Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text.
The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. Theano is a python library that makes writing deep learning models easy and gives the option of training them on a GPU.
The algorithm tutorials have some prerequisites. You should know some python, and be familiar with numpy. Since this tutorial is about using Theano, you should read over the Theano basic tutorial first.
Prerequisites: Python, Machine Learning
Feedforward Neural networks. Gradient descent and the backpropagation algorithm. Unit saturation, aka the vanishing gradient problem, and ways to mitigate it. RelU Heuristics for avoiding bad local minima. Heuristics for faster training. Nestors accelerated gradient descent. Regularization. Dropout.
Convolutional Neural Networks
Architectures, convolution / pooling layers
Recurrent Neural Networks
LSTM, GRU, Encoder Decoder architectures
Deep Unsupervised Learning
Autoencoders (standard, sparse, denoising, contractive, etc), Variational Autoencoders, Adversarial Generative Networks, Autoencoder and DBM
Attention and memory models, Dynamic memory networks
Applications of Deep Learning to Computer Vision
Image segmentation, object detection, automatic image captioning, Image generation with Generative adversarial networks, video to text with LSTM models. Attention models for computer vision tasks.
Applications of Deep Learning to NLP:
Introduction to NLP and Vector Space Model of Semantics.
Word Vector Representations: Continuous Skip-Gram Model, Continuous Bag-of- Words model (CBOW), Glove, Evaluations and Applications in word similarity, analogy reasoning.
Named Entity Recognition, Opinion Mining using Recurrent Neural Networks.
Parsing and Sentiment Analysis using Recursive Neural Networks.
Sentence Classification using Convolutional Neural Networks.
Dialogue Generation with LSTMs .
Applications of Dynamic Memory Networks in NLP.
Recent Research in NLP using Deep Learning: Factoid Question Answering, similar question detection, Dialogue topic tracking, Neural Summarization, Smart Reply.