deep learning part 2
Enhance Photos With Deep Learning Part 2: The Data
In this article, we are going to look at some methods to degrade our data so it would be representative of production-time images. You can find the notebook for this article here. Without further ado, let's get coding! Finding data for a particular task is not easy: Not only must we come up with lots of input data, but we also need to come up with carefully-crafted labels to train the model. For instance, image segmentation requires tediously examining every pixel in a picture and identifying what object they belong to.
Deep Learning: Not Just for Silicon Valley · fast.ai
Recent American news events range from horrifying to dystopian, but reading the applications of our fast.ai I was blown away by how many bright, creative, resourceful folks from all over the world are applying deep learning to tackle a variety of meaningful and interesting problems. Their passions range from ending illegal logging, diagnosing malaria in rural Uganda, translating Japanese manga, reducing farmer suicides in India via better loans, making Nigerian fashion recommendations, monitoring patients with Parkinson's disease, and more. Our mission at fast.ai is to make deep learning accessible to people from varied backgrounds outside of elite institutions, who are tackling problems in meaningful but low-resource areas, far from mainstream deep learning research. Our group of selected fellows for Deep Learning Part 2 includes people from Nigeria, Ivory Coast, South Africa, Pakistan, Bangladesh, India, Singapore, Israel, Canada, Spain, Germany, France, Poland, Russia, and Turkey.
Deep Learning Part 2: Transfer Learning and Fine-tuning Deep Convolutional Neural Networks
This is a blog series in several parts -- where I describe my experiences and go deep into the reasons behind my choices. In Part 1, I discussed the pros and cons of different symbolic frameworks, and my reasons for choosing Theano (with Lasagne) as my platform of choice. Part 2 of this blog series is based on my upcoming talk at The Data Science Conference, 2016. Here in Part 2, I describe Deep Convolutional Neural Networks (DCNNs) and how Transfer learning and Fine-tuning helps better the training process for domain specific images. Please feel free to email me at trivedianusua23@gmail.com if you have questions.
Deep Learning Part 2: Transfer Learning and Fine-tuning Deep Convolutional Neural Networks
This is a blog series in several parts -- where I describe my experiences and go deep into the reasons behind my choices. In Part 1, I discussed the pros and cons of different symbolic frameworks, and my reasons for choosing Theano (with Lasagne) as my platform of choice. Part 2 of this blog series is based on my upcoming talk at The Data Science Conference, 2016. Here in Part 2, I describe Deep Convolutional Neural Networks (DCNNs) and how Transfer learning and Fine-tuning helps better the training process for domain specific images. Please feel free to email me at [email protected] if you have questions.