TENSORFLOW 2.0: DEEP LEARNING AND ARTIFICIAL INTELLIGENCE

Neural Networks for Computer Vision, Time Series Forecasting, GANs, Reinforcement Learning, and More! What you will learn.

Deep Neural Networks are artificial neural networks. Predict stock returns. Forecasting the time series. There is a computer vision. How to build a stock trading bot. GANs arerative adversarial networks. Recommender systems. There is image recognition. Neural networks can be convolutional. Recurrent Neural Networks are neural networks. You can use a RESTful API to serve your model. You can export your model for mobile and embedded devices. Distribution Strategies can be used to parallelize learning. How to build your own custom models with low-level Tensorflow and gradient tape. Natural Language Processing can be used with deep learning. Code is used to demonstrate Moore’s Law. Transfer learning is used to create state-of-the-art image classification.

There are requirements.

You should know how to code in Python. Understand derivatives and probability for the theoretical part.

There is a description.

Tensorflow 2.0 is here to stay!

It is an exciting time. Four years have passed since the release of Tensorflow, and the library has evolved to its official second version.

It is a library for deep learning and artificial intelligence.

Recently, Deep Learning has been responsible for some amazing achievements.

There are pictures of people and things that never existed.

Beating the world champion in the strategy game Go.

There are self-driving cars.

Speech recognition and machine translation are used.

It’s possible to create videos of people doing and saying things they don’t do.

The world’s most popular deep learning library is built by Google, who recently became the most cash-rich company in the world. Many companies use it for machine learning and artificial intelligence.

If you want to do deep learning, you need to know Tensorflow.

All the way up to expert-level students can take this course. How can this be?

You know everything you need to know if you just took my free prerequisite. We will begin with some very basic machine learning models.

Along the way, you will learn about the major deep learning architectures, such as Deep Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks.

Current projects include:

Natural Language Processing is a type of language processing.

Recommender systems.

It is possible to transfer learning for computer vision.

GANs aregenerative adversarial networks.

There is a stock trading bot.

If you have taken all of my previous courses, you will still learn about how to convert your previous code so that it uses Tensorflow 2.0, as well as new and never-before-seen projects such as time series forecasting and how to do stock predictions.

The course is designed for students who want to learn quickly, but there are also in-depth sections if you want to dig a little deeper into the theory.

There are advanced Tensorflow topics.

A model is being deployed with Tensorflow in the cloud.

A model is being deployed with a mobile and embedded application.

Distribution strategies are used in distributed Tensorflow training.

Writing your own model.

The Tensorflow 1.x code can be converted to the Tensorflow 2.0 code.

There are Constants, Variables, and Tensors.

Eager to be executed.

The tape is textured.

If you join now, you can get the temporary section before it goes away.

There are three exercises in DeepDream.

Some features of Tensorflow 2.0 are still being worked on. Stay up to date!

The course focuses on breadth rather than depth, with less theory in favor of building more cool stuff. This is not the course for you. For recommendation systems, natural language processing, reinforcement learning, computer vision, GANs, etc. I have courses that are focused on those topics.

I will see you in class, thanks for reading. Who this course is for?

Beginners to advanced students can learn about deep learning. Screenshots. Tensorflow 2.0 is about deep learning and artificial intelligence.

Video and PDF are included in Tensorflow 2.0: Deep Learning and Artificial Intelligence.