A Practical Approach to Timeseries Forecasting Using Python - LSTM Implementation and Errors

The video tutorial guides viewers through implementing an LSTM model using TensorFlow. It begins with importing necessary libraries and layers, followed by building a sequential LSTM model. The model is compiled with an Adam optimizer and mean squared error loss function. The tutorial then demonstrates how to fit the model with training data, specifying epochs, batch size, and validation split. Finally, it covers evaluating the model's performance by plotting training and validation loss, and concludes with a brief mention of making predictions.

Which of the following layers is NOT mentioned as being imported in the initial setup?

• Convolutional

• Dropout

• Dense

• Bidirectional

What is the activation function used in the LSTM layer?

• Tanh

• Sigmoid

• Softmax

• ReLU

Which optimizer is used to compile the LSTM model?

• RMSprop

• Adam

• Adagrad

• SGD

What is the loss function used in the model compilation?

• Categorical Crossentropy

• Mean Squared Error

• Binary Crossentropy

• Mean Absolute Error

How many epochs are set for training the model?

• 50

• 62

• 100

• 30

What is the batch size used during model training?

• 64

• 32

• 16

• 8

What is the validation split percentage used in the model training?

• 0.05

• 0.2

• 0.1

• 0.15