Curious about deep learning? Start with the Fundamentals of Deep Learning booklet to learn the essentials on 25 pages – https://misraturp.gumroad.com/l/fdl

Watch my full deep learning course on YouTube
https://youtube.com/playlist?listPLM8lYG2MzHmQn55ii0duXdO9QSoDF5myF

In this video, let's look at the commonly used hyperparameter tuning techniques for neural networks.

We will look at grid search, random search, manual zooming and some other advanced techniques such as Bayesian search, gradient-based search and evolutionary algorithms.

Here are the libraries that implement some of these techniques:
Hyperopt – https://hyperopt.github.io/hyperopt/
Keras tuner – https://keras.io/keras_tuner/
Scikit-optimize – https://scikit-optimize.github.io/stable/
Sklearn-Deap – https://github.com/rsteca/sklearn-deap
Hyperband – https://github.com/zygmuntz/hyperband

SOURCES:
Data Science Kickstarter Mini Course: https://www.misraturp.com/courses/data-science-kick-starter-mini-course
The cheat sheet for pandas: https://misraturp.gumroad.com/l/pandascs
Streamlit template: https://misraturp.gumroad.com/l/stemp
NNs hyperparameters cheat sheet: https://www.misraturp.com/nn-hyperparameters-cheat-sheet
Fundamentals of Deep Learning in 25 pages: https://misraturp.gumroad.com/l/fdl

COURSES:
Practical Data Science: Complete Your First Portfolio Project: https://www.misraturp.com/hods

Website – https://misraturp.com/
Twitter – https://twitter.com/misraturp

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