Скачать или смотреть Intro to TensorFlow-Keras [Deep Neural Network]

Welcome back, everyone! In our previous videos, we delved into the intricacies of GCP Vertex AI, GenAI, Prompt design with Model Garden, and more. Today, let's take a step back and trace the roots of these concepts:

GenAI (Large Language Model) -- Tf.Keras -- TensorFlow -- DNN -- Neural Network

The journey from neural networks to GenAI has been fascinating, and today, I'm shining a spotlight on a crucial component of this evolution: the Keras framework.

Keras is an open-source library that provides a Python interface for artificial neural networks, acting as an interface for the powerful TensorFlow library.

In this video, we'll explore the two main types of Model APIs within Keras:

1. Sequential API:
A straightforward approach, resembling a simple list of layers. However, it's limited to single-input, single-output stacks of layers.

2. Functional API:
An easy-to-use, fully-featured API supporting arbitrary model architectures. This is often the go-to choice for most people and use cases, representing the "industry strength" model in Keras.

And then, we'll dive into the practical aspects of working with Keras on GCP:

Save/Load and Deploy a Keras Model on GCP:
We'll explore the steps involved in saving, loading, and deploying a Keras model seamlessly on Google Cloud Platform.

Deploying and Making Predictions:
Learn the ropes of deploying your Keras model and making predictions, bringing your machine learning models to life in a production environment.

I'm excited to share this journey with you, providing insights into the foundational elements that have paved the way for the advanced concepts we've been exploring. Sit back, relax, and let's unravel the world of Keras and its application on GCP. Enjoy the video! 🚀🤖 #KerasFramework #MachineLearning #GCPVertexAI #TechExploration #YouTubeTutorial