Deep learning ppt on model learning

1. Bayesian
statistics
Name : Vincy joel
Roll no: 227r1a7370
Sub: Deep Learning
Class: AIML-B

2. Introduction to Bayesian Statistics in Deep Learning
• Bayesian statistics provides a probabilistic framework for
modeling uncertainty in data and model parameters.
• In deep learning, it enables the quantification of uncertainty in
predictions, improving model robustness.
• This approach contrasts with traditional point estimates,
offering a more comprehensive understanding of model
behavior.

3. Foundations of Bayesian Inference
• Bayesian inference combines prior beliefs with observed data
using Bayes' theorem to update probabilities.
• The prior distribution encodes initial assumptions before seeing
data.
• The posterior distribution reflects updated beliefs after
considering the data.

4. Key Concepts: Prior, Likelihood, and Posterior
• The prior represents initial uncertainty about model parameters
before observing data.
• The likelihood models the probability of observed data given
parameters.
• The posterior is proportional to the product of prior and
likelihood, representing updated beliefs.

5. Bayesian Neural Networks (BNNs)
• BNNs incorporate probability distributions over network
weights instead of fixed values.
• They provide a measure of uncertainty in predictions, useful for
high-stakes applications.
• Training involves approximating the posterior distribution, often
using variational inference or Monte Carlo methods.

6. Variational Inference in Deep Learning
• Variational inference approximates complex posterior
distributions with simpler, parameterized distributions.
• It turns Bayesian inference into an optimization problem,
making it computationally feasible for large models.
• This approach is widely used in Bayesian deep learning due to
scalability and efficiency.

7. Monte Carlo Dropout as Approximate Bayesian
Inference
• Monte Carlo Dropout uses dropout at inference time to
approximate Bayesian posterior sampling.
• It provides an easy-to-implement method for estimating
uncertainty without changing the model architecture.
• This technique has been popular for uncertainty quantification
in deep neural networks.

8. Benefits of Bayesian Deep Learning
• Bayesian methods improve model calibration, making
probabilistic predictions more reliable.
• They enable uncertainty estimation, which is crucial for safety-
critical applications.
• Bayesian approaches can reduce overfitting by incorporating
prior knowledge into the learning process.

9. Challenges and Limitations
• Bayesian inference in deep learning can be computationally
intensive and complex to implement.
• Approximate methods may introduce biases or understate
uncertainty if not carefully applied.
• Scalability to very large neural networks remains an ongoing
research challenge.

10. Applications of Bayesian Deep Learning
• Bayesian deep learning is used in medical diagnosis,
autonomous vehicles, and financial forecasting.
• It helps improve decision-making by providing confidence
intervals and uncertainty measures.
• Ongoing research explores integrating Bayesian methods with
other deep learning techniques for enhanced performance.

11. Conclusion and Future Directions
• Bayesian statistics offers powerful tools for uncertainty
quantification in deep learning models.
• Advances in approximate inference methods are making
Bayesian deep learning more scalable and practical.
• Future research aims to combine Bayesian principles with large-
scale models to enhance reliability and interpretability.

12. Thank you