Advanced Statistical Modeling for Deep Learning Practitioner
Demystifying Statistics for Deep Learning Practitioners and Statistical Modeling Mastery for Deep Learning Professionals
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What You Will Learn!
- You will learn the most common probability distributions such as normal distribution and binomial distribution.
- You will learn how to transform skewed data to be normally distributed using different transformation methods such as log, square root, and power transformation
- You will learn how to calculate confidence intervals for statistical estimates such as model accuracy.
- You will learn the concepts of population data vs sample data.
- You will learn what random sampling means and how it affects data analysis.
- You will learn the evaluation metrics for classification models.
- You will understand what we mean by underfitting and overfitting in machine leaning and statistical modeling.
Description
In the rapidly evolving field of artificial intelligence, the ability to harness the power of deep learning models relies heavily on a strong foundation in advanced statistical modeling. This course is designed to equip deep learning practitioners with the knowledge and skills needed to navigate complex statistical challenges, make informed modeling decisions, and optimize the performance of deep neural networks.
Course Objectives:
1. Mastering Advanced Statistical Techniques: Gain a deep understanding of advanced statistical concepts and techniques, including multivariate analysis, Bayesian modeling, time series analysis, and non-parametric methods, tailored specifically for deep learning applications.
2. Optimizing Model Performance: Learn how to use statistical tools to fine-tune hyperparameters, handle imbalanced datasets, and address overfitting and underfitting issues, ensuring that your deep learning models achieve peak performance.
3. Interpreting Model Outputs: Develop the skills to interpret and critically evaluate the outputs of deep learning models, including confidence intervals, prediction intervals, and uncertainty quantification, enhancing the reliability of your AI systems.
4. Incorporating Probabilistic Modeling: Explore the world of probabilistic modeling and Bayesian neural networks to incorporate uncertainty into your models, making them more robust and reliable in real-world scenarios.
5. Time Series Forecasting: Master time series analysis techniques to make accurate predictions and forecasts, with a focus on applications like financial modeling, demand forecasting, and anomaly detection.
6. Advanced Data Preprocessing: Learn advanced data preprocessing methods to handle complex data types, such as text, images, and graphs, and apply statistical techniques to extract valuable insights from unstructured data.
7. Hands-On Projects: Apply your knowledge through hands-on projects and case studies, working with real-world datasets and deep learning frameworks to solve challenging problems across various domains.
8. Ethical Considerations: Discuss ethical considerations and best practices in statistical modeling, ensuring responsible AI development and deployment.
Who Should Attend:
- Data scientists and machine learning engineers seeking to deepen their statistical modeling skills for deep learning.
- Researchers and practitioners in artificial intelligence aiming to improve the robustness and interpretability of their deep learning models.
- Professionals interested in staying at the forefront of AI and machine learning, with a focus on advanced statistical techniques.
Prerequisites:
- A strong foundation in machine learning and deep learning concepts.
- Proficiency in programming languages such as Python.
- Basic knowledge of statistics is recommended but not mandatory.
Join us in this advanced statistical modeling journey, where you'll acquire the expertise needed to elevate your deep learning projects to new heights of accuracy and reliability. Uncover the power of statistics in the world of deep learning and become a confident and capable practitioner in this dynamic field.
Who Should Attend!
- This course is for students who want to learn statistics from data science perspective.