Gen AI for Quant Fin Python Modeling 101

🌟 Master Python for Generative AI - Your Gateway to Quant Finance Modelling! 🌟

Course Title: Gen AI for Quant Fin Python Modeling 101 Hands-on using BERT

Hands-On: Get Started with Python & Generative AI for Beginners

Dive into the realm of Generative AI for Quantitative Finance with our Python 101 course tailored for beginners. This course is designed to give you a comprehensive and practical understanding of how to apply Generative Artificial Intelligence (Gen AI) techniques, specifically BERT models, in Python for real-world Quantitative Financial applications.

Course Headline: Python & Generative AI 101 for Beginners: Fine Tuning with ChatGPT & Logistic Regression Backend

What You'll Accomplish:

• 🤖 Interact with AI: Learn to use a GPT-powered chat interface to query and retrain your models, enhancing feature selection and model performance.
• ⚙️ Fine Tuning: Get hands-on experience in fine-tuning BERT models and integrating them with a Logistic Regression backend.
• 📈 Real-Time Analytics: Understand how to deploy your AI models using Flask/FastAPI for real-time analytics applications.

Course Structure:

Two key projects will guide you through the course:

1. Fine-Tuning BERT with a Logistic Regression Layer: You'll learn how to enhance a BERT model with a Logistic Regression layer to create a robust AI system for quantitative finance predictions.

2. Deploying Models for Real-Time Analytics: Explore the toolkit needed to serve your text-based or data-generating models in real-time using tools like Flask/FastAPI.

Topics Covered:

1. 🎲 BERT vs GPT: Understand the differences and similarities between these two powerful generative models.
2. Torch & Tensors: Get familiar with the tools that enable deep learning models, including PyTorch.
3. FastAPI App: Build efficient web applications using FastAPI, a modern, fast web framework for Python 3.7+ based on standardPython type hints.
4. Logistic Regression Model: Implement an in-memory Logistic Regression model to complement your AI system.
5. Transformers & Hugging Face Models: Work with essential tools like Trainer, TrainingArguments, BertTokenizer, and BertForSequenceClassification.

Introduction to General AI in Finance:

Explore the application of BERT models in the financial sector, including:

• The power of Hugging Face pre-trained models for local training tasks.
• The process of fine-tuning models using DistilBERT and other techniques.
• Querying your backend using commands and simulating results.
• Addressing the limitations of current generative AI technologies (as of Dec 2024).
• Setting up OpenAI API alongside Hugging Face for a more comprehensive instruction set.

Future Work:

• Model Retraining on Real Data: Dive into retraining models with real-world datasets to improve accuracy and performance.
• Generative AI in Data Analytics: Discover applications of Generative AI in synthesizing data, detecting anomalies, and creating predictive models.
• Enhanced Query Methods: Learn how to use OpenAI's API to query your model with a wider array of instructions.

Logistic Regression Insights:

Understand the capabilities of Logistic Regression within this context, including:

• Defining input schemas for retraining.
• Setting up training arguments with optimizations.
• Utilizing libraries like joblib or pickle to serialize your models.

Future Enhancements:

• Input Validation & Feature Tracking: Ensure robustness in your model by validating inputs and tracking features.
• Hyperparameter Validation: Optimize your AI system with advanced hyperparameter tuning.
• Returning Probabilities for Predictions: Learn how to interpret the results of your Logistic Regression model, including returning probabilities for a better understanding of your predictions.

Join us on this journey to harness the full potential of Generative AI in Quantitative Finance with Python. Enroll now and transform your coding skills into predictive power for the financial sector! 🚀💼💰