Pattern detection

Pattern detection is a trait that’s

no longer unique to humans. The explosive growth of computer clock speed and memory has led us to an unusual situation: computers now can be used to make predictions, catch anomalies, rank items, and automatically label images.

Inductive Learning

Another name for machine learning is inductive learning, because the

code is trying to infer structure from data alone. It’s like going on vacation in a foreign country, and reading a local fashion magazine to mimic how to dress. You candevelop an idea of the culture from imagesof people wearing local articles of clothing. You’re learning inductively.

When is ML handy?

Machine learning comes in handy when the inner workings aren’t well understood. It provides us with a toolset to write software without defining every detail of the algorithm. The programmer can

leave some values undecidedand let themachine-learning system figure out the best valuesby itself.

Learning and Inference

Typically, we examine an algorithm in two stages: learning and inference. The objective of the learning stage is to

describe the data, which is called the feature vector, and summarize it in a model. The model is our recipe. In effect, the model is a program with a couple of open interpretations, and the data helps disambiguate it.

Learning stage

The

learning stage is the most time consuming. Running an algorithm may take hours, if not days or weeks, to converge into a useful model.

Learning process:

- Training data
- Feature vector
- Learning algorithm
- Model

Feature vector

Feature vectors are

practical simplifications of real-world data, which can be too complicated to deal with. Instead of attending to every little detail of a data item, a feature vector is a practical simplification.

Feature engineering

The

number of features to track also must be just right: not too few, or you’ll lose information you care about, and not too many, or they’ll be unwieldy and time consuming to keep track of. This tremendous effort to select both the number of measurements and which measurements to compare is called feature engineering. Depending on which features you examine, theperformance of your system can fluctuatedramatically. Selecting the right features to track can make up for a weak learning algorithm.

Curse of dimensionality

Adding too many features causes the number of data points required to describe the space to

increase exponentially. That’s why we can’t just design a 1,000,000-dimension feature vector toexhaust all possible factors and then expect the algorithm to learn a model. This phenomenon is called the curse of dimensionality.

Training data

A rule of thumb is to

not evaluate your model on the same data you used to train it, because you already know it works for the training data; you need to tell whether it works for data that wasn’t part of the training set, to make sure your model is general purpose and not biased to the data used to train it.Use the majority of the data for training, and the remaining for testing.

Use 60-20-20

Instead of the 70-30 split, machine-learning practitioners typically divided their data- set 60-20-20.

Training consumes 60%of the dataset, andtesting uses 20%, leaving the other20% for validation, which is explained in the next chapter.

Types of learning:

- Supervised learning - labeled data to developer a useful model
- Unsupervised learning - data comes without any labels
- Reinforced learning - learning system received feedback

Dimensionality reduction

Dimensionality reduction is about

manipulating the data to view it under a much simpler perspective. It’s the ML equivalent of the phrase, “Keep it simple, stupid.” For example, bygetting rid of redundant features, we can explain the same data in a lower-dimensional space and see which features matter.

Supervised learning vs Reinforced learning

Unlike supervised learning, where training data is conveniently labeled by a “teacher,” reinforcement learning

trains on information gathered by observing how the environment reacts to actions. Reinforcement learning is a type of machine learning that interacts with the environment to learn which combination of actions yields the most favorable results.

```
import numpy as np
import tensorflow as tf
print("Hello World!")
```

```
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
import tensorflow as tf
import numpy as np
m1 = [[1.0, 2.0],
[3.0, 4.0]]
m2 = np.array([[1.0, 2.0],
[3.0, 4.0]], dtype=np.float32)
m3 = tf.constant([[1.0, 2.0],
[3.0, 4.0]])
print(type(m1))
print(m1)
print(type(m2))
print(m2)
print(type(m3))
print(m3)
t1 = tf.convert_to_tensor(m1, dtype=tf.float32)
t2 = tf.convert_to_tensor(m2, dtype=tf.float32)
t3 = tf.convert_to_tensor(m3, dtype=tf.float32)
print(type(t1))
print(t1)
print(type(t2))
print(t2)
print(type(t3))
print(t3)
```

Using with Jupyter

Because TensorFlow is primarily a Python library, you should make full use of Python’s interpreter. Jupyter is a mature environment for exercising the interactive nature of the language. It’s a web application that displays computation elegantly so that you can share annotated interactive algorithms with others to teach a technique or demonstrate code.

Using Tensorboard

In machine learning, the most time-consuming part isn’t programming, but it’s

waiting for code to finish running. For example, a famous dataset called ImageNet contains over 14 million images prepared to be used in a machine-learning context. Sometimes it can take up to days or weeks to finish training an algorithm using a large dataset. TensorFlow’s handy dashboard, TensorBoard, affords you aquick peek into the way values are changing in each node of the graph, giving you some idea of how your code is performing.

Regression is a study of how to best fit a curve to summarize your data. It’s one of the most powerful and well-studied types of supervised-learning algorithms. In regression, we try to understand the data points by

discovering the curve that might have generated them.

Success of learning algorithm = low variance + low bias

Varianceindicates how sensitive a prediction is to the training set that was used. Ideally, how you choose the training set shouldn’t matter — meaning alower varianceis desired.Biasindicates the strength of assumptions made about the training dataset. Making too many assumptions might make the model unable to generalize, so you should preferlow biasas well.

Precision

Although the definitions of

true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN)are all useful individually, the true power comes in the interplay between them. Theratio of true positives to total positiveexamples is called precision.

```
precision = TP / (TP + FP)
recall = TP / (TP + FN)
```

Clustering

Clustering is the process of

intelligently categorizing the items in your dataset. The overall idea is thattwo items in the same cluster are “closer” to each otherthan items that belong to separate clusters. That’s the general definition, leaving the interpretation of closeness open.

Audio

The real world is continuous, but computers store data in discrete values. The

sound is digitalized into a discrete representationthrough an analog-to-digital converter (ADC). You can think about sound as fluctuation of a wave over time. But that data is too noisy and difficult to comprehend. An equivalent way to represent a wave is byexamining its frequencies at each time interval. This perspective is called the frequency domain. It’s easy to convert between time domains and frequency domains by using a mathematical operation called adiscrete Fourier transform(commonly implemented using an algorithm known as the fast Fourier transform).

Markov property

Markov realized that what helps

simplify a random system even furtheris considering only a limited area around the gas particle to model it. For example, maybe a gas particle in Europe has barely any effect on a particle in the United States. So why not ignore it? Themathematics is simplified when you look only at a nearby neighborhood instead of the entire system. This notion is now referred to as the Markov property.

Hidden Markov Model

The HMM is a description of

transition probabilities,emission probabilities, and one more thing:initial probabilities. The initial probability is the probability of each state happeningwith no prior knowledge.

3 concepts are defined as follows:

**Initial probability vector**— Starting probability of the states**Transition probability matrix**— Probabilities associated with landing on the next states, given the current state**Emission probability matrix**— Likelihood of an observed state implying the state you’re interested in has occurred

Viterbi decoding algorithm

The Viterbi decoding algorithm finds the

most likely sequence of hidden states, given a sequence of observations. It requires a caching scheme similar to the forward algorithm.

Reinforcement learning

Whereas supervised and unsupervised learning appear at opposite ends of the spectrum, reinforcement learning (RL) exists somewhere in the middle. It’s not supervised learning, because the

training data comes from the algorithm deciding between exploration and exploitation. And it’s not unsupervised, because thealgorithm receives feedback from the environment.