Artwork

Conteúdo fornecido por Google Developer Podcasts. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por Google Developer Podcasts ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.
Player FM - Aplicativo de podcast
Fique off-line com o app Player FM !

Machine Learning on the web

29:17
 
Compartilhar
 

Manage episode 359425140 series 3458329
Conteúdo fornecido por Google Developer Podcasts. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por Google Developer Podcasts ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.

Meet Jason Mayes, the public face of Web ML at Google and host of Made With TensorFlow.js. Join us as we talk about Jason’s journey and mission to make machine learning easy, fun and accessible on the web and how getting into the field of machine learning has never been easier.

Made With TensorFlow.js Playlist: http://goo.gle/made-with-tfjs

Learn Web ML on Google Developers: https://goo.gle/Learn-WebML

Connect with Jason on LinkedIn: https://goo.gle/3zcift1

Connect with Jason on Twitter: https://goo.gle/3Xh6MT7

Connect with Jason on Discord: https://goo.gle/3zeoMU1

Guest bio:

Jason Mayes is the public face of Web ML at Google. He helps web engineers around the globe take their first steps with machine learning in JavaScript, pushing the boundaries of what's possible in web-based machine learning which has grown exponentially. He also combines his knowledge of the technical and creative worlds to develop innovative prototypes for Google's largest customers and internal teams with over 15 years experience working within web engineering and investigating emerging technologies.

#AI #ML #MadeWithTFJS #WebML

  continue reading

24 episódios

Artwork

Machine Learning on the web

People of AI

21 subscribers

published

iconCompartilhar
 
Manage episode 359425140 series 3458329
Conteúdo fornecido por Google Developer Podcasts. Todo o conteúdo do podcast, incluindo episódios, gráficos e descrições de podcast, é carregado e fornecido diretamente por Google Developer Podcasts ou por seu parceiro de plataforma de podcast. Se você acredita que alguém está usando seu trabalho protegido por direitos autorais sem sua permissão, siga o processo descrito aqui https://pt.player.fm/legal.

Meet Jason Mayes, the public face of Web ML at Google and host of Made With TensorFlow.js. Join us as we talk about Jason’s journey and mission to make machine learning easy, fun and accessible on the web and how getting into the field of machine learning has never been easier.

Made With TensorFlow.js Playlist: http://goo.gle/made-with-tfjs

Learn Web ML on Google Developers: https://goo.gle/Learn-WebML

Connect with Jason on LinkedIn: https://goo.gle/3zcift1

Connect with Jason on Twitter: https://goo.gle/3Xh6MT7

Connect with Jason on Discord: https://goo.gle/3zeoMU1

Guest bio:

Jason Mayes is the public face of Web ML at Google. He helps web engineers around the globe take their first steps with machine learning in JavaScript, pushing the boundaries of what's possible in web-based machine learning which has grown exponentially. He also combines his knowledge of the technical and creative worlds to develop innovative prototypes for Google's largest customers and internal teams with over 15 years experience working within web engineering and investigating emerging technologies.

#AI #ML #MadeWithTFJS #WebML

  continue reading

24 episódios

Todos os episódios

×
 
Loading …

Bem vindo ao Player FM!

O Player FM procura na web por podcasts de alta qualidade para você curtir agora mesmo. É o melhor app de podcast e funciona no Android, iPhone e web. Inscreva-se para sincronizar as assinaturas entre os dispositivos.

 

Guia rápido de referências