Hi everyone
Instats is excited to offer a 1-day seminar, Deep Learning for Complex Datasets with ChatGPT, running June 6th and led by Professor Nikolay Oskolkov from Molecular Biosciences at Lund University. This intensive workshop equips PhD students, academics, and professional researchers with the practical skills needed to harness cutting-edge deep learning approaches for complex, real-world data. Through hands-on exercises in R, Python, TensorFlow, and Keras—guided by ChatGPT as an interactive coding consultant—you will learn foundational principles of machine and deep learning, build neural networks from scratch, explore convolutional and recurrent architectures for sequential and genomic data, apply autoencoders for dimensionality reduction, and examine case studies spanning multi-omics integration, single-cell biology, and personalized medicine. By the end of the day, participants will be able to design, train, evaluate, and refine their own models, integrate Large Language Models into existing analytical workflows, and take away actionable insights that immediately translate to their research.
Sign up today to secure your spot, and feel free to share this opportunity with colleagues and students who might benefit!
Best wishes
Michael Zyphur
Professor and Director
Institute for Statistical and Data Science
Instats is excited to offer a 1-day seminar, Deep Learning for Complex Datasets with ChatGPT, running June 6th and led by Professor Nikolay Oskolkov from Molecular Biosciences at Lund University. This intensive workshop equips PhD students, academics, and professional researchers with the practical skills needed to harness cutting-edge deep learning approaches for complex, real-world data. Through hands-on exercises in R, Python, TensorFlow, and Keras—guided by ChatGPT as an interactive coding consultant—you will learn foundational principles of machine and deep learning, build neural networks from scratch, explore convolutional and recurrent architectures for sequential and genomic data, apply autoencoders for dimensionality reduction, and examine case studies spanning multi-omics integration, single-cell biology, and personalized medicine. By the end of the day, participants will be able to design, train, evaluate, and refine their own models, integrate Large Language Models into existing analytical workflows, and take away actionable insights that immediately translate to their research.
Sign up today to secure your spot, and feel free to share this opportunity with colleagues and students who might benefit!
Best wishes
Michael Zyphur
Professor and Director
Institute for Statistical and Data Science
