PyCon HK 2017 that in fact Recurring Neural Platforms in Python: Keras and TensorFlow on time Series Research

PyCon HK 2017 - Recurrent Neural Networks in Python: Keras and TensorFlow for Time Series Analysis

PyCon Hong Kong 2017 Workshop

Recurrent Neural Networks in Python: Keras and TensorFlow for Time Series Analysis – by Matt O’Connor

A look at neural networks, specifically recurrent neural networks, and how to implement them in Python for various applications including time series (stock prediction) analysis, using popular machine learning libraries Keras and TensorFlow

http://pycon.hk/2017/topics/recurrent-neural-networks-in-python-keras-and-tensorFlow-for-time-series-analysis/
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Utilize Neural Webs for Crypto-currencies Investments

This video shows the workflow to train a small deep neural net for trading cryptocurrencies.
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I coded a Crypto Trading Bot. This is how much it made in a day

I code a crypto trading bot with you, then let’s see how much profit it makes.

Get my coding tips : https://codingtips.jacobamaral.com/join
Website : https://wetradehq.com/mentors/Jacob%20Amaral
GitHub : https://github.com/Jake0303/

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trading bots ​U.S. Government Required Disclaimer – Commodity Futures Trading Commission. Futures and options trading has large potential rewards, but also large potential risk. You must be aware of the risks and be willing to accept them in order to invest in the futures and options markets. Don’t trade with money you can’t afford to lose. This website is neither a solicitation nor an offer to Buy/Sell futures or options. No representation is being made that any account will or is likely to achieve profits or losses similar to those discussed on this website. The past performance of any trading system or methodology is not necessarily indicative of future results.

CFTC RULE 4.41 – HYPOTHETICAL OR SIMULATED PERFORMANCE RESULTS HAVE CERTAIN LIMITATIONS. UNLIKE AN ACTUAL PERFORMANCE RECORD, SIMULATED RESULTS DO NOT REPRESENT ACTUAL TRADING. ALSO, SINCE THE TRADES HAVE NOT BEEN EXECUTED, THE RESULTS MAY HAVE UNDER-OR-OVER COMPENSATED FOR THE IMPACT, IF ANY, OF CERTAIN MARKET FACTORS, SUCH AS LACK OF LIQUIDITY, SIMULATED TRADING PROGRAMS IN GENERAL ARE ALSO SUBJECT TO THE FACT THAT THEY ARE DESIGNED WITH THE BENEFIT OF HINDSIGHT. NO REPRESENTATION IS BEING MADE THAT ANY ACCOUNT WILL OR IS LIKELY TO ACHIEVE PROFIT OR LOSSES SIMILAR TO THOSE SHOWN.
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Pattern Recognition using Artificial Neural Network

A pattern recognition software I wrote in C# using a three-layers neural network with backpropagation.

It was generally supposed to be an Optical Character Recognition software, but it works for other simple patterns as well, such as, happy smiley or sad smiley.
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MIT 6.S094: Convolutional Neural Networks for End-to-End Learning of the Driving Task

This is lecture 3 of course 6.S094: Deep Learning for Self-Driving Cars taught in Winter 2017. This lecture introduces computer vision, convolutional neural networks, and end-to-end learning of the driving task.

INFO:
Slides: http://bit.ly/2HdXYvf
Website: https://deeplearning.mit.edu
GitHub: https://github.com/lexfridman/mit-deep-learning
Playlist: https://goo.gl/SLCb1y

Links to individual lecture videos for the course:

Lecture 1: Introduction to Deep Learning and Self-Driving Cars

Lecture 2: Deep Reinforcement Learning for Motion Planning

Lecture 3: Convolutional Neural Networks for End-to-End Learning of the Driving Task

Lecture 4: Recurrent Neural Networks for Steering through Time

Lecture 5: Deep Learning for Human-Centered Semi-Autonomous Vehicles

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►FREE YOLO GIFT – http://augmentedstartups.info/yolofreegiftsp

►KERAS Course – https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML

Hey guys and welcome to another fun and easy machine tutorial on Convolutional Neural Networks.

What are Convolutional Neural Networks and why are they important?

Convolutional Neural Networks (ConvNets or CNNs) are a category of Neural Networks that have proven very effective in areas such as image recognition and classification. ConvNets have been successful in identifying faces, objects and traffic signs apart from powering vision in robots and self-driving cars.

A ConvNet is able to recognize scenes and the system is able to suggest relevant captions for example (“a girl playing tennis”) while this image shows an example of ConvNets being used for recognizing everyday objects, humans and animals. There are also ConvNets involved in playing games like StarCraft, Mario and Doom.

ConvNets, therefore, are an important tool for most deep learning practitioners today. However, understanding ConvNets and learning to use them for the first time can sometimes be a bit daunting. But don’t worry you are in good hands here at Arduino Startups. If you are new to neural networks in general, I would recommend you check out my lecture on Artificial Neural Networks and then return to this one.

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