ββTutorial on Generative Adversarial Networks (GANs) with Keras and TensorFlow
Nice tutorial with enough theory to understand what you are doing and code to get it done.
Link: https://www.pyimagesearch.com/2020/11/16/gans-with-keras-and-tensorflow/
#Keras #TensorFlow #tutorial #wheretostart #GAN
Nice tutorial with enough theory to understand what you are doing and code to get it done.
Link: https://www.pyimagesearch.com/2020/11/16/gans-with-keras-and-tensorflow/
#Keras #TensorFlow #tutorial #wheretostart #GAN
π©βπOnline lectures on Special Topics in AI: Deep Learning
Fresh free and open playlist on special topics in #DL from University of Wisconsin-Madison. Topics covering reliable deep learning, generalization, learning with less supervision, lifelong learning, deep generative models and more.
Overview Lecture: https://www.youtube.com/watch?v=6LSErxKe634&list=PLKvO2FVLnI9SYLe1umkXsOfIWmEez04Ii
YouTube Playlist: https://www.youtube.com/playlist?list=PLKvO2FVLnI9SYLe1umkXsOfIWmEez04Ii
Syllabus: http://pages.cs.wisc.edu/~sharonli/courses/cs839_fall2020/schedule.html
#wheretostart #lectures #YouTube
Fresh free and open playlist on special topics in #DL from University of Wisconsin-Madison. Topics covering reliable deep learning, generalization, learning with less supervision, lifelong learning, deep generative models and more.
Overview Lecture: https://www.youtube.com/watch?v=6LSErxKe634&list=PLKvO2FVLnI9SYLe1umkXsOfIWmEez04Ii
YouTube Playlist: https://www.youtube.com/playlist?list=PLKvO2FVLnI9SYLe1umkXsOfIWmEez04Ii
Syllabus: http://pages.cs.wisc.edu/~sharonli/courses/cs839_fall2020/schedule.html
#wheretostart #lectures #YouTube
YouTube
CS839 Special Topics in Deep Learning: Course Overview (Lecture 1)
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Data Science by ODS.ai π¦
ββUltimate post on where to start learning DS Most common request we received through the years was to share insights and advices on how to start career in data science and to recommend decent cources. Apparently, using hashtag #wheretostart wasn't enoughβ¦
Hands on ML notebook series
Updated our ultimate post with a series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
Link: https://github.com/ageron/handson-ml
#wheretostart #opensource #jupyter
Updated our ultimate post with a series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.
Link: https://github.com/ageron/handson-ml
#wheretostart #opensource #jupyter
GitHub
GitHub - ageron/handson-ml: βοΈ DEPRECATED β See https://github.com/ageron/handson-ml3 instead.
βοΈ DEPRECATED β See https://github.com/ageron/handson-ml3 instead. - ageron/handson-ml
πOnline Berkeley Deep Learning Lectures 2021
University of Berkeley released its fresh course lectures online for everyone to watch. Welcome Berkeley CS182/282 Deep Learnings - 2021!
YouTube: https://www.youtube.com/playlist?list=PLuv1FSpHurUevSXe_k0S7Onh6ruL-_NNh
#MOOC #wheretostart #Berkeley #dl
University of Berkeley released its fresh course lectures online for everyone to watch. Welcome Berkeley CS182/282 Deep Learnings - 2021!
YouTube: https://www.youtube.com/playlist?list=PLuv1FSpHurUevSXe_k0S7Onh6ruL-_NNh
#MOOC #wheretostart #Berkeley #dl
ββSimple book about #ML β Machine Learning Simplified
The main purpose of the book is to build an intuitive understanding of how algorithms work through basic examples. In order to understand the presented material, it is enough to know basic mathematics and linear algebra.
After reading this book, you will know the basics of supervised learning, understand complex mathematical models, understand the entire pipeline of a typical ML project, and also be able to share your knowledge with colleagues from related industries and with technical professionals.
And for those who find the theoretical part not enough - the book is supplemented with a repository on GitHub, which has Python implementation of all methods and algorithms described in chapters.
Book is absolutely free to read.
Link: themlsbook.com
#wheretostart #book
The main purpose of the book is to build an intuitive understanding of how algorithms work through basic examples. In order to understand the presented material, it is enough to know basic mathematics and linear algebra.
After reading this book, you will know the basics of supervised learning, understand complex mathematical models, understand the entire pipeline of a typical ML project, and also be able to share your knowledge with colleagues from related industries and with technical professionals.
And for those who find the theoretical part not enough - the book is supplemented with a repository on GitHub, which has Python implementation of all methods and algorithms described in chapters.
Book is absolutely free to read.
Link: themlsbook.com
#wheretostart #book
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