🎥 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

🔗

​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

CONNECT
Site: https://coryjmaklin.com/
Medium: https://medium.com/@corymaklin
GitHub: https://github.com/corymaklin
Twitter: https://twitter.com/CoryMaklin
Linkedin: https://www.linkedin.com/in/cory-maklin-a51732b7/
Facebook: https://www.facebook.com/cory.maklin
Patreon: https://www.patreon.com/corymaklin

🎥 How to Code Deep Q Learning in Tensorflow (Tutorial)
👁 1 раз ⏳ 2604 сек.
Deep Q Learning w/ Pytorch: https://youtu.be/RfNxXlO6BiA
Where to find data for Deep Learning: https://youtu.be/9oW3WfKk6d4

#Tensorflow #DeepQLearning #OpenAIGym

In today's tutorial we are going to code a Deep Q Network, in the Tensorflow framework, to play the game Breakout, from the OpenAI Gym's Atari library.

We'll split our code into 2 classes: One to handle the neural networks for deep Q learning, and another to handle the agent's functionality, like memory and learning.

My model is still training

🎥 Machine Learning with Scikit-Learn Python | K-fold Cross-validation
👁 1 раз ⏳ 705 сек.
#normalizednerd #python #scikitlearn

In this video, I've explained the concept of k-fold cross-validation and how to implement it in the popular library known as sci-kit learn. Stay tuned more sci-kit learn videos are coming!

Previous video on confusion matrix -
https://www.youtube.com/watch?v=Dr7lbdgzpWM

For more videos please subscribe -
http://bit.ly/normalizedNERD

Playlist Learn Scikit Learn -
https://www.youtube.com/watch?v=mmnLkKYvGG8&list=PLM8wYQRetTxDHDWU-YBPfKXV3G0TKXvpy

Data source -
https:

​Python’s One Liner graph creation library with animations Hans Rosling Style

https://towardsdatascience.com/pythons-one-liner-graph-creation-library-with-animations-hans-rosling-style-f2cb50490396?source=collection_home---4------2---------------------

🔗 Python’s One Liner graph creation library with animations Hans Rosling Style
Animation, One Line graphs. It has it all

​A Data Science Workflow Canvas to Kickstart Your Projects

🔗 A Data Science Workflow Canvas to Kickstart Your Projects
Use this guide to help you complete your data science projects.

​Free online r course
fasteR: Fast Lane to Learning R!
https://github.com/matloff/fasteR

🔗 matloff/fasteR
Fast Lane to Learning R! Contribute to matloff/fasteR development by creating an account on GitHub.