Keras and Deep Learning Framework

Beginning deep learning is not presented as a matter of immediately starting with a neural-network model. This part of the course is a staged introduction. It first establishes the background of artificial intelligence, machine learning, and deep learning, then introduces the concepts needed to approach deep learning, then moves into Keras and practical neural-network work, and finally expands to the canonical machine-learning workflow.

A useful way to read this part of the course is as a sequence of learner states. At first, the learner has broad background context. The learner then acquires four foundational concepts, moves to a framework and workstation, studies working neural-network examples, reaches classification and regression tasks, and finally studies the wider machine-learning workflow with its common pitfalls and solutions.

The stage immediately before framework-based neural-network work introduces four subjects necessary to approach deep learning: tensors, tensor operations, gradient descent, and backpropagation. The source groups these subjects together as foundations rather than presenting them as unrelated vocabulary.

After the foundational concepts, the roadmap turns to getting started with neural networks through Keras, the deep-learning framework identified in the source. This stage also includes workstation setup and three foundational code examples with detailed explanations. Its role is to move the learner from knowing the concepts that precede deep learning toward practical framework-based neural-network work.

The important connection is sequential: the course does not introduce Keras as an isolated framework topic. Keras and workstation setup appear after the foundational-concepts stage and lead into examples that explain practical neural-network work.

The neural-network stage has a concrete learning outcome: by the end of Chapter 3, the learner will be able to train simple neural networks for classification and regression tasks. In this roadmap, classification and regression are presented as two broad task types that give the framework and examples a practical destination.

After the framework and neural-network foundations, the course broadens from individual examples to the canonical machine-learning workflow. This change in scope matters: model training is not presented as the entire process. Training is placed within a larger workflow, and the next chapter also addresses common pitfalls and their solutions.