The Use of AI/GenAI in Financial Markets: A Research and Data Perspective
The talk delves into the evolution of artificial intelligence, focusing on its applications in financial markets. It discusses how traditional AI models like LSTMs and newer generative models such as GANs and large language models (LLMs) like FinGPT enhance tasks like sentiment analysis, financial risk prediction, and data generation. The presentation also highlights the ethical concerns, regulatory challenges, and potential for optimization in applying AI technologies within financial sectors.
Knowledge Distillation : Making LLM model smaller - Streamlining AI
This talk explores the transformative potential of knowledge distillation in creating smaller, efficient AI models while preserving their performance. Delve into its role in flexible architectures, data augmentation, and resource-constrained applications like TinyML. The discussion covers key concepts, including the teacher-student framework, various distillation schemes, and objective loss functions. It also highlights practical tools like PyTorch’s torchdistill library and examines real-world applications in NLP and object detection.
Becoming a Life-long Learner and Reader: From AI/ML Engineer Perspective
The talk explores the philosophy and practical steps behind continuous personal and professional growth. Emphasizing curiosity, discipline, and a rejection of shortcuts, the talk draws inspiration from thought leaders like Richard Hamming. It introduces a structured framework for growth, focusing on micro (daily tasks), mezzo (milestones), and macro (long-term goals) planning. Practical tools and resources, such as AI engineering roadmaps and open-source models, are highlighted to help learners stay ahead in fields like AI and machine learning. The talk also encourages learning in public, engaging with high-quality research, and contributing to the community, while emphasizing the importance of balance and self-care in the journey of lifelong learning.
The Usages Of AI in Daily Life
This talk explores the evolution of Artificial Intelligence (AI) and its impact on daily life, from its origins to modern applications like generative AI. It highlights AI’s role in social media, entertainment, education, healthcare, and more, while offering practical tips on leveraging AI effectively. A concise guide to understanding and embracing AI’s transformative potential.
This talk outlines a practical roadmap for aspiring AI engineers, whether transitioning from software engineering or starting fresh. It covers foundational programming skills, essential tools, and concepts in AI and machine learning. The session highlights key areas like LLMOps, deployment practices, and hands-on learning resources, guiding participants on where to begin and how to progress. Designed for learners at all levels, it offers actionable advice to help build a strong foundation and excel in AI engineering.
Episode 11 - Talking about Women in STEM
Under the topic, Talking about Women in STEM, I shared detailed knowledge about STEM fields, the job opportunities in local and global markets and advised on how to start a career in one of the STEM fields. The podcast is organized by Friedrich Naumann Foundation - Yangon.
Training ML Models Using AutoML on GCP
Most people heard of the terms AI/ML and don’t really grasp the basic idea. By using AutoML on Google Cloud, developers can build and train models with little AI/ML knowledge. The talk is based on GCP Cloud Vertex AI services especially using AutoML. The talk introduced Machine Learning general flow/pipelines and demo with “Predicting loan risk with AutoML”.
Why Study Computer Science? and your future career prospects
This talk is intended for the students during career orientation programme hold by Albukhary Internation University, Malayisa.
This talk is intended for the students during Pre-University Learning Festival hold by Phandeeyer Institute, Myanmar.
Age of ML & AI | Preparing for ML & AI Jobs
This talk mainly covered demanding ML and AI roles, employment opportunities, requisite skill sets, and how to prepare for ML and AI jobs.
