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[ CourseWikia com ] Deep Learning with Python and Keras - Build a Model For Sentiment Analysis
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01 - Introduction
01 - An overview of sentiment analysis.mp4
01 - An overview of sentiment analysis.srt
02 - Prerequisites.mp4
02 - Prerequisites.srt
02 - 1. Chapter Name
01 - Preprocessing text for sentiment analysis.mp4
01 - Preprocessing text for sentiment analysis.srt
02 - Word vector encodings and word embeddings.mp4
02 - Word vector encodings and word embeddings.srt
03 - Types of sentiment analysis.mp4
03 - Types of sentiment analysis.srt
04 - Approaches and challenges in sentiment analysis.mp4
04 - Approaches and challenges in sentiment analysis.srt
03 - 2. Chapter Name
01 - Getting set up with Google Colab.mp4
01 - Getting set up with Google Colab.srt
02 - Importing Python modules and loading data.mp4
02 - Importing Python modules and loading data.srt
03 - Analyzing word lengths across sentiment categories.mp4
03 - Analyzing word lengths across sentiment categories.srt
04 - Cleaning and preprocessing text.mp4
04 - Cleaning and preprocessing text.srt
05 - Visualizing text using word clouds.mp4
05 - Visualizing text using word clouds.srt
04 - 3. Chapter Name
01 - Feed-forward neural networks.mp4
01 - Feed-forward neural networks.srt
02 - Splitting data into training test and validation sets.mp4
02 - Splitting data into training test and validation sets.srt
03 - Representing text using count vectorization.mp4
03 - Representing text using count vectorization.srt
04 - Configuring the dense neural network (DNN).mp4
04 - Configuring the dense neural network (DNN).srt
05 - Training and evaluating the DNN.mp4
05 - Training and evaluating the DNN.srt
06 - Configuring the count vectorizer as a model layer.mp4
06 - Configuring the count vectorizer as a model layer.srt
07 - Representing text using TFIDF vectorization.mp4
07 - Representing text using TFIDF vectorization.srt
08 - Training and evaluating the model.mp4
08 - Training and evaluating the model.srt
09 - Representing text using integer sequences.mp4
09 - Representing text using integer sequences.srt
10 - Training ADNN using embeddings.mp4
10 - Training ADNN using embeddings.srt
05 - 4. Chapter Name
01 - Recurrent neural networks.mp4
01 - Recurrent neural networks.srt
02 - Long memory cells.mp4
02 - Long memory cells.srt
03 - The LSTM and GRU cells.mp4
03 - The LSTM and GRU cells.srt
04 - Training a recurrent neural network.mp4
04 - Training a recurrent neural network.srt
05 - Training an LSTM network.mp4
05 - Training an LSTM network.srt
06 - Serializing a model to disk and loading the model.mp4
06 - Serializing a model to disk and loading the model.srt
06 - Conclusion
01 - Summary and next steps.mp4
01 - Summary and next steps.srt
Bonus Resources.txt
Ex_Files_Deep_Learning_Python_Keras
Exercise Files
final_code
dataset
Tweets.csv
demo_01_SentimentAnalysisUsingNeuralNetworksWithKeras.ipynb
Torrent description
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