, 12 Apr 2017 06:52:02 +0000 waldo]]> https://pipflow.com/forum/Thread-Differentiable-Neural-Computers-Neural-Networks-with-external-memory but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to
the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer
(DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the
random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent
and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained
with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate
reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest
path between specified points and inferring the missing links in randomly generated graphs, and then generalize these
tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC
can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together,
our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural
networks without external read–write memory.

http://www.nature.com/articles/nature201...qJ9TfJWBqz]]> but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to
the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer
(DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the
random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent
and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained
with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate
reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest
path between specified points and inferring the missing links in randomly generated graphs, and then generalize these
tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC
can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together,
our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural
networks without external read–write memory.

http://www.nature.com/articles/nature201...qJ9TfJWBqz]]> https://pipflow.com/forum/Thread-Design-and-Implementation-of-Neural-Network-in-FPGA-Article Tue, 11 Apr 2017 21:36:49 +0000 waldo]]> https://pipflow.com/forum/Thread-Design-and-Implementation-of-Neural-Network-in-FPGA-Article lookup table as in most researches, to perform an efficient NN. It consist of two main parts; the first part covers network training using MATLAB program, the second part represents the
hardware implementation of the trained network through Xilinx high performance Virtex2 FPGA schematic entry design tools.

  neural network FPGA.pdf (Tamaño: 696.85 KB / Descargas: 8) ]]> lookup table as in most researches, to perform an efficient NN. It consist of two main parts; the first part covers network training using MATLAB program, the second part represents the
hardware implementation of the trained network through Xilinx high performance Virtex2 FPGA schematic entry design tools.

  neural network FPGA.pdf (Tamaño: 696.85 KB / Descargas: 8) ]]>