~Get Your Files Here !/04 - 3. Chapter Name/10 - Training ADNN using embeddings.mp4 15.4 MB
~Get Your Files Here !/04 - 3. Chapter Name/03 - Representing text using count vectorization.mp4 14.9 MB
~Get Your Files Here !/03 - 2. Chapter Name/04 - Cleaning and preprocessing text.mp4 13.5 MB
~Get Your Files Here !/04 - 3. Chapter Name/07 - Representing text using TFIDF vectorization.mp4 9.5 MB
~Get Your Files Here !/02 - 1. Chapter Name/02 - Word vector encodings and word embeddings.mp4 9.4 MB
~Get Your Files Here !/04 - 3. Chapter Name/04 - Configuring the dense neural network (DNN).mp4 9.4 MB
~Get Your Files Here !/02 - 1. Chapter Name/04 - Approaches and challenges in sentiment analysis.mp4 9.0 MB
~Get Your Files Here !/05 - 4. Chapter Name/02 - Long memory cells.mp4 8.9 MB
~Get Your Files Here !/05 - 4. Chapter Name/04 - Training a recurrent neural network.mp4 8.1 MB
~Get Your Files Here !/04 - 3. Chapter Name/08 - Training and evaluating the model.mp4 8.0 MB
~Get Your Files Here !/03 - 2. Chapter Name/02 - Importing Python modules and loading data.mp4 7.8 MB
~Get Your Files Here !/04 - 3. Chapter Name/01 - Feed-forward neural networks.mp4 7.7 MB
~Get Your Files Here !/03 - 2. Chapter Name/03 - Analyzing word lengths across sentiment categories.mp4 7.6 MB
~Get Your Files Here !/01 - Introduction/01 - An overview of sentiment analysis.mp4 7.6 MB
~Get Your Files Here !/05 - 4. Chapter Name/05 - Training an LSTM network.mp4 7.3 MB
~Get Your Files Here !/04 - 3. Chapter Name/02 - Splitting data into training test and validation sets.mp4 7.3 MB
~Get Your Files Here !/04 - 3. Chapter Name/05 - Training and evaluating the DNN.mp4 6.9 MB
~Get Your Files Here !/05 - 4. Chapter Name/06 - Serializing a model to disk and loading the model.mp4 6.8 MB
~Get Your Files Here !/03 - 2. Chapter Name/01 - Getting set up with Google Colab.mp4 6.7 MB
~Get Your Files Here !/05 - 4. Chapter Name/01 - Recurrent neural networks.mp4 6.5 MB
~Get Your Files Here !/05 - 4. Chapter Name/03 - The LSTM and GRU cells.mp4 5.8 MB
~Get Your Files Here !/02 - 1. Chapter Name/03 - Types of sentiment analysis.mp4 5.7 MB
~Get Your Files Here !/04 - 3. Chapter Name/09 - Representing text using integer sequences.mp4 5.4 MB
~Get Your Files Here !/04 - 3. Chapter Name/06 - Configuring the count vectorizer as a model layer.mp4 5.3 MB
~Get Your Files Here !/03 - 2. Chapter Name/05 - Visualizing text using word clouds.mp4 5.0 MB
~Get Your Files Here !/02 - 1. Chapter Name/01 - Preprocessing text for sentiment analysis.mp4 4.0 MB
~Get Your Files Here !/Ex_Files_Deep_Learning_Python_Keras/Exercise Files/final_code/dataset/Tweets.csv 3.5 MB
~Get Your Files Here !/06 - Conclusion/01 - Summary and next steps.mp4 2.9 MB
~Get Your Files Here !/Ex_Files_Deep_Learning_Python_Keras/Exercise Files/final_code/demo_01_SentimentAnalysisUsingNeuralNetworksWithKeras.ipynb 1.9 MB
~Get Your Files Here !/01 - Introduction/02 - Prerequisites.mp4 1.4 MB
~Get Your Files Here !/04 - 3. Chapter Name/03 - Representing text using count vectorization.srt 13.7 kB
~Get Your Files Here !/04 - 3. Chapter Name/10 - Training ADNN using embeddings.srt 12.7 kB
~Get Your Files Here !/02 - 1. Chapter Name/02 - Word vector encodings and word embeddings.srt 11.1 kB
~Get Your Files Here !/02 - 1. Chapter Name/04 - Approaches and challenges in sentiment analysis.srt 11.0 kB
~Get Your Files Here !/03 - 2. Chapter Name/04 - Cleaning and preprocessing text.srt 10.9 kB
~Get Your Files Here !/04 - 3. Chapter Name/04 - Configuring the dense neural network (DNN).srt 9.5 kB
~Get Your Files Here !/05 - 4. Chapter Name/02 - Long memory cells.srt 9.2 kB
~Get Your Files Here !/01 - Introduction/01 - An overview of sentiment analysis.srt 8.8 kB
~Get Your Files Here !/04 - 3. Chapter Name/07 - Representing text using TFIDF vectorization.srt 8.6 kB
~Get Your Files Here !/04 - 3. Chapter Name/01 - Feed-forward neural networks.srt 8.1 kB
~Get Your Files Here !/03 - 2. Chapter Name/03 - Analyzing word lengths across sentiment categories.srt 7.8 kB
~Get Your Files Here !/03 - 2. Chapter Name/02 - Importing Python modules and loading data.srt 7.4 kB
~Get Your Files Here !/05 - 4. Chapter Name/01 - Recurrent neural networks.srt 7.1 kB
~Get Your Files Here !/04 - 3. Chapter Name/02 - Splitting data into training test and validation sets.srt 6.9 kB
~Get Your Files Here !/03 - 2. Chapter Name/01 - Getting set up with Google Colab.srt 6.8 kB
~Get Your Files Here !/05 - 4. Chapter Name/03 - The LSTM and GRU cells.srt 6.5 kB
~Get Your Files Here !/02 - 1. Chapter Name/03 - Types of sentiment analysis.srt 6.5 kB
~Get Your Files Here !/05 - 4. Chapter Name/04 - Training a recurrent neural network.srt 6.4 kB
~Get Your Files Here !/05 - 4. Chapter Name/06 - Serializing a model to disk and loading the model.srt 6.3 kB
~Get Your Files Here !/04 - 3. Chapter Name/05 - Training and evaluating the DNN.srt 5.6 kB
~Get Your Files Here !/04 - 3. Chapter Name/08 - Training and evaluating the model.srt 5.6 kB
~Get Your Files Here !/05 - 4. Chapter Name/05 - Training an LSTM network.srt 5.4 kB
~Get Your Files Here !/02 - 1. Chapter Name/01 - Preprocessing text for sentiment analysis.srt 5.2 kB
~Get Your Files Here !/04 - 3. Chapter Name/09 - Representing text using integer sequences.srt 5.2 kB
~Get Your Files Here !/04 - 3. Chapter Name/06 - Configuring the count vectorizer as a model layer.srt 4.6 kB
~Get Your Files Here !/06 - Conclusion/01 - Summary and next steps.srt 3.8 kB
~Get Your Files Here !/03 - 2. Chapter Name/05 - Visualizing text using word clouds.srt 3.0 kB
~Get Your Files Here !/01 - Introduction/02 - Prerequisites.srt 1.9 kB
~Get Your Files Here !/Bonus Resources.txt 386 Bytes