Deep Learning for NLP with TensorFlow

Natural Language Processing (NLP) is a hot topic in the field of Machine Learning. This is an advanced NLP course that employs a Deep Learning approach. This course begins with the configuration and installation of all required resources, including Tensor Flow 1.X CPU/GPU, Cuda, and Keras. If you have a GPU card, you will be able to accelerate the training processes of your models. If you don’t have a GPU card, you can use Google Colab to follow the instructions.
Following that, we will review the main concepts of Deep Learning in order to apply them to the Natural Language Processing field, providing you with a solid foundation for the all the chapters.

KEY FEATURE

Deep learning has exploded in popularity in scientific computing, and its algorithms are widely used by industries that deal with complex problems.

While deep learning algorithms use self-learning representations, they rely on ANNs to compute information in the same way that the brain does.

Deep learning employs artificial neural networks to perform complex computations on massive amounts of data. It is a type of machine learning based on the structure of human brain.

Deep learning algorithms teach machines by using examples to train them. Deep learning is commonly used in industries such as health care, eCommerce, entertainment, and advertising.

Deep Learning for NLP with TensorFlow Assignment Help

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Key Topics

    We Will Look At The Main Deep Learning Libraries And Models For NLP, Such As:

    1. Word Embeddings
    2. Word2Vec
    3. Glove
    4. FastText
    5. Universal Sentence Encoder
    6. RNN
    7. GRU
    8. LSTM
    9. Convolutions in 1D
    10. Seq2Seq
    11. Memory Networks
    12. and the Attention mechanism.

    This Course Offers You Many Examples, With Different Datasets Such As:

    1. Google News
    2. Yelp comments
    3. Amazon reviews
    4. IMDB reviews
    5. the Bible corpus, etc and different text corpus.

    At The Final In Chapters, You Will Put In Practice Your Knowledge With Practical Applications Such As:

    1. Multiclass Sentiment Analysis
    2. Text Generation
    3. Machine Translation
    4. Developing a ChatBot and more.
    For coding we are going to use TensorFlow, Keras, Google Colab and many Python libraries.

    If You Need A Foundation In Natural Language Processing Or Machine Learning, We Recommend The Following Courses:

    1. Python for Machine Learning and Data Mining or
    2. Natural Language Processing with Python and NLTK

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    Important Topics Studied Under Deep Learning for NLP with TensorFlow

    StatisticsOnlineAssignmentHelp.com assists students with Deep Learning For NLP assignments. In addition to a detailed step-by-step solution and explanation, we provide help with following topics studied under Deep Learning for NLP with TensorFlow.

    1. Installation and Setup

    – Resources: Codes, Datasets, Papers and more
    – Installing TensorFlow for CPU/GPU/Cuda and Keras

    2. Deep Learning Overview

    – Deep Learning Concept
    – Gradient Descent
    – Vanishing and Exploding Gradients
    – Beyond Gradient Descent
    – Loss Functions
    – Activation Functions
    – Batch Normalization
    – Convolutional Layers

    3. Deep Learning For NLP

    – Word Embeddings on Sentiment Analysis
    – Visualizing Word Embeddings
    – Word2Vec Model
    – Word2Vec-Skipgrams-Keras
    – Word2Vec-CBOW-Keras
    – The GloVe model
    – Yelp Comments Classification with GloVe part 1 and part 2
    – FastText model
    – FastText with Gensim
    – FastText on Google Collaboratory
    – RNN-GRU-LSTM models
    – Sentiment Analysis with GRU, CuDNNGRU, LSTM, CuDNNLSTM-part 1 and part 2
    – 1D Convolutional Layer for NLP
    – Sentiment Analysis with 1D ConvNet part 1 and part 2
    – Seq2Seq and the Attention Mechanism
    – Encoder-Decoder with Attention
    – Universal Sentence Encoder
    – Semantic Similarity with TF-Hub Universal Encoder
    – Text Classification with TF-Hub Universal Encoder

    4. Applications

    – Text Generation
    – Emotion recognition with LSTM and Attention part 1 and part 2
    – Neural Machine Translation with Seq2Seq
    – Improving Neural Machine Translation part 1 and part 2
    – Developing a Chat Bot part 1 and part 2

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