Data scientists, machine learning engineers, and AI researchers all have their own skillsets. But what special quality do they have in common?

They are all masters of deep learning.

We often hear about AI, or self-driving cars, or algorithmic magic at Google, Facebook, and Amazon. But it is not magic - it is deep learning. And more specifically, it is usually deep neural networks - the single algorithm that rules them all.

In this course, we'll teach you to master Deep Learning. We start with the basics and take you step by step toward building your very first (or second, or third...) deep learning algorithm; we program everything in Python and explain each line of code. We do this early on to give you the confidence to progress to the more complex topics we cover.

All sophisticated concepts we teach are explained intuitively. You'll get fully acquainted with TensorFlow and NumPy, two tools that are essential for creating and understanding Deep Learning algorithms. You'll explore layers, their building blocks, and activations - sigmoid, tanh, ReLu, softmax, and more.

You'll understand the backpropagation process, intuitively and mathematically. You'll be able to spot and prevent overfitting, one of the biggest issues in machine and deep learning. You'll master state-of-the-art initialization methods. Don't know what initialization is? We explain that, too. you'll learn how to build deep neural networks using real data, implemented by real companies in the real world-templates included! Also, you will create your very own deep learning algorithm.

Take the first step toward a satisfying data science career and becoming a Master of Deep Learning.

All the code files are placed at

Citación :

https://github.com/PacktPublishing/Master-Deep-Learning-with-TensorFlow-2.0-in-Python-2019

Citación :

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