Linear regression is likely the first algorithm that you would learn when starting down a career path  in data science or AI, because it’s simple to implement and easy to apply in real-time.

Here’s a great primer on how to do linear regression in TensorFlow 2.0.

This algorithm is widely used in data science and statistical fields to model the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). Several types of regression techniques are available based on the data being used. Although linear regression involves simple mathematical logic, its applications are put into use across different fields in real-time. In this article, we’ll discuss linear regression in brief, along with its applications, and implement it using TensorFlow 2.0.

TensorFlow started as an open-source deep learning library and has today evolved into an end to end machine learning platform that includes tools, libraries and resources for the research community to push the state of the art in deep learning and developers in the industry to build ML & DL powered applications.

Best of all, it remains approachable to beginners while fulfilling the needs of cutting edge researchers.

Here’s a great tutorial on how to get started.

In this article, I will focus on the marvel that is TensorFlow 2.0. We will understand how it differs from TensorFlow 1.x, how Keras fits into the picture and how to set up your machine to install and use TensorFlow 2.x. And then comes the icing on the cake – we will implement TensorFlow 2.0 for image classification and text classification tasks!

Learn how to use TensorFlow 2.0 in this full tutorial course for beginners.

This course is designed for Python programmers looking to enhance their knowledge and skills in machine learning and artificial intelligence.

Throughout the 8 modules in this course you will learn about fundamental concepts and methods in ML & AI like core learning algorithms, deep learning with neural networks, computer vision with convolutional neural networks, natural language processing with recurrent neural networks, and reinforcement learning.

Notebooks

Course Contents

  • ⌨️ Module 1: Machine Learning Fundamentals (00:03:25)
  • ⌨️ Module 2: Introduction to TensorFlow (00:30:08)
  • ⌨️ Module 3: Core Learning Algorithms (01:00:00)
  • ⌨️ Module 4: Neural Networks with TensorFlow (02:45:39)
  • ⌨️ Module 5: Deep Computer Vision – Convolutional Neural Networks (03:43:10)
  • ⌨️ Module 6: Natural Language Processing with RNNs (04:40:44)
  • ⌨️ Module 7: Reinforcement Learning with Q-Learning (06:08:00)
  • ⌨️ Module 8: Conclusion and Next Steps (06:48:24)

TensorFlow 2.0 is all about ease of use, and there has never been a better time to get started.

In this talk, learn about model-building styles for beginners and experts, including the Sequential, Functional, and Subclassing APIs.

We will share complete, end-to-end code examples in each style, covering topics from “Hello World” all the way up to advanced examples. At the end, we will point you to educational resources you can use to learn more.

Presented by: Josh Gordon

View the website → https://goo.gle/36smBfW