pmigdal + deep-learning   338

[D] Have we overfit to ImageNet? : MachineLearning
Just came across yet another architecture search paper: Everyone is treating .1% improvements as...
overfitting  imagenet  deep-learning 
18 days ago by pmigdal
Capture the Flag: the emergence of complex cooperative agents | DeepMind
Recent progress in artificial intelligence through reinforcement learning (RL)
has shown great success on increasingly complex single-agent environments and two-player turn-based games. However, the real world contains multiple agents, each learning and acting independently to cooperate and compete with other agents, and environments reflecting this degree of complexity remain an open challenge. In this work, we demonstrate
for the first time that an agent can achieve human-level in ...
deep-learning  reinforcement-learning  ctf 
6 weeks ago by pmigdal
Identifying dog breeds using Keras
Modern deep learning architectures are becoming increasingly effective in various fields of artificial intelligence. One of these fields is image classi...
keras  transfer-learning  deep-learning 
7 weeks ago by pmigdal
quark0/darts: Differentiable architecture search for convolutional and recurrent networks
GitHub is where people build software. More than 28 million people use GitHub to discover, fork, and contribute to over 85 million projects.
deep-learning  pytorch 
7 weeks ago by pmigdal
Multi-GPU Framework Comparisons – Ilia Karmanov – Medium
Having previously examined a wide breadth of deep-learning frameworks, it was difficult to go into a lot of depth for each one. In this post I take Tensorflow, PyTorch, MXNet, Keras, and Chainer and…
deep-learning  gpu  performance  keras  pytorch  tensorflow  mxnet 
7 weeks ago by pmigdal
Don’t learn TensorFlow! Start with Keras or PyTorch instead |
Keras and PyTorch are both excellent choices for your first deep learning framework. Learn how they differ and which one will suit your needs better.
pytorch  keras  ternsorflow  deep-learning 
7 weeks ago by pmigdal
Papers with Code : the latest in machine learning
Papers with Code highlights trending ML research and the code to implement it.
machine-learning  publications  github  deep-learning 
8 weeks ago by pmigdal
Neural scene representation and rendering | DeepMind
We introduce the Generative Query Network (GQN), a framework within which machines learn to perceive their surroundings by training only on data obtained by themselves as they move around scenes. Much like infants and animals, the GQN learns by trying to make sense of its observations of the world around it. In doing so, the GQN learns about plausible scenes and their geometrical properties, without any human labelling of the contents of scenes.
deep-learning  2d  3d  rendering 
9 weeks ago by pmigdal
Model Zoo - Pretrained deep learning models for transfer learning, educational purposes, and more
ModelZoo curates and provides a platform for deep learning researchers to easily find pre-trained models for a variety of platforms and uses. Find models that you need, for educational purposes, transfer learning, or other uses.
deep-learning  models  pretrained 
9 weeks ago by pmigdal
bonlime/keras-deeplab-v3-plus: Keras implementation of Deeplab v3+ with pretrained weights
GitHub is where people build software. More than 27 million people use GitHub to discover, fork, and contribute to over 80 million projects.
image-segmentation  deep-learning 
11 weeks ago by pmigdal
[D] How impactful could the choice of the Optimizer in NN be ? : MachineLearning
Hi, I was training a simple fully connected NN recently (on keras), and was stuck at a certain accuracy (45%) using...
adam  optimizer  deep-learning 
12 weeks ago by pmigdal
Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention) – Jay Alammar – Visualizing machine learning one concept at a time
Sequence-to-sequence models are deep learning models that have achieved a lot of success in tasks like machine translation, text summarization, and image captioning. Google Translate started using such a model in production in late 2016. These models are explained in the two pioneering papers (Sutskever et al., 2014, Cho et al., 2014).

I found, however, that understanding the model well enough to implement it requires unraveling a series of concepts that build on top of each other. ...
rnn  data-visualization  deep-learning  nlp 
may 2018 by pmigdal
GauravBh1010tt/DeepLearn: Implementation of research papers on Deep Learning+ NLP+ CV in Python using Keras, Tensorflow and Scikit Learn.
GitHub is where people build software. More than 27 million people use GitHub to discover, fork, and contribute to over 80 million projects.
deep-learning  nlp  keras 
april 2018 by pmigdal
LouieYang/stroke-controllable-fast-style-transfer: Code and data for paper:
GitHub is where people build software. More than 27 million people use GitHub to discover, fork, and contribute to over 80 million projects.
neural-style  deep-learning 
april 2018 by pmigdal
junxiaosong/AlphaZero_Gomoku: An implementation of the AlphaZero algorithm for Gomoku (also called Gobang or Five in a Row)
GitHub is where people build software. More than 28 million people use GitHub to discover, fork, and contribute to over 79 million projects.
reinforcement-learning  pytorch  alpha-go  deep-learning 
march 2018 by pmigdal
The GAN Zoo – Deep Hunt
Every week, new papers on Generative Adversarial Networks (GAN) are coming out and it’s hard to keep track of them all, not to mention the incredibly creative ways in which researchers are naming…
deep-learning  gan 
february 2018 by pmigdal
A guide to receptive field arithmetic for Convolutional Neural Networks
The receptive field is perhaps one of the most important concepts in Convolutional Neural Networks (CNNs) that deserves more attention from the literature. All of the state-of-the-art object…
convolution  deep-learning 
february 2018 by pmigdal
Hyperparticle/one-pixel-attack-keras: Keras reimplementation of "One pixel attack for fooling deep neural networks" using differential evolution on cifar10
one-pixel-attack-keras - Keras reimplementation of "One pixel attack for fooling deep neural networks" using differential evolution on cifar10
deep-learning  keras  hacking-dl 
february 2018 by pmigdal
How To Create A Machine Learning Framework From Scratch In 491 steps
Note: We already posted a a short post-mortem of this project on reddit about 4 months ago. This is a significantly expanded, more detailed and polished version with detailed insights, design choices…
deep-learning  programming  education2 
february 2018 by pmigdal
How I Shipped a Neural Network on iOS with CoreML, PyTorch, and React Native - Stefano J. Attardi
UI Engineering and Design consultant, specializing in React and React performance. Previously at Facebook and Storehouse. Winner of the first Node.js Knockout with
pytorch  deep-learning  ios 
february 2018 by pmigdal
Machine learning mega-benchmark: GPU providers (part 2) | RaRe Technologies
We had recently published a large-scale machine learning benchmark using word2vec, comparing several popular hardware providers and ML frameworks in pragmatic aspects such as their cost, ease of use, stability, scalability and performance.
gpu  aws  deep-learning 
february 2018 by pmigdal
"Grad Student Descent" - [D] How do people come up with all these crazy deep learning architectures? : MachineLearning
For the past few days, I've been reading TensorFlow source codes for some of the latest DL architectures (e.g. Tacotron, Wavenet) and the more I...
february 2018 by pmigdal
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