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NLP-in-Tensorflow

Building natural language processing systems using TensorFlow starting from processing text to generating poetry with neural networks (LSTMs)

Skills -> Natural Language Processing & Generating, Tokenizing, Generating n_grams, Padding, Embeddings, Bidirectional LSTM, Hyper-Parameters, Tensorflow, Keras, Python

These notebooks are submitted as part of assignments while completing a course in Coursera

Included notebooks are

  1. https://github.com/TechWithRamaa/NLP-in-Tensorflow/blob/main/Tokenization_Padding_in_Tensorflow.ipynb

    • Embarked on a journey into the world of Natural Language Processing (NLP) by learning how to tokenize words, transforming them into numbers that a machine can understand
    • This foundational knowledge paved the way for understanding how machines can begin to comprehend human language
    • Dataset - https://www.kaggle.com/c/learn-ai-bbc/data

  2. https://github.com/TechWithRamaa/NLP-in-Tensorflow/blob/main/BBC_News_Articles_Neural_Network_Classifier.ipynb

    • Explored Embeddings
    • These powerful tools map our vocabulary into higher-dimensional space, allowing the machine to grasp the subtleties of word meanings
    • Learned how words with similar sentiments are clustered together, and how the direction of these vectors can reveal the underlying emotions in text
    • The introduction of subword tokenization further highlighted the importance of not just the words themselves, but also the sequence in which they appear
    • Dataset - https://www.kaggle.com/c/learn-ai-bbc/data classifying articles into 5 categories ['tech', 'business', 'sport', 'sport', 'entertainment']

  3. https://github.com/TechWithRamaa/NLP-in-Tensorflow/blob/main/Text_Classification_Keras_LSTMs.ipynb

    • Explored various model formats that help capture context (forward & backward), allowing for a more nuanced understanding of sentiment in text
    • Learnt best practices for defining Hyper parameters
    • Adjusting hyper parameters & analyzing accuracy of neural network models with different Keras Layers like Conv1D, Dropout, GlobalMaxPooling1D, MaxPooling1D, LSTM and Bidirectional(LSTM)
    • Started experimenting with text prediction, laying the groundwork for creating entirely new sequences of words resulting in Poetry
    • Dataset - https://www.kaggle.com/c/learn-ai-bbc/data classifying articles into 5 categories ['tech', 'business', 'sport', 'sport', 'entertainment']

  4. https://github.com/TechWithRamaa/NLP-in-Tensorflow/blob/main/Creating_Poetry_With_Bidirectional_LSTMs.ipynb