Data representation
In our quest to solve complex tasks using ML, we come across diverse types of raw data. Our primary role involves transforming this raw data (which could be text, images, audio, or video) into numerical representations. These representations allow our ML models to easily digest and learn the underlying patterns in the data efficiently. To achieve this, this is where TensorFlow and its fundamental data structure, tensors, come into play. While numerical data is commonly used in training models, our models are also adept at efficiently handling binary and categorical data. For such data types, we apply techniques such as one-hot encoding to transform them into a model-friendly format.
Tensors are multi-dimensional arrays designed for numerical data representation; although they share some similarities with NumPy arrays, they possess certain unique features that give them an advantage in deep learning tasks. One of these key advantages is their ability to utilize...