Deep Learning with Python (Video 4)

This video tutorial introduces deep learning using the MNIST dataset of handwritten digits. It covers setting up the environment with Anaconda, Theano, and Keras, and demonstrates loading and preparing the dataset. The tutorial defines a convolutional neural network model, compiles it, and trains it using Keras. The model achieves high accuracy on the test set. The session concludes with a brief overview of the topics covered and hints at future discussions on backpropagation and Theano.

What is the primary dataset used in this tutorial for training a deep neural network?

• ImageNet

• Fashion-MNIST

• CIFAR-10

• MNIST

Which library is used alongside Theano for building the neural network in this tutorial?

• Scikit-learn

• Keras

• PyTorch

• TensorFlow

What is the purpose of reshaping the MNIST images during preprocessing?

• To fit the input format expected by the network

• To increase the resolution

• To add noise for data augmentation

• To convert them to color images

What is the activation function used after the convolutional layers in the model?

• Sigmoid

• Tanh

• ReLU

• Softmax

How many convolutional layers are included in the model described in the tutorial?

• Two

• Three

• Four

• Five

What is the purpose of using dropout in the neural network model?

• To prevent overfitting

• To improve training speed

• To increase the number of neurons

• To enhance image quality

What accuracy was achieved on the test set after training the model?

• 85%

• 90%

• 99%

• 95%