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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Source
Many thanks but you speak faster and that makes grasping the information hard for people who are just learning ML.
Notice me Senpai
Auto
Regressive
Integrated
Moving
Average
do we really need math behind neural net to use them efficiently ?
bruh what
Bakchodi karbalo isse bass
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
When you fail at something hide all evidence that you ever tried.
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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Temperature: 0.2
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Temperature: 0.5
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Temperature: 1.0
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the stock market has been of immense success. since i started with Pat Brown in January, i always make huge profits
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?
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
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
i understood everything u said
yea yea i get it… i think
if you can predict stock market price, why havn't yo done so yourself and became rich instead of making videos on youtube.
You talk too fast.
Your forecasting after all these utterings was disaster. You even didnt bother to compare it with a benchmark model
If you use Python and expensive commercial software to develop your machine learning/deep learning applications then you deserve everything that happens to you.
Ayyyy lmao