ahmedbesbes

23 ms·

31.
Visualizing Class Activation Maps to Make CNNs Interpretable in Medical Imaging(ahmedbesbes.com)
2 points by ahmedbesbes 7y ago|0 comments
32.
How to detect knee injuries from MRI exams using ConvNets in PyTorch(ahmedbesbes.com)
1 points by ahmedbesbes 7y ago|0 comments
33.
PyTorch Implementation of MRNet: a model for knee injury classification(github.com)
1 points by ahmedbesbes 7y ago|0 comments
34.
MRI classification using PyTorch for knee injuries detection(ahmedbesbes.com)
1 points by ahmedbesbes 7y ago|0 comments
35.
Automatic Diagnosis of Knee Injuries with Deep Learning in PyTorch(ahmedbesbes.com)
1 points by ahmedbesbes 7y ago|0 comments
36.
Quora Insincere Questions Classification: Two Notebook Tutorials in PyTorch(github.com)
2 points by ahmedbesbes 7y ago|0 comments
37.
by ahmedbesbes 8y ago
PyTorch implementation Tensorboard metric plots Trained models Documentation Annex video tutorial
38.
Introduction to Character Based CNN and PyTorch Code and Trained Model and Viz(youtube.com)
1 points by ahmedbesbes 8y ago|0 comments
39.
Character Based CNNs for text classification in PyTorch and trained model+ viz(github.com)
2 points by ahmedbesbes 8y ago|1 comments
40.
Automatic image captioning with visual attention using PyTorch(youtube.com)
6 points by ahmedbesbes 8y ago|0 comments
41.
Demo: Automatic image captioning with visual attention using PyTorch(youtube.com)
1 points by ahmedbesbes 8y ago|0 comments
42.
Fast Neural Style Transfer demo. Turn your pictures into artwork(youtube.com)
1 points by ahmedbesbes 8y ago|0 comments
43.
Build your neural network from scratch using Numpy, with no framework involved(github.com)
19 points by ahmedbesbes 8y ago|0 comments
44.
Comparison of tranditional and deep learning models in text classification(ahmedbesbes.com)
2 points by ahmedbesbes 8y ago|0 comments
45.
Seven NLP models for tweet classification:Bag of words and deep learning based(ahmedbesbes.com)
1 points by ahmedbesbes 8y ago|0 comments
46.
Benchmark of traditional and deep learning models in text classification(ahmedbesbes.com)
1 points by ahmedbesbes 8y ago|0 comments
47.
How to Score 0.8134 in Titanic Kaggle Challenge(ahmedbesbes.com)
2 points by ahmedbesbes 9y ago|0 comments
48.
Understanding Deep Convolutional Neural Networks with a Practical Case in Keras(kdnuggets.com)
1 points by ahmedbesbes 9y ago|0 comments
49.
Understanding Deep Convolutional Neural Networks with a Practical Case in Keras(ahmedbesbes.com)
4 points by ahmedbesbes 9y ago|0 comments
50.
Understanding Convolutional Neural Networks with a Practical Usecase in Keras(ahmedbesbes.com)
4 points by ahmedbesbes 9y ago|0 comments
51.
Understanding Convolutional Neural Networks with a Practical Example in Keras(ahmedbesbes.com)
1 points by ahmedbesbes 9y ago|0 comments
52.
A guide to understand Convolutional Neural Networks and put them in practice(ahmedbesbes.com)
1 points by ahmedbesbes 9y ago|0 comments
53.
How to mine newsfeed data and extract interactive insights in Python(ahmedbesbes.com)
2 points by ahmedbesbes 9y ago|0 comments
54.
Sentiment analysis on Twitter using word2vec and keras(ahmedbesbes.com)
2 points by ahmedbesbes 9y ago|0 comments
55.
How to mine newsfeed data and extract interactive insights in Python(ahmedbesbes.com)
2 points by ahmedbesbes 9y ago|0 comments
56.
by ahmedbesbes 9y ago
the aim of my post is not to make a prediction based on a standard alogrithm. my goal is to show that DL techniques make better predictions. What I wrote was a first draft of the algorithm, and I've not made my point yet, unfortunate
57.
by ahmedbesbes 9y ago
Yes, I'm totally aware of that. I'm currently working on a slightly modified version of the code that will include CNN and hopefully better accuracy.
58.
by ahmedbesbes 9y ago
thank you for the feedback, I'll definitely give it a shot.
59.
How to Score 0.8134 in Titanic Kaggle Challenge(ahmedbesbes.com)
2 points by ahmedbesbes 9y ago|0 comments
60.
How to mine newsfeed data and extract interactive insights in Python(ahmedbesbes.com)
1 points by ahmedbesbes 9y ago|0 comments

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