In this episode of the AI Show, explore updates to the Azure Machine learning service model registry to provide more insights about your model.
Also, learn how you can deploy your models easily without going through the effort of creating additional driver and configuration files.
- Don’t miss new episodes, subscribe to the AI Show
- Create a Free account (Azure)
- AI Blog
- Fast ML
- MIT News | AI
- Medium | Francesca Lazzeri
- Deep Learning vs. Machine Learning
- Get Started with Machine Learning
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
Artificial Intelligence is the subject of an art exhibition at London’s Barbican Museum.
It looks at AI data training and how it can be interpreted differently by humans.
As always BBC Click has great coverage.
Chris Hawkes compares the Python Programming Language and the C# Programming Language in this video.
As a C# developer that has switched to Python, I find both languages powerful and flexible.
More importantly, both are viable languages, just for different types of tasks.
The Economist examines the political ramifications of DeepFakes.
Will Kwan spent 50 days to create an AI Startup, out of the project out of Y Combinator Startup School.
You can try it out here: https://omnipost.co.
I’m building a machine learning/SaaS startup. In this video, I share the results my first 50 days of full-time work, explaining my business strategy, showing the core features I designed and programmed, and summarizing what I learned from my users. I also give a overview of all the programming frameworks and API’s I used.
Siraj Raval shows off examples of machine learning apps from his students.
If you’re wondering about my stance on the recent controversies around Siraj, I recorded a Data Point about that.
Machine Learning powers almost every internet service we use these days, but it’s rare to find a full pipeline example of machine learning being deployed in a web app. In this episode, I’d like to present 5 full-stack machine learning demos submitted as midterm projects from the students of my current course. The midterm assignment was to create a paid machine learning web app, and after receiving countless incredible submissions, I’ve decided to share my favorite 5 publicly. I was surprised by how many students in the course had never coded before and to see them building a full-stack web app in a few weeks was a very fulfilling experience. Use these examples as a template to help you ideate on potential business ideas to make a positive impact in the world using machine learning. And if you’d like, be sure to reach out and support each of the students I’ve demoed here today in any way can you offer. They’ve been working their butts off. Enjoy!
Presentation notebook: https://colab.research.google.com/drive/1m5aLHPnwIhVX8zgMvZUtK4iG9xSaMbk8
- Example 1 (Medicine): Dermatitis Detection
- Example 2 (Entertainment): Script Generator
- Example 3 (Finance): Price Prediction
- Example 4 (Recruiting): Resume Scanner
- Example 5 (Security): Threat Detection
Data may be the new oil, but what about using data to examine oil stockpiles?
Wall Street Journal explains how satellite imagery and AI to get an edge in the oil market.