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Complete guide on deriving and implementing word2vec, GLoVe, word embeddings, and sentiment analysis with recursive nets
What Will I Learn?
- 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
- Install Numpy, Matplotlib, Sci-Kit Learn, Theano, and TensorFlow (should be extremely easy by now)
- Understand backpropagation and gradient descent, be able to do it on your own.
- Code a recurrent neural network in Theano
- Code a feedforward neural network in Theano
DescriptionIn this course we are going to look at advanced NLP.
- king – man = queen – woman
- France – Paris = England – London
- December – Novemeber = July – June
- Watch it at 2x.
- Take handwritten notes. This will drastically increase your ability to retain the information.
- Write down the equations. If you don’t, I guarantee it will just look like gibberish.
- Ask lots of questions on the discussion board. The more the better!
- Realize that most exercises will take you days or weeks to complete.
- Write code yourself, don’t just sit there and look at my code.
- (The Numpy Stack in Python)
- Linear Regression in Python
- Logistic Regression in Python
- (Supervised Machine Learning in Python)
- (Bayesian Machine Learning in Python: A/B Testing)
- Deep Learning in Python
- Practical Deep Learning in Theano and TensorFlow
- (Supervised Machine Learning in Python 2: Ensemble Methods)
- Convolutional Neural Networks in Python
- (Easy NLP)
- (Cluster Analysis and Unsupervised Machine Learning)
- Unsupervised Deep Learning
- (Hidden Markov Models)
- Recurrent Neural Networks in Python
- Artificial Intelligence: Reinforcement Learning in Python
- Natural Language Processing with Deep Learning in Python
- linear algebra
- probability (conditional and joint distributions)
- Python coding: if/else, loops, lists, dicts, sets
- Numpy coding: matrix and vector operations, loading a CSV file
- neural networks and backpropagation
- Can write a feedforward neural network in Theano and TensorFlow
- Can write a recurrent neural network / LSTM / GRU in Theano and TensorFlow
Who is the target audience?
- 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.