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.

Explain the difference between the step function and the sigmoid function.

What is the purpose of the Keras module in Tensorflow as discussed in the lecture?

How do you determine the number of epochs needed for training a model?

Summarize the key points discussed in the lecture regarding the implementation of linear classification in Tensorflow.