Author Description

Learn all about TensorFlow TensorFlow is an open-source framework for machine learning and deep learning. Developed by Google Brain Team, it provides a rich set of tools and libraries for building and training machine learning models, neural networks, and deep learning algorithms. TensorFlow is written in Python, but it also provides interfaces for several other languages, including C++, Java, and Go. At its core, TensorFlow is a computational framework that enables developers to define and execute complex mathematical computations with large amounts of data. It uses a graph-based approach to represent mathematical computations as a directed graph, with nodes representing operations and edges representing the data inputs and outputs. This allows for efficient parallelization of computations across multiple CPUs and GPUs, making it possible to work with large datasets and complex models. The book covers the following: 1. Introduction to TensorFlow What is TensorFlow? The history of TensorFlow Why use TensorFlow? TensorFlow vs other machine learning frameworks 2. Getting started with TensorFlow Installing TensorFlow Creating a simple TensorFlow program Understanding TensorFlow's computational graph Using TensorFlow with Jupyter notebooks 3. Data preparation Data preprocessing techniques Data cleaning Feature engineering Data augmentation Data normalization and scaling 4. Building machine learning models with TensorFlow Regression models Classification models Neural networks Convolutional neural networks Recurrent neural networks Transfer learning 5. Training and evaluating models Choosing an appropriate loss function Gradient descent and other optimization algorithms Overfitting and underfitting Evaluating model performance Hyperparameter tuning 6. Deploying TensorFlow models Exporting mode