ananth

machine learning deep learning natural language processing deep neural networks nlp convolutional neural networks image classification neural networks bayes theorem probability theory word representation ai computer vision artificial intelligence convolution cnn regression problems linear models classification problems recurrent neural network add 1 smoothing word2vec language model joint probability distributions cosine similarity stemming lemmatization conditional gan image to image translation sagan cyclegan gan generative adversarial networks senet inception googlenet localization imagenet resnet backpropagation adagrad sigmoid relu variance bias underfitting overfitting gaussian naive bayes generative models independence conditional independence naive bayes classifier product rule of probability sum rule of probability gaussian random variables bayes networks graph search state space uniform cost search a star dfs bfs search problems robotics overview introduction latent semantic analysis singular value decomposition svd lsa neural turing machine memory networks speech recognition speech synthesis deep neural networks for speech tensorflow theano deep learning frameworks id3 algorithm entropy information gain decision tree supervised learning perceptron learning decision surface unsupervised learning gated recurrent units gru long short term memory lstm bidirectional rnn rnn tagging maximum entropy models ner maxent named entity recognition loglinear forward backward algorithm backward algorithm forward algorithm hidden markov model baum welch algorithm hmm viterbi algorithm minimum edit distance edit distance applications of minimum edit distance dynamic programming algorithm interpolation discounting smoothing techniques wordnet distributed representation n-grams baysean networks text normalization words tokenization nlp applications deep learning for practitioners practical deep learning feature learning restricted boltzmann machines autoencoders introduction to deep learning primer