# Title Text

# 1. Differentiate

- Fisher analysis/Accelerated MCMC through gradients (HMC, Variational Inference)
- Gradient descent (fast optimization)
- Differential equations

Finite differences: slow + error -> O(n) evaluations for n-dimensional gradient!

# How to differentiate automatically?

f'(x)=\lim _{h\to 0}{\frac {f(x+h)-f(x)}{h}}

**A) Finite differences**

Expression swell: duplicated computation

# How to differentiate automatically?

h(x) = f(x) g(x)

**B) Symbolic differentiation**

h'(x) = f'(x) g(x) + f(x) g'(x)

# How to differentiate automatically?

**C) Autodiff**

```
def f(x1,x2):
a = x1 / x2
b = np.exp(x2)
return (np.sin(a) + a - b) * (a-b)
```

# 2. Accelerate

- XLA (special compiler) -> Accelerated Linear Algebra. Fuses operations to avoid writing intermediate results to memory
- Run "numpy" on GPUs/TPUs
- Jit (just-in-time compilation)
- pmap to execute code in parallel (even across GPUs)
- vmap: automatic vectorization

#### deck

By carol cuesta

# deck

- 215