About : Bramesh Tech Analysis facilitates traders with the technical analysis of stocks, derivatives, futures and commodities, helps them understand the market dynamics of trading world with the extensive use of Gann Price and Time Methodologies. Our array of independent analysis and training sessions would allow traders to make informed and better trading decisions. Our trading methods are rule-based and systematic that will guide you every step of the way.
For more info, please drop a mail to:email@example.com Or contact 09985711341 Video Rating: / 5
Convolutional Neural Networks
About this course: This course will teach you how to build convolutional neural networks and apply it to image data. Thanks to deep learning, computer vision is working far better than just two years ago, and this is enabling numerous exciting applications ranging from safe autonomous driving, to accurate face recognition, to automatic reading of radiology images. You will: – Understand how to build a convolutional neural network, including recent variations such as residual networks. – Know how to apply convolutional networks to visual detection and recognition tasks. – Know to use neural style transfer to generate art. – Be able to apply these algorithms to a variety of image, video, and other 2D or 3D data. This is the fourth course of the Deep Learning Specialization.
Who is this class for: – Learners that took the first two courses of the specialization. The third course is recommended. – Anyone that already has a solid understanding of densely connected neural networks, and wants to learn convolutional neural networks or work with image data.
Deep convolutional models: case studies
Learn about the practical tricks and methods used in deep CNNs straight from the research papers.
• Understand multiple foundational papers of convolutional neural networks
• Analyze the dimensionality reduction of a volume in a very deep network
• Understand and Implement a Residual network
• Build a deep neural network using Keras
• Implement a skip-connection in your network
• Clone a repository from github and use transfer learning
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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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. Video Rating: / 5