<p>Data scientists, machine learning engineers, and AI researchers all have their own skillsets. However, there is a quality they all have in common. They are all masters of deep learning. </p><p>We often hear about artificial intelligence, self-driving cars, and algorithmic magic at Google, Facebook, and Amazon. All these are related to deep learning. And more specifically, it is usually deep neural networks, the single algorithm that is responsible for them all. </p><p></p><p>In this course, you’ll gain useful insights into deep learning. You’ll start with the basics and then progress toward building a deep learning algorithm. The course will help you learn easily as it programs everything in Python and explains each line of code clearly. All this will help you move on to the more complex topics easily. </p><p></p><p>You'll get familiar with TensorFlow and NumPy, two tools that are essential for creating and understanding deep learning algorithms. You'll also explore layers, along with their building blocks and activations – sigmoid, tanh, ReLU, Softmax, and more. </p><p></p><p>As you progress, you’ll understand the backpropagation process. You'll be able to spot and prevent overfitting, one of the biggest issues in machine and deep learning. The course will then guide you through state-of-the-art initialization methods. Later, you'll learn how to build deep neural networks using real data, implemented by companies in the real world, along with templates. </p><p></p><p>By the end of this course, you will have developed the skills you need to advance in your data science career and confidently build deep learning algorithms. </p><p></p><p>All code files are placed at https://github.com/PacktPublishing/Master-Deep-Learning-with-TensorFlow-2.0-in-Python-2019</p>