The document provides information about implementing LSTM for stock market analysis. It discusses LSTM architecture, applications of LSTM including time series prediction and speech recognition, advantages such as dealing with vanishing gradients, and disadvantages including longer training time. It also discusses applications of LSTM in areas like robot control, rhythm learning, and handwriting recognition.

1. Implement LSTM.
Perform LSTM on stock
market analysis

2. Course Title: Deep Learning
Course Code: 18CS3074P
Skill number: 5
Group: 180030292 180030331
180030572 180031117
180031222 180031318

3. LSTM
• By Hochreiter and Schmiduber.
• Long short-term memory (LSTM) is an artificial recurrent neural
network (RNN) architecture used in the field of deep learning.

4. Applications
• Widely been used for speech recognition,
language modeling, sentiment analysis and text prediction.
• This is a behavior required in complex problem domains like
machine translation, speech recognition, and more.

5. Advantages
• LSTMs were developed to deal with the vanishing gradient problem that
can be encountered when training traditional RNNs.
• Relative insensitivity to gap length is an advantage of LSTM over
RNNs, hidden Markov models and other sequence learning methods in
numerous applications.

6. Disadvantages
• LSTMs take longer to train.
• LSTMs require more memory to train.
• LSTMs are easy to overfit.
• Dropout is much harder to implement in LSTMs.
• LSTMs are sensitive to different random weight initializations.

7. Applications Of LSTM
Applications of LSTM include:
• Robot control.
• Time series prediction.
• Speech recognition.
• Rhythm learning.
• Music composition.
• Grammar learning.
• Handwriting recognition.
• Human action recognition.

8. Long Short Term Memory
• Drawback of RNN pushed the scientists to develop and invent a new
variant of the RNN model, called Long Short Term Memory.
• LSTM can solve this problem, because it uses gates to control the
memorizing process.
• capable of learning long-term dependencies.
• Remembering information for long periods of time is practically their
default behavior

9. LSTM
• The symbols used here have following
meaning:
• a) X : Scaling of information
• b)+ : Adding information
• c) σ : Sigmoid layer
• d) tanh: tanh layer
• e) h(t-1) : Output of last LSTM unit
• f) c(t-1) : Memory from last LSTM unit
• g) X(t) : Current input
• h) c(t) : New updated memory
• i) h(t) : Current output

10. DropOut
• from keras.layers import Dropout
• The Dropout layer randomly sets input units to 0 with a frequency of rate
at each step during training time, which helps prevent overfitting
• from sklearn.preprocessing import MinMaxScaler
• Transform features by scaling each feature to a given range.
• between zero and one.

11. Sequential
• Sequential model is a linear stack of layers.You can create a Sequential
model by passing a list of layer instances to the constructor:
• from keras.models import Sequential
• from keras.layers import Dense, Activation
• model = Sequential([ Dense(32, input_shape=(784,))

12. Code Process
• #Importing the libraries
• #Visualizing the fetched data
• #Data Preprocessing
• #Defining the LSTM Recurrent Model
• #Compiling and fitting the model
• Fetching the test data and preprocessing
• Making predictions on the test data
• Visualizing the prediction

14. Code Implementation: