pmigdal + deep-learning   338

[D] Have we overfit to ImageNet? : MachineLearning
Just came across yet another architecture search paper: https://arxiv.org/abs/1712.00559 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 | deepsense.ai
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: https://arxiv.org/abs/1802.07101
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 Swarmation.com.
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...
deep-learning 
february 2018 by pmigdal
« earlier      
per page:    204080120160

related tags

2d  3d  3d-conv  academia  acoustics  adam  adversarial  ai  alpha-go  ami  analogies  analogy  architecture  arrhythmia  art  atrial-fibrillation  autoencoder  aws  backpropagation  basic-income  batch-norm  bayesian  beauty  benchmark  binary  biology  book  bot  caffe  cancer  capsnet  captcha  career  cars  cats  cell  cervix  char2char  cheatsheet  chemistry  cloud-computing  cnn  cognition  cognitive  collab  coloring  complex  compression  confusion-matrix  convnet  convolution  course  cpu  ctf  curiosity  data  data-art  data-augmentation  data-visualization  data-viz  dataset  data_visualization  debugging  decision-tree  deep-dream  deep-learning  deepdream  deepsense  demography  diagram  differentation  differential  doom  drawing  dream  dropout  ecg  economy  ecs  education2  evolution  expression  eye  face  faceswap  facial-recognition  flow  fluid  font  forecasting  fourier  functional  gamedev  gan  github  go  google  gpu  gradient-descent  grammar  graph  group-theory  gru  gui  hacking-dl  health  heart  heatmap  history  hn  human-learning  humour  hype  illusion  image-classification  image-detection  image-generation  image-processing  image-recognition  image-segmentation  imagenet  install  interactive  interpolation  interpretation  invariance  ios  ipython-notebook  javascript  journal  jupyter  jupyter-notebook  kaggle  keras  l2  language  lasagne  latent  latent-variable  latex  learning-rate  linear-algebra  log-loss  lstm  lua  lung  machine-learning  machinelearning  mario  mask-rcnn  maths  matrix  media  medicine  meme  meta-learning  metaphor  metropolis  microscopy  models  mri  mushroom  music  mxnet  network-visualization  neural-network  neural-networks  neural-style  neuroscience  news  nlp  nsfw  nude  nudity  object-detection  openscience  optimization  optimizer  overfitting  painting  papers  performance  philosophy  photography  photos  physics  pokemon  porn  pretrained  production  programming  progress  pronoun  prototypical  publications  pymc3  python  pytorch  pytroch  q-learning  quantum  rcnn  reddit  reinforcement-learning  relu  rendering  repo  resnet  resources  retina  rmsprop  rnn  saliency  sarcasm  scala  science  sdg  segmentation  selfie  sentiment-analysis  seq2seq  society  sociology  sound  spacy  spectral  starcraft  starcraft2  symmetry  tensor  tensorboard  tensorflow  ternsorflow  test  theano  torch  transfer-learning  translation  tree  trends  turing  tutorial  typography  u-net  uber  unet  unsupervised  upsampling  upscaling  ux  version-control  vgg  video  vision  visualisation  visualization  webdesign  webdev  webgl  whale  whales  word2vec  word2wec  xgboost  yolo 

Copy this bookmark:



description:


tags: