Data Science and Machine Learning (Theory and Projects) A to Z - Introduction to TensorFlow: FashionMNIST Example Plan N

The video tutorial covers the process of building a neural network model using TensorFlow in Google Colab. It begins with importing necessary libraries like TensorFlow, NumPy, and Matplotlib. The Fashion MNIST dataset is loaded and explored, followed by data preprocessing steps such as scaling. A neural network model is constructed, compiled, and trained over several epochs. The tutorial demonstrates making predictions on test data using the trained model. Finally, it introduces the concept of convolutional neural networks as a next step for image data processing.

Which library is primarily used for deep learning in this tutorial?

• Theano

• Scikit-learn

• PyTorch

• TensorFlow

What is the shape of each image in the Fashion MNIST dataset?

• 32x32

• 128x128

• 64x64

• 28x28

How many classes are there in the Fashion MNIST dataset?

• 5

• 15

• 10

• 20

What is the purpose of scaling image data before training?

• To increase image size

• To change image format

• To normalize pixel values

• To reduce computational cost

Which activation function is used in the hidden layers of the neural network?

• Tanh

• Sigmoid

• ReLU

• Softmax

What is the role of the dropout layer in the neural network?

• To increase model complexity

• To speed up training

• To prevent overfitting

• To reduce model size

Which optimizer is used for compiling the model in this tutorial?

• SGD

• RMSprop

• Adam

• Adagrad

What is the purpose of the softmax layer in the model?

• To apply dropout

• To flatten the input

• To convert outputs to probabilities

• To add more layers

How is the model's performance evaluated in this tutorial?

• By checking loss

• By measuring accuracy

• By counting epochs

• By visualizing data

What is the next topic mentioned for future videos?

• Generative Adversarial Networks

• Convolutional Neural Networks

• Recurrent Neural Networks

• Support Vector Machines