This document discusses machine learning using Tensorflow. It begins with an introduction to machine learning basics like loss reduction, datasets, training and test sets, validation sets, feature engineering, and data cleaning. It then provides an overview of Tensorflow, describing it as a framework for building machine learning models that allows using lower-level or higher-level APIs. The document concludes by showing pseudo code and a full code example to define and train a linear regression model with Tensorflow.

1. MACHINE LEARNINGW I T H T E N S O R F L O W

2. I lead the technology team at
DysonSphere
Hello!
I Am VATSAL
You can contact me at
vatsalm@dysonspherelab.com

3. THE
BUZZ-
WORDS
DEEP LEARNING
MACHINE LEARNING
ARTIFICIAL INTELLIGENCE
Breaking down the buzzwords to explain
What each of these terms really mean.

4. How to get started in ML using Tensorflow
Machine Learning

5. BASICS OF MACHINE LEARNING
1. Loss Reduction/ Gradient Descent
2. Dataset
3. Training Sets and Test Sets
4. Validation Sets
5. Feature Engineering
6. Good vs Bad Features
7. Data Cleaning

6. BASICS OF MACHINE LEARNING
Loss Reduction/ Gradient Descent
Finding the right values of learning rate and batch size
to reduce the Root Mean Square(RMS) value of the function.
Descent on the loss curve to find the local minima is Gradient
descent.

7. BASICS OF MACHINE LEARNING
Dataset
The large volume set of data containing the structured or
unstructured information to be used in feature modeling.

8. BASICS OF MACHINE LEARNING
Training Set & Test Set
The entire data is divided into two sets with unequal
proportions and the algorithm is first trained using training
set
After finding the best RMS value the same model is run
using test set to check the accuracy of the outcomes..

9. BASICS OF MACHINE LEARNING
Validation Set
A new partition is introduced to prevent the overfitting
problem of the ML model. This helps model to stay unaware
of the too many things.

10. BASICS OF MACHINE LEARNING
Feature Engineering
The data columns or properties are converted into ML
model consumable features

11. BASICS OF MACHINE LEARNING
Good vs Bad Features
The features must follow certain rules to simplify the
process of ML training thus reducing the cost and time to
train the model and easier to debug and verify the
predictions.
house_type: victorian
unique_house_id: 8SK982ZZ1242Z
house_age_years:
27
house_age: 851472000
quality_rating: -
1

12. BASICS OF MACHINE LEARNING
Data Cleaning
The dataset most often doesn’t come to ML engineers in the
most suitable way. It contains many impurities and
sometimes even false information stored within the system
due to human errors, etc..

13. TENSORFLOW
Tensorflow
Tensorflow is a computational framework for building
machine learning models. TensorFlow provides a variety of
different toolkits that allow you to construct models at your
preferred level of abstraction. You can use lower-level APIs
to build models by defining a series of mathematical
operations. Alternatively, you can use higher-level APIs
(like tf.estimator) to specify predefined architectures, such
as linear regressors or neural networks.

14. TENSORFLOW
Pseudo Code
import tensorflow as tf
# Set up a linear classifier.
classifier = tf.estimator.LinearClassifier(feature_columns)
# Train the model on some example data.
classifier.train(input_fn=train_input_fn, steps=2000)
# Use it to predict.
predictions = classifier.predict(input_fn=predict_input_fn)

15. TENSORFLOW
Full Code
from __future__ import print_function
import math
from IPython import display
import numpy as np
import pandas as pd
from sklearn import metrics
import tensorflow as tf
from tensorflow.python.data import Dataset
tf.logging.set_verbosity(tf.logging.ERROR)
pd.options.display.max_rows = 10
pd.options.display.float_format = '{:.1f}'.format
california_housing_dataframe = pd.read_csv("https://download.mlcc.google.com/mledu-
datasets/california_housing_train.csv", sep=",")
california_housing_dataframe = california_housing_dataframe.reindex(
np.random.permutation(california_housing_dataframe.index))
california_housing_dataframe["median_house_value"] /= 1000.0
california_housing_dataframe
# Define the input feature: total_rooms.
my_feature = california_housing_dataframe[["total_rooms"]]
# Configure a numeric feature column for total_rooms.
feature_columns = [tf.feature_column.numeric_column("total_rooms")]
# Define the label.
targets = california_housing_dataframe["median_house_value"]
# Use gradient descent as the optimizer for training the model.
my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)
my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)
# Configure the linear regression model with our feature columns and optimizer.
# Set a learning rate of 0.0000001 for Gradient Descent.
linear_regressor = tf.estimator.LinearRegressor(
feature_columns=feature_columns,
optimizer=my_optimizer
)
def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):
"""Trains a linear regression model of one feature.
"""
# Convert pandas data into a dict of np arrays.
features = {key:np.array(value) for key,value in dict(features).items()}
# Construct a dataset, and configure batching/repeating.
ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit
ds = ds.batch(batch_size).repeat(num_epochs)
# Shuffle the data, if specified.
if shuffle:
ds = ds.shuffle(buffer_size=10000)
# Return the next batch of data.
features, labels = ds.make_one_shot_iterator().get_next()
return features, labels
_ = linear_regressor.train(
input_fn = lambda:my_input_fn(my_feature,
targets),
steps=100
)

16. Thanks!
You can find me at: vatsalm@dysonspherelab.com
Any questions?