Natural Language Processing with Deep Learning in Python

• Understand and implement word2vec
• Understand the CBOW method in word2vec
• Understand the skip-gram method in word2vec
• Understand the negative sampling optimization in word2vec
• Understand and implement GloVe using gradient descent and alternating least squares
• Use recurrent neural networks for parts-of-speech tagging
• Use recurrent neural networks for named entity recognition
• Understand and implement recursive neural networks for sentiment analysis
• Understand and implement recursive neural tensor networks for sentiment analysis
• Use Gensim to obtain pretrained word vectors and compute similarities and analogies 

Who this course is for:

• Students and professionals who want to create word vector representations for various NLP tasks
• Students and professionals who are interested in state-of-the-art neural network architectures like recursive neural networks
• SHOULD NOT: Anyone who is not comfortable with the prerequisites.