Deep Learning - Artificial Neural Networks with Tensorflow - Code Preparation (Classification Theory)

This video provides a crash course on linear classification using TensorFlow 2.0. It begins with an overview of classification and basic machine learning assumptions. The architecture of a logistic regression model is explained, focusing on the use of activation functions like the sigmoid. The video then details how to implement this model in TensorFlow using Keras, covering the creation of input and Dense layers, and the compilation of the model with specific arguments. Finally, it discusses training the model, using the fit function, and evaluating its performance through metrics like accuracy.

What is the first step in the machine learning process as discussed in the lecture?

• Compiling the model

• Predicting outcomes

• Loading the data

• Evaluating the model

In the context of model architecture, what does the equation W1X1 + W2X2 + b = 0 represent?

• A hyperplane

• A circle

• A parabola

• A point

What function is used to convert the activation into a probability in logistic regression?

• Tanh function

• ReLU function

• Step function

• Sigmoid function

Which Keras layer is used to implement the linear transformation in TensorFlow?

• Dropout

• Flatten

• Dense

• Conv2D

What is the purpose of the 'fit' function in the training process?

• To compile the model

• To evaluate the model

• To initialize the model

• To train the model with data

What is the default optimizer used in TensorFlow for deep learning?

• Adagrad

• Adam

• RMSprop

• SGD

What is the role of the binary cross entropy in the training process?

• To initialize weights

• To compile the model

• To predict outcomes

• To serve as a cost function

What does the term 'epochs' refer to in the context of training a model?

• The number of predictions made by the model

• The number of layers in a model

• The number of times the learning algorithm works through the entire training dataset

• The number of neurons in a layer

Why is it important to evaluate a model on validation data?

• To improve the model's accuracy on training data

• To reduce the model size

• To increase the training speed

• To ensure the model performs well on unseen data

What does the 'metrics' argument in the compile function help track?

• The model's accuracy

• The number of epochs

• The learning rate

• The optimizer used