Transformer Models and BERT Model with Google Cloud training

This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference.

Skills you'll learn

Transformer neural networks ? NLP transformers ? Bert

Prerequisite Details

To optimize your success in this program, we've created a list of prerequisites and recommendations to help you prepare for the curriculum. Prior to enrolling, you should have the following knowledge:

• Machine learning model implementation
• TensorFlow
• Machine learning frameworks in Python
• Intermediate Python
• Attention mechanisms
• PyTorch

Course Lessons

Transformer Models and BERT Model with Google Cloud

This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model.

1.1 Introduction to Transformer Architecture

• Overview of the Transformer architecture as a revolutionary neural network architecture
• Understanding the self-attention mechanism and its significance in sequence modeling

1.2 Fundamentals of BERT (Bidirectional Encoder Representations from Transformers)

• Introduction to BERT as a pre-trained transformer model for natural language processing tasks
• Understanding the bidirectional encoding approach and its advantages in capturing contextual information

1.3 Key Components of Transformer Models

• In-depth exploration of key components such as attention layers, multi-head attention, and feedforward networks in transformer models
• Analysis of how these components contribute to the model's ability to capture complex dependencies

1.4 BERT Architecture and Pre-training Process

• Detailed walkthrough of the architecture and pre-training process of BERT
• Insight into how BERT learns contextualized representations from vast amounts of unlabeled data

1.5 Applications of Transformer Models and BERT

• Exploration of real-world applications where transformer models and BERT excel
• Showcase of use cases in natural language understanding, sentiment analysis, and other NLP tasks

1.6 Google Cloud Services for Transformer Models

• Overview of Google Cloud services and tools that support the deployment and management of transformer models
• Introduction to specific products and solutions tailored for working with transformer models on Google Cloud

1.7 Hands-On Implementation of BERT with Google Cloud

• Practical session on implementing BERT models using Google Cloud services
• Step-by-step guide on setting up and fine-tuning BERT models for specific NLP tasks

contact us

email - [email protected]