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/ Video Rating: / 5
We released an EA based on perceptron. Its logic is simple to understand. Please check source code on our site: www.fintechee.com
We explained how AI(artificial intelligence) works and how to make an EA based on AI via our tutorial page:
www.fintechee.com/neuralnetwork.html Video Rating: / 5
This video shows the workflow to train a small deep neural net for trading cryptocurrencies. Video Rating: / 5
I code a crypto trading bot with you, then let’s see how much profit it makes.
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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. Video Rating: / 5
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.
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
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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