Exploring the Different Layers Of TensorFlow Keras: Dense, Convolutional & Recurrent Networks With Sample Data
TensorFlow Keras, a high-level API for TensorFlow, offers a powerful and versatile toolkit for building deep learning models. This guide delves into three fundamental layer types in Keras: Dense, Convolutional, and Recurrent networks, providing clear explanations and practical code examples using sample data to foster understanding and encourage further exploration. 1. Dense Networks: Unlocking Pattern Recognition Dense layers are the workhorses of many deep neural networks, connecting all neurons in one layer to every neuron in the subsequent layer. They excel at tasks involving pattern recognition, classification, and regression, especially when the relationship between inputs and outputs is intricate and non-linear. Let's illustrate this with a simple dataset of 5 houses, for which we want to predict prices based on features like area, number of bedrooms, and location (encoded numerically). import pandas as pd from tensorflow import keras data = pd.DataFrame({'area...