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

O’Reilly and TensorFlow teamed up to present the first TensorFlow World last week.

It brought together the growing TensorFlow community to learn from each other and explore new ideas, techniques, and approaches in deep and machine learning.

Presenters in the keynote:

  • Jeff Dean, Google
  • Megan Kacholia, Google
  • Frederick Reiss, IBM
  • Theodore Summe, Twitter
  • Craig Wiley, Google
  • Kemal El Moujahid, Google

O’Reilly has a a great round up post from the first ever TensorFlow World conference that took place earlier this week.

People from across the TensorFlow community came together in Santa Clara, California for TensorFlow World . Below you’ll find links to highlights from the event. TensorFlow World 2019 opening keynote Jeff Dean explains why Google open-sourced TensorFlow and discusses its progress. Accelerating ML at Twitter Theodore Summe offers a […]

Here’s a great tutorial on using Keras to create a digit recognizer using the classic MNIST set.

An artificial neural network is a mathematical model that converts a set of inputs to a set of outputs through a number of hidden layers. An ANN works with hidden layers, each of which is a transient form associated with a probability. In a typical neural network, each node of a layer takes all nodes of the previous layer as input. A model may have one or more hidden layers.

Siraj Raval just posted this video on defending AI against adversarial attacks

Machine Learning technology isn’t perfect, it’s vulnerable to many different types of attacks! In this episode, I’ll explain 2 common types of attacks and 2 common types of defenses using various code demos from across the Web. There’s some really dope mathematics involved with adversarial attacks, and it was a lot of fun reading about the ‘cat and mouse’ game between new attack techniques, followed by new defense techniques. I encourage anyone new to the field who finds this stuff interesting to learn more about it. I definitely plan to. Let’s look into some math, code, and examples. Enjoy!

Slideshow for this video:
https://colab.research.google.com/drive/19N9VWTukXTPUj9eukeie55XIu3HKR5TT

Demo project:
https://github.com/jaxball/advis.js

 

The TensorFlow team has been on a journey to make the training, deployment, managing, and scaling of machine learning Machine Learning models as easy as possible.

TensorFlow 2.0 provides a comprehensive ecosystem of tools for developers, enterprises, and researchers who want to push the state-of-the-art machine learning and build scalable ML-powered applications.

This video is also subtitled in Chinese, Indonesian, Italian, Japanese, Korean, Portuguese, and Spanish.


Watch more

Coding TensorFlow → https://goo.gle/2Y43cN4

You may have heard of Deep Dream, program that uses deep learning to, well dream up, imagery based on imagery a neural network is exposed to.

Here’s a great write up on how it works.

Deep Dream Using Tensorflow My image which generated by Deep Dream. Whenever any person hears about Deep Learning or Neural Network the things which first come into their mind are that it’s used for Object Detection, Face Recognition, Natural Language Processing, and Speech Recognition. But Neural Network is also […]