Deep learning. (Packt Publishing, 2023)
TensorFlow is the world's most popular library for deep learning, and it is built by Google. It is the library of choice for many companies doing AI and machine learning. So, if you want to do deep learning, you got to know TensorFlow. In this course, you will learn how to use TensorFlow 2 to build deep neural networks. We will first start by learning the basics of machine learning, classification, and regression. Then in the next section, we will understand the connection between artificial neural networks and biological neural networks and how that inspires our thinking in the field of deep learning. In the last two sections, you will learn about loss functions to understand mean squared error, binary cross entropy, and categorical cross entropy and gradient descent to understand stochastic gradient descent, momentum, variable and adaptive learning rates, and Adam optimization. By the end of this course, we will have understood how to use TensorFlow for artificial neural networks in deep learning. What You Will Learn Understand what machine learning is Build linear models with TensorFlow 2 Learn how to build deep neural networks with TensorFlow 2 Learn how to perform image classification and regression with ANN Learn loss functions such as mean-squared error and cross-entropy loss Learn about stochastic gradient descent, momentum, and Adam optimization Audience This course is designed for anyone interested in deep learning and machine learning, anyone who wants to implement deep neural networks in TensorFlow 2, or anyone interested in building a foundation for convolutional neural networks, recurrent neural networks, LSTMs (Long Short Term Memory), and transformers. One must have decent Python programming skills and should be comfortable with data science libraries such as NumPy and Matplotlib. About The Author Lazy Programmer: The Lazy Programmer is an AI and machine learning engineer with a focus on deep learning, who also has experience in data science, big data engineering, and full-stack software engineering. With a background in computer engineering and specialization in machine learning, he holds two master's degrees in computer engineering and statistics with applications to financial engineering. His expertise in online advertising and digital media includes work as both a data scientist and big data engineer. He has created deep learning models for prediction and has experience in recommendation systems using reinforcement learning and collaborative filtering. He is a skilled instructor who has taught at universities including Columbia, NYU, Hunter College, and The New School. He has web programming expertise, with experience in technologies such as Python, Ruby/Rails, PHP, and Angular, and has provided his services to multiple businesses.