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Keep the gradient flowing

Policy Gradients Part 1: The REINFORCE Estimator On the Link Between Optimization and Polynomials, Part 6. Optimization Nuggets: Stochastic Polyak Step-size, Part 2 Optimization Nuggets: Stochastic Polyak Step-size On the Convergence of the Unadjusted Langevin Algorithm The Russian Roulette: An Unbiased Estimator of the Limit Notes on the Frank-Wolfe Algorithm, Part III: backtracking line-search On the Link Between Optimization and Polynomials, Part 5 Optimization Nuggets: Implicit Bias of Gradient-based Methods Optimization Nuggets: Exponential Convergence of SGD On the Link Between Optimization and Polynomials, Part 4 On the Link Between Optimization and Polynomials, Part 3 On the Link Between Optimization and Polynomials, Part 2 On the Link Between Polynomials and Optimization, Part 1 How to Evaluate the Logistic Loss and not NaN trying Notes on the Frank-Wolfe Algorithm, Part II: A Primal-dual Analysis Three Operator Splitting Notes on the Frank-Wolfe Algorithm, Part I Optimization inequalities cheatsheet A fully asynchronous variant of the SAGA algorithm Hyperparameter optimization with approximate gradient Lightning v0.1 scikit-learn-contrib, an umbrella for scikit-learn related projects. SAGA algorithm in the lightning library On the consistency of ordinal regression methods Holdout cross-validation generator IPython/Jupyter notebook gallery PyData Paris - April 2015 Data-driven hemodynamic response function estimation Plot memory usage as a function of time
new get_blas_funcs in scipy.linalg
Fabian Pedregosa · 2011-04-23 · via Keep the gradient flowing

Today got merged some changes I made to function scipy.linalg.get_blas_funcs(). The main enhacement is that get_blas_funcs() now also accepts a single string as input parameter and a dtype, so that fetching the BLAS function for a specific type becomes more natural. For example, fetching the gemm routine for a single-precision complex number now looks like this: [cc lang="python"] gemm = scipy.linalg.get_blas_funcs('gemm', dtype=np.complex64) [/cc] compared to the clumsy old syntax: [cc lang="python"] X = np.empty(0, dtype=np.complex64) gemm, = scipy.linalg.get_blas_funcs(('gemm',), (X,)) [/cc]