Gongsta

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Gongsta

import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences

sentences = [
    'I love eating at McDonalds',
    'I love the food here'
]

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/*  Ultrasonic distance sensor HC-SR04 demo sketch 
 * 
 * This sketch calculates the distance between the HC-SR04 sensor and
 * an object directly infront of it.
 * 
 * 
 * 
 * Components
 * ----------
 *  - Arduino Uno

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import dask
import dask.dataframe as dd
df = dask.datasets.timeseries()

#Unlike Pandas, Dask DataFrames are lazy and so no data is printed here.
df

df.dtypes

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from dask.distributed import Client
client = Client()
client

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import dask.array as da
x = da.random.random((10000, 10000), chunks=(1000, 1000))
x

y = x + x.T
z = y[::2, 5000:].mean(axis=1)
z

z.compute()

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# Importing the Keras libraries and packages
import keras
from keras.models import Sequential #used to initialize the NN
from keras.layers import Dense  #used to build the hidden Layers
from keras.layers import Dropout

# Initialising the ANN
classifier = Sequential()

# Adding the input layer and the first hidden layer with dropout

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import pandas as pd

# Importing the dataset
dataset = pd.read_csv('/Users/stevengong/Desktop/Deep-Learning-course/Volume 1 - Supervised Deep Learning/Part 1 - Artificial Neural Networks (ANN)/Section 4 - Building an ANN/Churn_Modelling.csv')
X = dataset.iloc[:, 3:13].values
y = dataset.iloc[:, 13].values

# Encoding categorical data
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.compose import ColumnTransformer

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# Predicting the Test set results
y_pred = classifier.predict(X_test)
y_pred = (y_pred > 0.5)

# Making the Confusion Matrix
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)

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# Fitting the ANN to the Training set
classifier.fit(X_train, y_train, batch_size = 10, epochs = 100)