University of California, Berkeley full course 2018
This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm.
By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings
https://inst.eecs.berkeley.edu/~cs188/fa18/
This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm.
By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings
https://inst.eecs.berkeley.edu/~cs188/fa18/
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Deep Learning with Applications Using Python
Deep learning course from MIT by Lex Fridman.
New lecture videos regarding on Deep learning .
I highly recommend you to watch these videos
New lecture videos regarding on Deep learning .
I highly recommend you to watch these videos
AI and Machine Learning: How to learn them visually
https://medium.freecodecamp.org/ai-and-machine-learning-how-to-learn-them-visually-79a56d9b7dbb?source=rss----336d898217ee---4
https://medium.freecodecamp.org/ai-and-machine-learning-how-to-learn-them-visually-79a56d9b7dbb?source=rss----336d898217ee---4
Here are some of the best resources to improve your coding skills
https://medium.freecodecamp.org/here-are-some-of-the-best-resources-to-improve-your-coding-skills-d97aa0e48fdd?source=rss----336d898217ee---4
https://medium.freecodecamp.org/here-are-some-of-the-best-resources-to-improve-your-coding-skills-d97aa0e48fdd?source=rss----336d898217ee---4
freeCodeCamp.org
25 Free Resources for New JavaScript Developers
by freeCodeCamp 25 Free Resources for New JavaScript Developers We asked our campers to share their favorite free resources for new JavaScript developers on Camper News [http://freecodecamp.com/news]. The list includes some time-tested books as well as podcasts…
Deep Learning State of the Art 👉🏻
- Bert and Natural Language processing
- Tesla Autopilot Hardware v2+: NN at scale
- AdaNet: AutoML with Ensembles
- AutoAugmentation
- Training Deep Networks with Synthetic data
- Segmentation Annotation with Polygon-RNN++
- DAWNBench: Training fast and cheap
- Big GAN: state of the art in image sythesis
- Video to Video Synthesis
- Semantic segmentation
- AlphaZero and openAI Five
- Deep learning frameworks
https://www.youtube.com/watch?v=53YvP6gdD7U
- Bert and Natural Language processing
- Tesla Autopilot Hardware v2+: NN at scale
- AdaNet: AutoML with Ensembles
- AutoAugmentation
- Training Deep Networks with Synthetic data
- Segmentation Annotation with Polygon-RNN++
- DAWNBench: Training fast and cheap
- Big GAN: state of the art in image sythesis
- Video to Video Synthesis
- Semantic segmentation
- AlphaZero and openAI Five
- Deep learning frameworks
https://www.youtube.com/watch?v=53YvP6gdD7U
YouTube
Deep Learning State of the Art (2019) - MIT
New lecture on recent developments in deep learning that are defining the state of the art in our field (algorithms, applications, and tools). This is not a complete list, but hopefully includes a good sampling of new exciting ideas. For more lecture videos…
Review: DeepLabv3 — Atrous Convolution (Semantic Segmentation)
https://towardsdatascience.com/review-deeplabv3-atrous-convolution-semantic-segmentation-6d818bfd1d74
https://towardsdatascience.com/review-deeplabv3-atrous-convolution-semantic-segmentation-6d818bfd1d74
Medium
Review: DeepLabv3 — Atrous Convolution (Semantic Segmentation)
Rethink DeepLab, Better Than PSPNet (The Winner in 2016 ILSVRC Scene Parsing Challenge)
Deep learning courses: lecture slides and notebooks
https://m2dsupsdlclass.github.io/lectures-labs/?fbclid=IwAR3V4f2UB9U9lwq9H_Vzr_E6gfUJJ_tDuXnsw0_459hCRVIjOAsm8B_Hd64
https://m2dsupsdlclass.github.io/lectures-labs/?fbclid=IwAR3V4f2UB9U9lwq9H_Vzr_E6gfUJJ_tDuXnsw0_459hCRVIjOAsm8B_Hd64
lectures-labs
Deep Learning course: lecture slides and lab notebooks
Slides and Jupyter notebooks for the Deep Learning lectures at Master Year 2 Data Science from Institut Polytechnique de Paris