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How to Predict Stock Prices Easily – Intro to Deep Learning #7



Siraj Raval

We’re going to predict the closing price of the S&P 500 using a special type of recurrent neural network called an LSTM network. I’ll explain why we use recurrent nets for time series data, and why LSTMs boost our network’s memory power.

Coding challenge for this video:
https://github.com/llSourcell/How-to-Predict-Stock-Prices-Easily-Demo

Vishal’s winning code:
https://github.com/erilyth/DeepLearning-SirajologyChallenges/tree/master/Image_Classifier

Jie’s runner up code:
https://github.com/jiexunsee/Simple-Inception-Transfer-Learning

More Learning Resources:
http://colah.github.io/posts/2015-08-Understanding-LSTMs/
http://deeplearning.net/tutorial/lstm.html
https://deeplearning4j.org/lstm.html
https://www.tensorflow.org/tutorials/recurrent
http://machinelearningmastery.com/time-series-prediction-lstm-recurrent-neural-networks-python-keras/
https://blog.terminal.com/demistifying-long-short-term-memory-lstm-recurrent-neural-networks/

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music in the intro is chambermaid swing by parov stelar
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20 thoughts on “How to Predict Stock Prices Easily – Intro to Deep Learning #7
  1. Bruv what I have to do to have you host a Netflix show / I'm mad at myself for only discovering your channel today! Keep up the amazing videos, 5/5 explanations

  2. Training new model w/ 3-layer, 128-cell LSTMs
    Training on 15,228,624 character sequences.
    Epoch 1/50
    14871/14871 [==============================] – 987s 66ms/step – loss: 1.4959
    Epoch 2/50
    14871/14871 [==============================] – 969s 65ms/step – loss: 1.3340
    Epoch 3/50
    14871/14871 [==============================] – 961s 65ms/step – loss: 1.3073
    Epoch 4/50
    14871/14871 [==============================] – 964s 65ms/step – loss: 1.2924
    Epoch 5/50
    14871/14871 [==============================] – 969s 65ms/step – loss: 1.2819
    Epoch 6/50
    14871/14871 [==============================] – 969s 65ms/step – loss: 1.2739
    Epoch 7/50
    14871/14871 [==============================] – 963s 65ms/step – loss: 1.2673
    Epoch 8/50
    14871/14871 [==============================] – 997s 67ms/step – loss: 1.2616
    Epoch 9/50
    14871/14871 [==============================] – 1006s 68ms/step – loss: 1.2566
    Epoch 10/50
    14871/14871 [==============================] – 997s 67ms/step – loss: 1.2521
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  3. I didnt get how the train_test_split was done using this "lstm" helper library. What is being used as a label? I mean y_train and y_test. Is it the x_train shifted by 50? How many data points were indexed to the test set?

  4. Wow been looking for something like this for so long.. But I didn't understand a lot since I don't know how to code and don't know anything about machine learning and data science

  5. 8:48 "it's an analytical tool to help us make educated guesses about the direction of the market, that is slightly better than random" —–just like macd, stochastics, bollinger bands, rsi, or any other indicator

  6. if you can predict stock market price, why havn't yo done so yourself and became rich instead of making videos on youtube.

  7. If you use Python and expensive commercial software to develop your machine learning/deep learning applications then you deserve everything that happens to you.

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