Fix IFP lecture: Complete NumPy and JAX implementations with verification

Major fixes and improvements:

1. NumPy Implementation (K_numpy and solve_model_numpy):
   - Fixed type hints (np.ndarray instead of jnp.ndarray)
   - Fixed loop iteration bug (range(n_z) instead of n_z)
   - Fixed undefined variable references (savings_grid[i], z_grid[k])
   - Added missing γ parameter to u_prime() and u_prime_inv() calls
   - Fixed boundary condition assignment (new_c_vals instead of c_vals)
   - Fixed broadcasting in endogenous grid calculation
   - Fixed return values (c_vals, ae_vals instead of undefined σ)

2. JAX Implementation (K and solve_model):
   - Fully implemented K operator using jax.vmap for vectorization
   - Fixed solve_model body function variable names
   - Added JAX 64-bit precision configuration for numerical accuracy
   - Verified results match NumPy to machine precision (~10^-15)

3. Code Throughout Lecture:
   - Fixed all solve_model unpacking (returns tuple, not single value)
   - Fixed asset law of motion plot (interpolate on endogenous grid)
   - Fixed cake eating sanity check (removed non-existent function)
   - Fixed compute_asset_stationary to use correct interpolation grid
   - Fixed all exercise solutions (1, 2, 3)
   - Improved variable naming consistency (σ_init → c_vals_init, z → k, lb → label)

4. Added Verification Section:
   - Compares NumPy and JAX implementations
   - Shows numerical differences at machine precision
   - Validates correctness of JAX implementation

All code tested end-to-end and verified working correctly.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <[email protected]>

Author: jstac