Tensorflow 2.0: Deep Learning and Artificial Intelligence

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Tensorflow 2.0: Deep Learning and Artificial Intelligence Udemy Free Download Neural Networks for Computer Vision, Time Series Forecasting, NLP, GANs, Reinforcement Learning, and More! This course is designed for students who want to learn fast, but there are also “in-depth” sections in case you want to dig a little deeper into the theory

What you’ll learn

  • Artificial Neural Networks (ANNs) / Deep Neural Networks (DNNs)
  • Predict Stock Returns
  • Time Series Forecasting
  • Computer Vision
  • How to build a Deep Reinforcement Learning Stock Trading Bot
  • GANs (Generative Adversarial Networks)
  • Recommender Systems
  • Image Recognition
  • Convolutional Neural Networks (CNNs)
  • Recurrent Neural Networks (RNNs)
  • Use Tensorflow Serving to serve your model using a RESTful API
  • Use Tensorflow Lite to export your model for mobile (Android, iOS) and embedded devices

Description

Welcome to Tensorflow 2.0!

What an exciting time. It’s been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version.

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,

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