Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

Colleagues, in the “Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization” you will develop skills in Applied Machine Learning, Deep Learning, Machine Learning, Artificial Neural Networks, Machine Learning Algorithms, Algorithms, Computer Programming, Mathematics, Python Programming, Mathematical Theory and Analysis, and Human Learning. You will learn the best practices to train and develop test sets and analyze bias/variance for building deep learning applications; be able to use standard neural network techniques such as initialization, L2 and dropout regularization, hyperparameter tuning, batch normalization, and gradient checking; implement and apply a variety of optimization algorithms, such as mini-batch gradient descent, Momentum, RMSprop and Adam, and check for their convergence; and implement a neural network in TensorFlow. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI. Skill-based training modules include: 1) Practical Aspects of Deep Learning, 2) Optimization Algorithms, and 3) Hyperparameter Tuning, Batch Normalization and Programming Frameworks. 

Enroll today (teams & execs welcome): https://imp.i384100.net/LKZ95a 

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