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Deep Learning for Image Recognition Beginner | SciPy 2016 Tutorial | Bargava Subramanian
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The current state-of-art technique for image recognition is deep learning. This workshop would cover some of the common deep learning architectures for image recognition, advantages and concerns along with hands-on implementing them using the latest deep learning libraries in Python. The main topics would be multi-layer perceptron, deep convolution networks and autoencoders.
This workshop introduces artificial neural networks and deep learning. The building blocks of neural networks are discussed in detail. Attendees are introduced to learning using ANN along with backpropagation algorithm. A preliminary model using multi-layer perceptron is implemented to get a feel of the model structure and the deep learning library keras.
The workshop then proceeds to introduce the state-of-art convolution neural networks. The building blocks of CNN are explained and is implemented on the dataset to train the image recognition model and use it to test on unseen data. Overfitting is a big issue in deep learning. Some ways to overcome that are discussed and implemented.
We'll also show how GPU's affect the computation.
Unsupervised learning using autoencoders are introduced and implemented.
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