In many scenarios you need to create more custom training than what model.fit supports and what's great is that we can actually modify how a training step is done inside model.fit and increase the flexibility of training models. In this video I show how this can be done and add more flexibility, then in the next video I will show you how to build custom training loops completely from scratch for scenarios where even more flexibility is needed.

I learned a lot and was inspired to make these TensorFlow videos by the TensorFlow Specialization on Coursera. Below you'll find both affiliate and non-affiliate links, the pricing for you is the same but a small commission goes back to the channel if you buy it through the affiliate link.
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TensorFlow Playlist:
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This is a clip from a conversation with Gary Marcus from Oct 2019. New full episodes every Mon & Thu and 1-2 new clips or a new non-podcast video on all other days. You can watch the full conversation here: https://www.youtube.com/watch?v=vNOTDn3D_RI
(more links below)

Podcast full episodes playlist:
https://www.youtube.com/playli....st?list=PLrAXtmErZgO

Podcasts clips playlist:
https://www.youtube.com/playli....st?list=PLrAXtmErZgO

Podcast website:
https://lexfridman.com/ai

Podcast on iTunes:
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Podcast on Spotify:
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Podcast RSS:
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Note: I select clips with insights from these much longer conversation with the hope of helping make these ideas more accessible and discoverable. Ultimately, this podcast is a small side hobby for me with the goal of sharing and discussing ideas. For now, I post a few clips every Tue & Fri. I did a poll and 92% of people either liked or loved the posting of daily clips, 2% were indifferent, and 6% hated it, some suggesting that I post them on a separate YouTube channel. I hear the 6% and partially agree, so am torn about the whole thing. I tried creating a separate clips channel but the YouTube algorithm makes it very difficult for that channel to grow unless the main channel is already very popular. So for a little while, I'll keep posting clips on the main channel. I ask for your patience and to see these clips as supporting the dissemination of knowledge contained in nuanced discussion. If you enjoy it, consider subscribing, sharing, and commenting.

Gary Marcus is a professor emeritus at NYU, founder of Robust.AI and Geometric Intelligence, the latter is a machine learning company acquired by Uber in 2016. He is the author of several books on natural and artificial intelligence, including his new book Rebooting AI: Building Machines We Can Trust. Gary has been a critical voice highlighting the limits of deep learning and discussing the challenges before the AI community that must be solved in order to achieve artificial general intelligence.

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In this project I built a neural network and trained it to play Snake using a genetic algorithm.

Thanks for watching! Subscribe if you enjoyed and Share if you know anyone who would be interested!

GitHub Repo: https://github.com/greerviau/SnakeAI
Twitter: https://twitter.com/greerviau
Support me on Patreon: https://www.patreon.com/greerviau

Thanks to Josh Cominelli for the music!
Soundcloud: https://soundcloud.com/josh-cominelli

So how do you get started with machine learning and AI? What should you learn first? Well in this video I will be discussing the exact things you need to learn to get started with machine learning. I'll be talking about which language to learn, how much math you need and what ML algorithms to learn first.

⭐️ Thanks to Kite for sponsoring this video! Download the best AI automcolplete for python programming for free: https://kite.com/download/?utm_medium=referral&utm_source=youtube&utm_campaign=techwithtim&utm_content=how-to-get-started-with-machine-learning

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