Upwork is hiring a Exploring Generative Pre-trained Transformers (GPT): From GPT-3 to GPT-4 Training Course

Job Title: Exploring Generative Pre-trained Transformers (GPT): From GPT-3 to GPT-4 Training Course Instructor

Job Type: Contract

Location: Remote

Duration: 3 days (Flexible dates)

Number of Students: 12

Overview:

We are seeking an experienced instructor to deliver a 3-day training course on Exploring Generative Pre-trained Transformers (GPT): From GPT-3 to GPT-4. The course will be conducted remotely and is designed for individuals interested in understanding and leveraging the power of generative pre-trained transformers for natural language processing tasks. The instructor should have a strong understanding of GPT models, transformer architectures, and practical applications of GPT in various domains.

Course Outline:

Day 1:

Morning Session:

- Introduction to Generative Pre-trained Transformers:

1. Overview of pre-trained language models

2. Evolution of GPT models (from GPT-1 to GPT-4)

3. Use cases and applications of GPT models

Afternoon Session:

- Transformer Architectures and Training:

1. Understanding transformer architecture in GPT models

2. Training strategies and techniques for GPT models

3. Fine-tuning and transfer learning with GPT models

Day 2:

Morning Session:

- Advanced Techniques in GPT:

1. Contextual embeddings and self-attention mechanisms

2. Handling long-range dependencies in GPT models

3. GPT model variants and architectural advancements

Afternoon Session:

- GPT for Natural Language Understanding (NLU):

1. Text classification and sentiment analysis with GPT

2. Named Entity Recognition (NER) using GPT

3. Question-answering and chatbot applications

Day 3:

Morning Session:

- GPT for Natural Language Generation (NLG):

1. Text generation and completion with GPT models

2. Storytelling and creative writing with GPT

3. Controlling output and generating diverse responses

Afternoon Session:

- Ethical Considerations and Challenges with GPT:

1. Bias and fairness issues in GPT models

2. Responsible AI practices and guidelines

3. Mitigating risks and challenges in GPT applications

Student Assumptions:

The students participating in the course are assumed to have the following prerequisites:

- Proficiency in Python programming language

- Familiarity with deep learning concepts and frameworks (e.g., PyTorch, TensorFlow)

- Basic understanding of natural language processing (NLP) concepts

If you are a qualified instructor with expertise in GPT models and natural language processing, please submit your proposal outlining your experience and teaching approach. Please include your availability and any relevant certifications or previous teaching experience.

We look forward to receiving your proposals!