​I Built My Own Self-Driving Car. Part #3
In the last article, I have introduced you to a very simple lane detection with OpenCV. Taking into account the simplicity, it was doing well. However, it was not up to my expectations. Looks like the current solution that I am going to show you today does not get better unless you want switch to the Recurrent Convolutional Neural Networks (and this is exactly what I am up to). We are going to talk about a way to get better results based on the Hough Transform method output.

#programming #IT #artificialintellegence #developer #HaarCascade #Python #OpenCV #MadDevs

https://blog.maddevs.io/i-built-my-own-self-driving-car-part-3-1ca1d85e9d59

🔗 I Built My Own Self-Driving Car. Part #3 – Mad Devs

​I Built My Own Self-Driving Car. Part #2

In my last article, I have played around with OpenCV and Haar Cascade Classifier in order to detect vehicles. It did not perform very well though. There were many false predictions and the FPS could not get above 15. Today I will introduce you to the third version of the YOLO algorithm developed for the Darknet and ported to Tensorflow and Keras so we can use a nice and shiny Python environment. We are not going to train our own model today and use pre-trained weights.

#programming #IT #artificialintellegence #developer #HaarCascade #Python #OpenCV #MadDevs

https://blog.maddevs.io/i-built-my-own-self-driving-car-part-2-e3894ce4eb42

🔗 I Built My Own Self-Driving Car. Part #2 – Mad Devs

​New interesting research from Facebook about simulating focus effect with DNN
https://research.fb.com/publications/deepfocus-siggraph-asia-2018/

🔗 DeepFocus: Learned Image Synthesis for Computational Displays
In this paper, we introduce DeepFocus, a generic, end-to-end convolutional neural network designed to efficiently solve the full range of computational tasks for accommodation-supporting HMDs. This network is demonstrated to accurately synthesize defocus blur, focal stacks, multilayer decompositions, and multiview imagery using only commonly available RGB-D images, enabling real-time, near-correct depictions of retinal blur with a broad set of accommodation-supporting HMDs.

Applied Deep Learning with PyTorch - Full Course

🎥 Applied Deep Learning with PyTorch - Full Course
👁 1 раз ⏳ 20404 сек.
In this course you will learn the key concepts behind deep learning and how to apply the concepts to a real-life project using PyTorch and Python.

You'll learn the following:
⌨️ RNNs and LSTMs
⌨️ Sequence Modeling
⌨️ PyTorch
⌨️ Building a Chatbot in PyTorch

⭐️Requirements ⭐️
⌨️ Some Basic High School Mathematics
⌨️ Some Basic Programming Knowledge
⌨️ Some basic Knowledge about Neural Networks

⭐️Contents ⭐️
⌨️ (0:00:08) Recurrent Nerual Networks - RNNs and LSTMs
⌨️ (0:35:54) Sequence-To-Sequence Models
⌨️

​Hands-On with Unsupervised Learning
A quick tutorial on k-means clustering and principal component analysis (PCA).

🔗 Hands-On with Unsupervised Learning – Towards Data Science
A quick tutorial on k-means clustering and principal component analysis (PCA).

​Building an Experimentation Framework for Composite Algorithms

🔗 Building an Experimentation Framework for Composite Algorithms
Its often very useful to develop a framework quickly on top of an existing ML Library for changing the model hyper-parameters and blending…

​Modernizing Life Science Manufacturing with AWS Machine Learning

🔗 Modernizing Life Science Manufacturing with AWS Machine Learning
Learn more about Pharma & Biotech in the Cloud - https://amzn.to/2CWCmhc In this webinar, you will hear from the pharmaceutical manufacturer Novo Nordisk, an...

Margaret Maynard-Reid: Machine Learning for Mobile with TensorFlow

🎥 Margaret Maynard-Reid: Machine Learning for Mobile with TensorFlow
👁 1 раз ⏳ 3547 сек.
Recorded at GDG Boulder on 2019-01-29:
https://www.meetup.com/Google-Developer-Group-Boulder/events/257013855/

Are you interested in getting started with machine learning, and making intelligent apps with the most popular deep learning framework TensorFlow? Come to this talk to learn your various options: from using ready made APIs to training your own custom models. You will learn the end to end process of how to train a model with TensorFlow high level API tf.Keras, convert to a TensorFlow Lite model and

🎥 Introduction to Deep Learning Lecture 5
👁 1 раз ⏳ 5709 сек.

​Премия имени Ильи Сегаловича. Рассказ о компьютерных науках и публикациях по случаю запуска

🔗 Премия имени Ильи Сегаловича. Рассказ о компьютерных науках и публикациях по случаю запуска
Сегодня мы запускаем научную премию имени Ильи Сегаловича iseg. Она будет присуждаться за достижения в области компьютерных наук. Студенты и аспиранты могут...

