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Covers: implementation of Back Propagation

- Pseudocode for Back Propagation

Read Algorithm 6.1 (Forward Prop) and Algorithm 6.2 (Backward Prop)

Deep learning book

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- Objectives
- This recipe introduces Machine Learning in Physics. It describes the motive for applying ML in physics, and discusses current progress and challenges in the field with detail.
- Potential Use Cases
- Incorporating physics into ML models, Using ML models to learn physics
- Who is This For ?
- INTERMEDIATEPhysics researchers, machine learning engineers incorporating physics

Click on each of the following **annotated items** to see details.

Resources

ARTICLE 1. Introduction to Machine Learning for Physics

- How do you teach physics to machine learning models?

10 minutes

PAPER 2. ML in Physics and the Physics of Learning

- What are the challenges of applying ML to Physics? How can ML Models leverage physical laws? What kind of models are used for each physics problem?

30 minutes

VIDEO 3. Application of ML in Fluid Dynamics

- How can we use ML to solve a physics problem: turbulence modelling?

20 minutes

ARTICLE 4. Algorithm for backpropagation

- Pseudocode for Back Propagation

20 minutes

VIDEO 5. Intuitive understanding of Backward Propagation

- What is Forward Propagation?
- What is Backward Propagation?

10 minutes

OTHER 6. Convolutional Neural Network

- What is a convolutional neural network?

10 minutes

BOOK_CHAPTER 7. Encoder-Decoder network explained

- What is encoder- decoder network ?

30 minutes

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