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[3 Day Training Course] Machine Learning
More details are here: Why this training? In this three-day course you will be given clear explanations of machine learning theory, practical scenarios of machine learning algorithms, linear and non-linear regression, overfitting, regularization, loss functions, neural networks, introduction to the backpropagation algorithm and best practices in using different neural networks architectures. The participants will gain experience in machine learning techniques, neural network configuration and model optimization in R and Python. This course is based on hands-on exercises and gives developers an extended overview of various tools, services and frameworks which become essential in machine learning. More details are here:

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What we're about

Does TensorFlow have the potential to become the engine behind most successful consumer and industrial products of the next 10-20 years?

Absolutely! A much better question is how, and in what new ways, can we:

a) train more computers to teach themselves by sifting through massive amounts of data coming out of all sorts of places

b) make these computers available through an API to provide a high-quality answer to anyone who is in need.

Why? Because everyone in need deserves the right answer.

So.... why don't we standardize & democratize the field with and around TensorFlow, and help change the world!?

What. We are a local micro community of those interested in advancing their knowledge of TensorFlow, its use cases and applications.

How. Join our group for a regular dose of human interaction, conversations, smiles, food and drinks on all things TensorFlow:

• How-To's & Best Practices

• Tutorials

• Models

• Algorithms

• Multi-GPU Autoscaling in the cloud

• Tools & Integrations

We will learn how others solve some of the hard problems that exist in the world around us, and how others make predictive APIs and applications.

Join the movement. Let’s change the world. Because everyone in need deserves the right answer.

You are welcome:)

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