Forwarded from Data Science by ODS.ai 🦜
Where to start with Data Science
There is now way to be taught to be data scientist, but you can learn how to become one yourself. There is no right way, but there is a way, which was adopted by a number of data scientists and it goes through online courses (MOOC). Following suggested order is not required, but might be helpful.
Best resources to study Data Science /Machine Learning
1. Andrew Ng’s Machine Learning (https://www.coursera.org/learn/machine-learning).
2. Geoffrey Hinton’s Neural Networks for Machine Learning (https://www.coursera.org/learn/neural-networks).
3. Probabilistic Graphical Models specialisation on Coursera from Stanford (https://www.coursera.org/specializations/probabilistic-graphical-models).
4. Learning from data by Caltech (https://work.caltech.edu/telecourse.html).
5. CS229 from Stanford by Andrew Ng (http://cs229.stanford.edu/materials.html)
6. CS224d: Deep Learning for Natural Language Processing from Stanford (http://cs224d.stanford.edu/syllabus.html).
7. CS231n: Convolutional Neural Networks for Visual Recognition from Stanford (http://cs231n.stanford.edu/syllabus.html).
8. Deep Learning Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville (http://www.deeplearningbook.org/).
9. Machine Learning Yearning by Andrew Ng (http://www.mlyearning.org/).
#books #wheretostart #mooc
There is now way to be taught to be data scientist, but you can learn how to become one yourself. There is no right way, but there is a way, which was adopted by a number of data scientists and it goes through online courses (MOOC). Following suggested order is not required, but might be helpful.
Best resources to study Data Science /Machine Learning
1. Andrew Ng’s Machine Learning (https://www.coursera.org/learn/machine-learning).
2. Geoffrey Hinton’s Neural Networks for Machine Learning (https://www.coursera.org/learn/neural-networks).
3. Probabilistic Graphical Models specialisation on Coursera from Stanford (https://www.coursera.org/specializations/probabilistic-graphical-models).
4. Learning from data by Caltech (https://work.caltech.edu/telecourse.html).
5. CS229 from Stanford by Andrew Ng (http://cs229.stanford.edu/materials.html)
6. CS224d: Deep Learning for Natural Language Processing from Stanford (http://cs224d.stanford.edu/syllabus.html).
7. CS231n: Convolutional Neural Networks for Visual Recognition from Stanford (http://cs231n.stanford.edu/syllabus.html).
8. Deep Learning Book by Ian Goodfellow and Yoshua Bengio and Aaron Courville (http://www.deeplearningbook.org/).
9. Machine Learning Yearning by Andrew Ng (http://www.mlyearning.org/).
#books #wheretostart #mooc
Coursera
Probabilistic Graphical Models
Offered by Stanford University. Probabilistic Graphical ... Enroll for free.
How to Install OpenAI's Universe and Make a Game Bot [LIVE]
https://www.youtube.com/watch?v=XI-I9i_GzIw&feature=em-lss
https://www.youtube.com/watch?v=XI-I9i_GzIw&feature=em-lss
Онлайн курс "Программирование глубоких нейронных сетей на Python"
http://www.asozykin.ru/courses/nnpython
Материалы курса:
Введение.
Лекция “Искусственные нейронные сети”.
Лекция “Обучение нейронных сетей”.
Лекция “Библиотеки для глубокого обучения”.
Лекция “Распознавание рукописных цифр”.
Лекция “Анализ качества обучения нейронной сети”.
Практическая работа “Распознование рукописных цифр из набора данных MNIST на Keras”.
Лекция “Сверточные нейронные сети”.
Лекция “Распознавание объектов на изображениях”.
Практическая работа “Распознавание объектов на изображениях с помощью Keras”.
Рекуррентные нейронные сети.
Анализ текстов с помощью рекуррентных нейронных сетей.
https://www.youtube.com/watch?v=GX7qxV5nh5o&list=PLtPJ9lKvJ4oiz9aaL_xcZd-x0qd8G0VN_
http://www.asozykin.ru/courses/nnpython
Материалы курса:
Введение.
Лекция “Искусственные нейронные сети”.
Лекция “Обучение нейронных сетей”.
Лекция “Библиотеки для глубокого обучения”.
Лекция “Распознавание рукописных цифр”.
Лекция “Анализ качества обучения нейронной сети”.
Практическая работа “Распознование рукописных цифр из набора данных MNIST на Keras”.
Лекция “Сверточные нейронные сети”.
Лекция “Распознавание объектов на изображениях”.
Практическая работа “Распознавание объектов на изображениях с помощью Keras”.
Рекуррентные нейронные сети.
Анализ текстов с помощью рекуррентных нейронных сетей.
https://www.youtube.com/watch?v=GX7qxV5nh5o&list=PLtPJ9lKvJ4oiz9aaL_xcZd-x0qd8G0VN_
Тренируем нейронную сеть написанную на TensorFlow в облаке, с помощью Google Cloud ML и Cloud Shell
Конвертер моделей darknet на tensorflow.
Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++
https://github.com/thtrieu/darkflow
Load trained weights, retrain/fine-tune them using tensorflow, export constant graph def to C++
https://github.com/thtrieu/darkflow
Recurrent Shop
Framework for building complex recurrent neural networks with Keras
Recurrent shop providing a set of RNNCells, which can be added sequentially to a special layer called RecurrentContainer along with other layers such as Dropout and Activation, very similar to adding layers to a Sequential model in Keras. The RecurrentContainer then behaves like a standard Keras Recurrent instance. In case of RNN stacks, the computation is done depth-first, which results in significant speed ups.
https://github.com/datalogai/recurrentshop
#keras
Framework for building complex recurrent neural networks with Keras
Recurrent shop providing a set of RNNCells, which can be added sequentially to a special layer called RecurrentContainer along with other layers such as Dropout and Activation, very similar to adding layers to a Sequential model in Keras. The RecurrentContainer then behaves like a standard Keras Recurrent instance. In case of RNN stacks, the computation is done depth-first, which results in significant speed ups.
https://github.com/datalogai/recurrentshop
#keras
Music auto-tagging models
and trained weights in keras/theano
How was it trained?
Using 29.1s music files in Million Song Dataset
split setting: A repo for split setting for an identical setting.
See https://arxiv.org/pdf/1609.04243v3.pdf
https://github.com/keunwoochoi/music-auto_tagging-keras
https://github.com/keunwoochoi/music-auto_tagging-keras/blob/master/slide-ismir-2016.pdf
#keras
and trained weights in keras/theano
How was it trained?
Using 29.1s music files in Million Song Dataset
split setting: A repo for split setting for an identical setting.
See https://arxiv.org/pdf/1609.04243v3.pdf
https://github.com/keunwoochoi/music-auto_tagging-keras
https://github.com/keunwoochoi/music-auto_tagging-keras/blob/master/slide-ismir-2016.pdf
#keras
SalGAN: Visual Saliency Prediction with Generative Adversarial Networks
saliency-salgan-2017
https://imatge-upc.github.io/saliency-salgan-2017/
https://github.com/imatge-upc/saliency-salgan-2017
#Lasagne #theano #salience
saliency-salgan-2017
https://imatge-upc.github.io/saliency-salgan-2017/
https://github.com/imatge-upc/saliency-salgan-2017
#Lasagne #theano #salience