Configurable Keras Multilayer Perceptron Neural Network - Part 1: Configuration
https://www.youtube.com/watch?v=icVMkykh_qE

🎥 Configurable Keras Multilayer Perceptron Neural Network - Part 1: Configuration
👁 1 раз ⏳ 4554 сек.
First video of 2019. It was a rather terrible one. Kept coughing and having some technical difficulties with python. Disclaimer: Not a python programmer so there will be a lot of bumps along the way.

This video is the first part of a series of videos to implement a feed forward multi-layer perceptron network using keras and python. The difference between this and other videos on the Internet is that we want to build an application (command line application) and not just another set of function calls to bui

🎥 ЛУЧШИЙ COMBO VINE 2019 ТРЕКИ В ОПИСАНИИ
👁 1 раз ⏳ 638 сек.
Решил сегодня сделать топовую Combo Vine, и вот что получилось, приятного просмотра, поддержите лайком, и не забудьте подписаться на канал.
Треки:
1. -----
2. Xk4001 - l watch the purpl
3. Vi Veri - Veniversum Vivus Vici
4. #268 Iceman Bells X Auto Combo
5. OG RON - Act A Fool
6. Cono Puri
7. #855 MOLDAVITE X MXNT - EXODUS X Auto Combo
8. -----
9. #961 Mesqo - Spotlight (Bass Version) X Auto Combo
10. Sagath, Fatal-M - Мистерия
11. Auto Combo - #SIRI
12. ------
13. plug - akihoma
14. Bad Decisions - Yeah

​Книга "Python Data Science Handbook" - это подробное руководство по самым разным вычислительным и статистическим методам, без которых немыслима любая интенсивная обработка данных, научные исследования и передовые разработки. Читатели, уже имеющие опыт программирования и желающие эффективно использовать Python в сфере Data Science, найдут в этой книге ответы на всевозможные вопросы, например: 1) как мне считать этот формат данных в мой скрипт? 2) Как преобразовать, очистить эти данные и манипулировать ими? 3) Как визуализировать данные такого типа? Как при помощи этих данных разобраться в ситуации, получить ответы на вопросы, построить статистические модели или реализовать машинное обучение?

📝 Плас Дж. Вандер - Python для сложных задач наука о данных и машинное обучение (Бестселлеры O'Reilly) - 2018.PDF - 💾12 530 081

Learning how to perform Twitter Sentiment Analysis
https://towardsdatascience.com/keras-challenges-the-avengers-541346acb804?source=collection_home---4------2---------------------

​Find Code for Research papers

https://researchcode.com/

🔗 ResearchCode
Find Code for Research papers

Learn Web Scraping using Python in under 5 minutes
https://towardsdatascience.com/web-scraping-using-python-4cb2faade338?source=collection_home---4------4---------------------

🎥 Making Faces: Conditional Generation of Faces using GANs... - Sophia R Searcy, Justin Blinder
👁 9 раз ⏳ 5227 сек.
PyData NYC 2018

Generative Adversarial Networks are a promising modern application of Deep Learning that allows models to generate examples. However, GANs are complex, difficult to tune, and limited to small examples. We will explore recent GAN progress with a model that generates faces conditional on desired features, like 'smiling' and 'bangs'. Accessible to anyone comfortable with keras or tensorflow.
===
www.pydata.org

PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in

🎥 MIT Self-Driving Cars: State of the Art (2019)
👁 11 раз ⏳ 3274 сек.
Introductory lecture of the MIT Self-Driving Cars series (6.S094) with an overview of the autonomous vehicle industry in 2018 and looking forward to 2019, including Waymo, Tesla, Cruise, Ford, GM, and out-of-the-box ideas of boring tunnels, flying cars, connected vehicles, and more. This covers the state of the art in terms of industry developments and not the perception and planning algorithm development. The latter will be covered in detail in future lectures. For more lecture videos on deep learning, rei

🎥 [Hindi]Deep Learning Object Detection Part 2 : Tensorflow Object Detection with Images|Python|2019
👁 1 раз ⏳ 3874 сек.
[Hindi]Deep Learning Object Detection Part 2 : Tensorflow Object Detection with Images|Python|2019

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🎥 The new kid on the block: deep learning with GluonNLP - Sneha Jha
👁 3 раз ⏳ 2287 сек.
PyData NYC 2018

GluonNLP is one of the newest toolkits for natural language processing (NLP), providing building blocks for text data pipelines and neural models. The focus is on enabling fast prototyping. This tutorial will provide an introduction to GluonNLP with basic examples. There will be a quick refresher for deep learning in NLP before delving into the specifics of the toolkit itself.
===
www.pydata.org

PyData is an educational program of NumFOCUS, a 501(c)3 non-profit organization in the United S