🎥 Python Neural Networks - TensorFlow 2.0 Tutorial - What is a Neural Network?
👁 1 раз ⏳ 1629 сек.
This python neural network tutorial series will discuss how to use tensorflow 2.0 and provide tutorials on how to create neural networks with python and tensorflow. This specific video is the introduction video in the series and discusses what a neural network is.

Want a sneak peak into my life? Follow my Instagram @tech_with_tim where I'm going to be filming a video each morning sharing my goals for the day and what I have planned:
https://www.instagram.com/tech_with_tim

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​Announcing Google-Landmarks-v2: An Improved Dataset for Landmark Recognition & Retrieval
http://ai.googleblog.com/2019/05/announcing-google-landmarks-v2-improved.html/

🔗 Announcing Google-Landmarks-v2: An Improved Dataset for Landmark Recognition & Retrieval
Posted by Bingyi Cao and Tobias Weyand, Software Engineers, Google AI Last year we released Google-Landmarks , the largest world-wide la...

​Using What-If Tool to investigate Machine Learning models.

🔗 Using What-If Tool to investigate Machine Learning models.
An open source tool from Google to easily analyze ML models without the need to code.

​Joining Data Sources

🔗 Joining Data Sources
a must-have skill for data science

​Reinforcement Learning for Real-World Robotics

🔗 Reinforcement Learning for Real-World Robotics
Ideas from the literature on RL for real-world robot control

​A library for real-time video stream decoding to CUDA memory

By Constanta: https://github.com/Fonbet/argus-tensor-stream

🔗 Fonbet/argus-tensor-stream
A library for real-time video stream decoding to CUDA memory - Fonbet/argus-tensor-stream

🎥 Deep Learning with PyTorch - Intermediate Workshop
👁 4 раз ⏳ 10562 сек.
https://www.meetup.com/dsnet-blr/events/260996023/

Code:
- https://jvn.io/aakashns/a1b40b04f5174a18bd05b17e3dffb0f0
- https://jvn.io/aakashns/fdaae0bf32cf4917a931ac415a5c31b0

​Machine Learning Classification: The Success of Kickstarter Tech Projects

🔗 Machine Learning Classification: The Success of Kickstarter Tech Projects
As of April 2019, over 400,000 projects have been launched on Kickstarter. With crowdfunding becoming an ever-increasingly popular method…

🎥 OpenCV Python Tutorial For Beginners 16 - matplotlib with OpenCV
👁 1 раз ⏳ 889 сек.
In this video on OpenCV Python Tutorial For Beginners, I am going to show How to use matplotlib with OpenCV. matplotlib is a User friendly, but powerful, plotting library for python. I is commonly used with OpenCv images. pylab is a module in matplotlib that gets installed alongside matplotlib; and matplotlib.pyplot is a module in matplotlib. matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats.

Gist of code I used in this video (How to Disp

🎥 Нейросеть Вконтакте. Интервью с Разработчиком-Исследователем
👁 2 раз ⏳ 1196 сек.
Курс "React.js. Разработка веб-приложений":
https://vk.cc/9lSCcQ

В этом выпуске Loftblog в гостях у самой известной соцсети в России #ВКонтакте. Данил Гаврилов - разработчик из команды прикладных исследований расскажет нам про технологии искусственного интеллекта и их использовании.

Команда прикладных исследований ВКонтакте разработала нейросеть, которая генерирует новостные заголовки на русском и английском языках, cообщает пресс-служба соц.сети.

Ссылка на новость:
https://vk.cc/9l9Wp2

Полезные ссы

​https://m.youtube.com/watch?v=Wxb0jN0X7cs

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​Data Driven Growth with Python 🚀 — Part 1: Know Your Metrics

🔗 Data Driven Growth with Python 🚀 — Part 1: Know Your Metrics
Learn what and how to track with Python

​Наш телеграм канал - tglink.me/ai_machinelearning_big_data

https://towardsdatascience.com/6-bits-of-advice-for-data-scientists-6e5758c52fb2?source=collection_home---4------3---------------------

🔗 6 bits of advice for Data Scientists
Syndromes, Hypotheses, Fallacies, Lies, Awareness, and Probabilities

​Optimal Control: LQR

🔗 Optimal Control: LQR
Intuitive ground-up explanation to LQR, a fundamental concept in optimal control.

​Trick Out Your Terminal in 10 Minutes or Less

🔗 Trick Out Your Terminal in 10 Minutes or Less
How to make a better, faster, stronger, and sexier terminal in mere minutes

🎥 Unsupervised Learning in NLP
👁 1 раз ⏳ 2051 сек.
In this video we learn how to perform topic modeling using unsupervised learning in natural language processing.

Our goal is to train a model that generates topics from a given document/collection of text, without us telling it what the topics are/may be.

LinkedIn: https://www.linkedin.com/in/carlos-lara-1055a16b/
Email: info@poincaregroup.com
Website: https://www.poincaregroup.com

​https://arxiv.org/abs/1903.10176

🔗 DeepRED: Deep Image Prior Powered by RED
Inverse problems in imaging are extensively studied, with a variety of strategies, tools, and theory that have been accumulated over the years. Recently, this field has been immensely influenced by the emergence of deep-learning techniques. One such contribution, which is the focus of this paper, is the Deep Image Prior (DIP) work by Ulyanov, Vedaldi, and Lempitsky (2018). DIP offers a new approach towards the regularization of inverse problems, obtained by forcing the recovered image to be synthesized from a given deep architecture. While DIP has been shown to be effective, its results fall short when compared to state-of-the-art alternatives. In this work, we aim to boost DIP by adding an explicit prior, which enriches the overall regularization effect in order to lead to better-recovered images. More specifically, we propose to bring-in the concept of Regularization by Denoising (RED), which leverages existing denoisers for regularizing inverse problems. Our work shows how the two (DeepRED) can be merged to a highly effective recovery process while avoiding the need to differentiate the chosen denoiser, and leading to very effective results, demonstrated for several tested inverse problems.

🎥 Python Neural Networks - Tensorflow 2.0 Tutorial - Creating a Model
👁 1 раз ⏳ 1068 сек.
This python neural network tutorial covers how to create a model using tensorflow 2.0 and keras. We will then train the model on our dataset and have it predict the classification of our test data.

Text-Based Tutorial: Coming soon..

Tensorflow Website: https://www.tensorflow.org/alpha/tutorials/keras/basic_classification

Want a sneak peak into my life? Follow my Instagram @tech_with_tim where I'm going to be filming a video each morning sharing my goals for the day and what I have planned:
https://www.in

​Light on Math ML: Intuitive Guide to Understanding GloVe Embeddings

🔗 Light on Math ML: Intuitive Guide to Understanding GloVe Embeddings
Understanding theory behind GloVe and Keras implementation!

​Markov Chains and HMMs

🔗 Markov Chains and HMMs
In this article, we’ll focus on Markov Models, where an when they should be used, and Hidden Markov Models. This article will focus on the…

🎥 Machine Learning: Dimensionality Reduction With Principal Component Analysis
👁 2 раз ⏳ 848 сек.
In this video, we cover how to reduce the number of features using principal component analysis.

Video explaining PCA in depth:
https://www.youtube.com/watch?v=g-Hb26agBFg&t=1421s

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