Artificial Neural Networks with TensorFlow 2: ANN Architecture Machine Learning Projects

Hands-on TensorFlow 2 for neural network projects 💻

Artificial Neural Networks with TensorFlow 2: ANN Architecture Machine Learning Projects takes a practical route through ANN design in TensorFlow 2, starting with the basics and moving steadily into more advanced model-building work. Poornachandra Sarang structures the material around real implementation topics rather than abstract theory alone, which gives the book a useful project-driven feel.

From foundations to deeper model work

The opening chapters introduce TensorFlow 2.x, , and core neural-network concepts before moving into binary classification, CNNs, transfer learning, regression, and estimator-based workflows. That progression makes the book feel like a guided build: readers see how one idea leads into the next, and how different ANN approaches fit together in practice.

What the book covers

• TensorFlow 2.x basics and the API
• Neural network setup and training workflows
• Binary classification and image classification
• Convolutional neural networks and transfer learning
• Regression with neural networks
• Estimator-based project workflows

A practical choice for learners who want to build

This is a good fit for readers who want a hands-on introduction to TensorFlow 2 neural networks and prefer learning by following implemented examples. The chapter sequence suggests a steady ramp in complexity, making it suitable for developers, students, and self-directed learners who want to connect ANN concepts with working machine learning code.

Why it stands out

Rather than treating neural networks as a single monolithic topic, the book separates the main approaches clearly: classification, CNNs, transfer learning, regression, and estimators. That makes it easier to understand how TensorFlow 2 supports different machine learning tasks and where each workflow belongs.