Understanding Artificial Intelligence (AI) and Exploring Keras and TensorFlow

Introduction to Artificial Intelligence (AI)
Artificial intelligence (AI) is transforming our interaction with technology. It refers to computer systems that can perform tasks traditionally done by humans. Here are some essential points:

Definition: AI includes technologies such as machine learning, deep learning, and natural language processing (NLP), facilitating reasoning, decision-making, and problem-solving.

Scope: AI spans a broad range of technologies that enhance many services and products we use daily. From applications that suggest TV shows to chatbots offering instant customer support, AI's presence is widespread.

Debate: There is a debate whether current AI technologies are advanced machine learning or true "general artificial intelligence" (GAI). However, today's reference to AI typically means machine learning-driven technologies that undertake tasks once thought to be human-exclusive, like content generation, driving, or data analysis.

Keras and TensorFlow Frameworks
Keras:

Keras is the high-level API for the TensorFlow platform.
It offers an accessible, highly productive interface for addressing machine learning (ML) challenges, particularly in modern deep learning.
Keras facilitates the entire ML workflow, from data management to hyperparameter optimization to deployment.
Advantages include rapid prototyping, scalability, and compatibility across platforms.
Keras is ideal for quick development and ease of use.
TensorFlow:

TensorFlow is a robust ML framework.
It provides scalability and versatility, operating on TPUs, GPUs, and allowing model export to browsers or mobile devices.
Keras models can be deployed via a web API using TensorFlow.
TensorFlow is suited for intricate tasks and extensive scalability.
Relationship: Keras functions as a high-level wrapper

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