Artificial Neural Networks - Building an Image Classifier with Keras - Part II

1 Artificial Neural Networks - Introduction to Artificial Neural Networks

2 ANN - What is full form of ANNs?

3 ANN - In Artificial Neural Network interconnected processing elements are called

4 ANN - Artificial neural network is used for

5 Artificial Neural Networks - Perceptrons

6 ANN - Choose the correct options -

7 Artificial Neural Networks - Multi-Layer Perceptrons and Backpropagation - Part I

8 Artificial Neural Networks - Multi-Layer Perceptrons and Backpropagation - Part II

9 ANN - Which of the following gives non-linearity to a neural network?

10 ANN - In a neural network

11 ANN - Step function converts continuous integer values into boolean (binary) values.

12 ANN - Count the total number of weight variables in the given picture.

13 ANN - On which point the rate of change of error will not change with respect to input?

14 ANN - On which point the rate of change of error with respect to input is maximum?

15 ANN - ANN is nothing but an extension of the multilayer perceptron (MLP) model

16 ANN - Which is true for neural networks?

17 ANN - Training of neural network means?

18 ANN - Bias is only used in the first layer of a deep neural network?

19 Artificial Neural Networks - Classification with Multi-Layer Perceptron

20 ANN - An MLP is often used for classification, with each output corresponding to a different binary class.

21 ANN - When the classes are exclusive(like cat, dog, parrot classification problem), the output layer of a Multi-Layer Perceptron is typically modified by replacing the individual activation functions with a shared sigmoid function.

22 Artificial Neural Networks - Building an Image Classifier with Keras - Part I

23 Artificial Neural Networks - Building an Image Classifier with Keras - Part II

24 ANN - If we have the class label in the form of a one-hot vector, we could use `categorical_crossentropy`. Else if the class label is an integer index, we use `sparse_categorical_cross entropy`.

25 ANN - Which of the following activation functions would we use for the output classification layer in a classification problem with 5 exclusive classes?

26 ANN - The value of softmax and sigmoid is in the range 0 and 1.

27 Artificial Neural Networks - Regression MLP

28 ANN - What could be done to avoid overfitting?

29 Artificial Neural Networks - Building Complex Models

30 ANN - When we evaluate a wide and deep model, Keras will return the total loss, as well as all the individual losses.

31 ANN - A wide and deep network could be used to perform two simultaneous tasks of classification using the same input data.

32 Artificial Neural Networks - Saving and Restoring Models

33 ANN - Keras will use the HDF5 format to save which of the following of a trained model?

34 ANN - Callbacks argument of fit() allows us to specify a list of objects that Keras will call at the start and end of the training, at the start and end of each epoch, and even before and after processing each batch.

35 ANN - ModelCheckpoint callback is used in conjunction with training using model.fit() to save a model or weights (in a checkpoint file) at some interval, so the model or weights can be loaded later to continue the training from the state saved.

36 Artificial Neural Networks - TensorBoard

37 ANN - TensorBoard is a great interactive visualization tool that you can use to

38 ANN - We cannot analyze training statistics using TensorBoard.

39 ANN - In general, you want to point the TensorBoard server to a root log directory and configure your program so that it writes to a different subdirectory every time it runs.

40 ANN - Which of the following enables visualizations for TensorBoard?

41 Artificial Neural Networks - Fine-tuning Neural Networks Hyperparameters

42 ANN - In a neural network, which of the following techniques is used to deal with overfitting?

43 ANN - What if we use a learning rate that’s too large?

44 ANN - In the following diagram, Learning rate is?

45 ANN - Bias is used to give more degrees of freedom.