Deep Learning: Neural Networks with Tensorflow

Tensorflow is Google’s library for deep learning and artificial intelligence. Deep Learning has been responsible for some amazing achievements recently, such as:

• Generating beautiful, photo-realistic images of people and things that never existed (GANs)
• Beating world champions in the strategy game Go, and complex video games like CS:GO and Dota 2 (Deep Reinforcement Learning)
• Self-driving cars (Computer Vision)
• Speech recognition (e.g. Siri) and machine translation (Natural Language Processing)
• Even creating videos of people doing and saying things they never did (DeepFakes – a potentially nefarious application of deep learning)

Tensorflow is the world’s most popular library for deep learning, and it’s built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning. In other words, if you want to do deep learning, you gotta know Tensorflow. Deep Learning is one of the most popular fields in computer science today. It has applications in many and very varied domains. With the publishing of much more efficient deep learning models in the early 2010s, we have seen a great improvement in the state of the art in domains like Computer Vision, Natural Language Processing, Image Generation, and Signal Processing. The demand for Deep Learning engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn’t easy. There’s so much information out there, much of which is outdated and many times don’t take the beginners into consideration. In this course, we shall take you on an amazing journey in which you’ll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow (the world’s most popular library for deep learning, and built by Google).