Benefit Computing Using Python & Ml

Stock Price Prediction Using Python & Machine Learning (LSTM).
In this video you will learn how to create an artificial neural network called Long Short Term Memory to predict the future price of stock.

NOTE: In the video to calculate the RMSE I put the following statement:
rmse=np.sqrt(np.mean((predictions- y_test)**2))

When in fact I meant to put :
rmse=np.sqrt(np.mean(((predictions- y_test)**2)))

You can use the following statements to calculate RMSE:
1. rmse =np.sqrt(np.mean(((predictions- y_test)**2)))
2. rmse = np.sqrt(np.mean(np.power((np.array(y_test)-np.array(predictions)),2)))
3. rmse = np.sqrt(((predictions – y_test) ** 2).mean())

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Continual Neural Networks like twitter (LSTM / RNN) Application by having Keras is that Python

#RNN #LSTM #RecurrentNeuralNetworks #Keras #Python #DeepLearning

In this tutorial, we implement Recurrent Neural Networks with LSTM as example with keras and Tensorflow backend. The same procedure can be followed for a Simple RNN.

We implement Multi layer RNN, visualize the convergence and results. We then implement for variable sized inputs.

Recurrent Neural Networks RNN / LSTM / GRU are a very popular type of Neural Networks which captures features from time series or sequential data. It has amazing results with text and even Image Captioning.

In this example we try to predict the next digit given a sequence of digits. Same concept can be extended to text images and even music.

Find the codes here
GitHub : https://github.com/shreyans29/thesemicolon
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Facebook Stock Speculation By using Python And SVR

Predict FB Stock Price Using Support Vector Regression (SVR) Models In Python

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Training information: Profound Strength Learning For Computerized Dealing Python

In this tutorial, we’ll see an example of deep reinforcement learning for algorithmic trading using BTGym (OpenAI Gym environment API for backtrader backtesting library) and a DQN algorithm from a medium post (link below) to interact with the environment and does the trading.

Access to the code: https://gist.github.com/arsalanaf/d10e0c9e2422dba94c91e478831acb12

Telegram Group: https://t.me/joinchat/DmGkrhIE_g6Mk-zJS6sWgA

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Bitcoin TensorForce Trading Bot: https://github.com/lefnire/tforce_btc_trader
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QuantConnect how to Exchange pairs Trading using Python

Learn how to select correlated pairs to build a long-short hedged pairs trading position with Python in QuantConnect.

Sponsored by QuantConnect

In this video I describe the results of a 14 hour trade bot on binance trading the coin pair ZEN-BNB. We see a profit of 4.4% over 14 hours, beating the ‚market‘ of .7%. Also discussed is the threshold settings for Neural Network strategy on Gekko.

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Intense Understanding in Python | Neural Platforms for Buying and selling | System Practicing Techniques | Quantra

Deep Learning in Python | Neural Networks for Trading | Machine Learning Algorithms | Quantra

** Neural Networks for Trading: https://quantra.quantinsti.com/course/neural-networks-deep-learning-trading-ernest-chan ** START FOR FREE!
This Quantra video on Deep Learning in Python will give you an overview of a course for quants and traders to implement neural network and deep learning in financial markets. Offered by Dr. Ernest Chan, learn to use advanced techniques such as LSTM, RNN in live trading. Below are the topics covered in this course:

1. Neural Networks Trading Strategy
2. Adding deep learning layers
3. Recurrent Neural Networks
4. Long Short Term Memory units
5. Cross Validation in Keras
6. Live Trading models

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