Building Spatial Intelligence from Real-World 3D Data Using Deep-Learning Framework fVDB
a blog of links related to computer animation and production technology Sponsored by ReelMatters.com
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braindump.me – Building an AI game studio: what we’ve learned so far
https://braindump.me/blog-posts/building-an-ai-game-studio
Braindump is an attempt to imagine what game creation could be like in the brave new world of LLMs and generative AI to give you an entire AI game studio, complete with coders, artists, and so on, to help you create your dream game.
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Lisa Tagliaferri – 3 Python Machine Learning Projects
A Compilation of 3 Python Machine Learning Projects
- How To Build a Machine Learning Classifier in Python with Scikit-learn
- How To Build a Neural Network to Recognize Handwritten Digits with
TensorFlow - Bias-Variance for Deep Reinforcement Learning: How To Build a Bot for Atari with openAI gym
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Tatev Aslanyan – Machine Learning for Beginners 2024: Theory to Practice with Python Project
Especially Crafted For:
- Budding Data Scientists and Python enthusiasts
- Innovators in Machine Learning and AI fields
- Engineers looking to transition into ML roles
- Product Managers eager to incorporate ML insights
- Educational pioneers and high school students exploring tech
- Anyone intrigued by the potential of ML and Python
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Tom Hanks on his debut novel “The Making of Another Major Motion Picture Masterpiece”: Nothing comes easy if you learnt all through mistakes…
https://www.cbc.ca/listen/live-radio/1-50-q/clip/16014382-tom-hanks
Two-time Oscar winner Tom Hanks (Forrest Gump, Philadelphia, A League of Their Own) on his debut novel “The Making of Another Major Motion Picture Masterpiece,” the insecurities he’s felt throughout his career, and what drives his passion for filmmaking when it feels like “the odds are stacked against you.”
Nothing comes easy if you learnt all through mistakes…
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How to learn to become a VFX artist
- Structure your learning time
- Consistency
- Retention
- Mental health
- Don’t feel intimidated
- Don’t feel rushed
- Be kind
- Luck is when preparation meets opportunity
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Foundry Nuke Cattery – A library of open source machine learning models
The Cattery is a library of free third-party machine learning models converted to .cat files to run natively in Nuke, designed to bridge the gap between academia and production, providing all communities access to different ML models that all run in Nuke. Users will have access to state-of-the-art models addressing segmentation, depth estimation, optical flow, upscaling, denoising, and style transfer, with plans to expand the models hosted in the future.
https://www.foundry.com/insights/machine-learning/the-artists-guide-to-cattery
https://community.foundry.com/cattery
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chat.openai – A conversational AI system that listens, learns, and challenges
https://openai.com/blog/chatgpt/
Tell me about when Christopher Columbus came to the US in 2015
This question is a bit tricky because Christopher Columbus died in 1506, so he could not have come to the US in 2015. But let’s pretend for a moment that he did!
If Columbus arrived in the US in 2015, he would likely be very surprised at the changes that have occurred since he first landed in the “New World” in 1492. For one, he would probably be shocked to find out that the land he “discovered” was actually already inhabited by Native Americans, and that now the United States is a multicultural nation with people from all over the world. He would likely also be amazed by the advances in technology, from the skyscrapers in our cities to the smartphones in our pockets. Lastly, he might be surprised to find out that many people don’t view him as a hero anymore; in fact, some people argue that he was a brutal conqueror who enslaved and killed native people. All in all, it would be a very different experience for Columbus than the one he had over 500 years ago.
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Oliver Theobald – Machine Learning For Absolute Beginners
http://infinity.wecabrio.com/B07335JNW1-machine-learning-for-absolute-beginners-a-plain-e.pdf
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Google’s ’20% rule’ shows exactly how much time you should spend learning new skills, and why it works
“We encourage our employees, in addition to their regular projects, to spend 20% of their time working on what they think will most benefit Google,”
- Identify Specific Skills: Clearly define the skill you want to learn. Focusing on one specific area rather than spreading yourself thin across multiple subjects helps in achieving faster and more noticeable improvement.
- Seek Value in Every Effort: Even if a project doesn’t lead to immediate success, look for the minimum benefits such as learning new techniques, gaining exposure to different fields, or networking. This ensures your time is never wasted.
- Stay Flexible and Committed: Be disciplined in protecting your learning time, but also flexible enough to adjust when urgent tasks arise. The key is consistency, not a rigid schedule.
- Make it Enjoyable: Incorporate fun elements into your learning process. This could be through interactive methods like working with friends, enjoying relevant media, or engaging in creative projects.
- Think Long-Term: View your learning as a long-term investment. Small, consistent efforts compound over time, leading to significant improvements and opportunities in the future.
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