Natural Language Processing: Autoencoder - understanding Word2Vec

Now it's time to do some NLP, Natural Language Processing, and we will start with the famous word2vec example. So word2vec is a way to compress your multidimensional text data into smaller-sized vectors, and with those vectors, you can actually do calculations or further attach downstream neural network layers, for example, for classification. And in this case, we will just do both. So we will start with some imports, and we put here a seat that examples are replicable. So let's start with some sentences. The initial lectures series on this topic can find in the below links: Introduction to Anomaly Detection https://www.youtube.com/watch?v=IFHX4HUAo1w&list=PLpW3QouFxOnM6YWVOrcUBaQiy8EWi05pi&index=37

How to implement an anomaly detector (1/2) https://www.youtube.com/watch?v=DN0H2Qz3Rxg How to implement an anomaly detector (2/2) https://www.youtube.com/watch?v=nYZuQg5K22Y How to deploy a real-time anomaly detector https://www.youtube.com/watch?v=LnPrT-IkzNw Introduction to Time Series Forecasting https://www.youtube.com/watch?v=G7_uNCOFEzE Stateful vs. Stateless LSTMs https://www.youtube.com/watch?v=R7CwkhZYJdU Batch Size! which batch size is to choose? https://www.youtube.com/watch?v=wfyErdPsZPI Number of Time Steps, Epochs, Training and Validation https://www.youtube.com/watch?v=tsprdX9RkRg Batch size and Trainin Set Size https://www.youtube.com/watch?v=5kLLKhNJlEY Input and Output Data Construction https://www.youtube.com/watch?v=zCHrQRlu688 Designing the LSTM network in Keras https://www.youtube.com/watch?v=Y3ApYArvBr8 Anatomy of a LSTM Node https://www.youtube.com/watch?v=WrA3LlKAbf0 Number of Parameters:How LSTM Parmeter Num is Computed. https://www.youtube.com/watch?v=oXNBR0U1A54 Training and loading a saved model. https://www.youtube.com/watch?v=jKjm1cX-mtM ... https://www.youtube.com/watch?v=c77ihg2fTuI