Artificial intelligence with python pdf free download

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Save my name, email, and website in this browser for the next time I comment. How to Visualize Data with D3 [Video]. How to Visualize Data with R [Video]. There is another thing called the Total Turing Test that deals with vision and movement. To pass this test, the machine needs to see objects using computer vision and move around using Robotics.

Making machines think like humans For decades, we have been trying to get the machine to think like a human. In order to make this happen, we need to understand how humans think in the first place. How do we understand the nature of human thinking? One way to do this would be to note down how we respond to things. But this quickly becomes intractable, because there are too many things to note down.

Another way to do this is to conduct an experiment based on a predefined format. We develop a certain number of questions to encompass a wide variety of human topics, and then see how people respond to it. Once we gather enough data, we can create a model to simulate the human process. This model can be used to create software that can think like humans. Of course this is easier said than done! All we care about is the output of the program given a particular input. If the program behaves in a way that matches human behavior, then we can say that humans have a similar thinking mechanism.

The following diagram shows different levels of thinking and how our brain prioritizes things:. Within computer science, there is a field of study called Cognitive Modeling that deals with simulating the human thinking process. It tries to understand how humans solve problems. It takes the mental processes that go into this problem solving process and turns it into a software model.

This model can then be used to simulate human behavior. Cognitive modeling is used in a variety of AI applications such as deep learning, expert systems, Natural Language Processing, robotics, and so on. Building rational agents A lot of research in AI is focused on building rational agents.

What exactly is a rational agent? Before that, let us define the word rationality. Rationality refers to doing the right thing in a given circumstance. This needs to be performed in such a way that there is maximum benefit to the entity performing the action.

An agent is said to act rationally if, given a set of rules, it takes actions to achieve its goals. It just perceives and acts according to the information that's available. This system is used a lot in AI to design robots when they are sent to navigate unknown terrains. How do we define the right thing? The answer is that it depends on the objectives of the agent.

The agent is supposed to be intelligent and independent. We want to impart the ability to adapt to new situations. It should understand its environment and then act accordingly to achieve an outcome that is in its best interests. The best interests are dictated by the overall goal it wants to achieve. Let's see how an input gets converted to action:. How do we define the performance measure for a rational agent?

One might say that it is directly proportional to the degree of success. The agent is set up to achieve a particular task, so the performance measure depends on what percentage of that task is complete.

But we must think as to what constitutes rationality in its entirety. If it's just about results, can the agent take any action to get there?

Making the right inferences is definitely a part of being rational, because the agent has to act rationally to achieve its goals. This will help it draw conclusions that can be used successively. What about situations where there are no provably right things to do? There are situations where the agent doesn't know what to do, but it still has to do something. In this situation, we cannot include the concept of inference to define rational behavior.

Shaw, and Allen Newell. It was the first useful computer program that came into existence in the AI world. The goal was to make it work as a universal problem-solving machine. Of course there were many software programs that existed before, but these programs performed specific tasks. GPS was the first program that was intended to solve any general problem. GPS was supposed to solve all the problems using the same base algorithm for every problem.

As you must have realized, this is quite an uphill battle! The basic premise is to express any problem with a set of well-formed formulas. These formulas would be a part of a directed graph with multiple sources and sinks. In a graph, the source refers to the starting node and the sink refers to the ending node.

In the case of GPS, the source refers to axioms and the sink refers to the conclusions. Even though GPS was intended to be a general purpose, it could only solve well-defined problems, such as proving mathematical theorems in geometry and logic. It could also solve word puzzles and play chess. The reason was that these problems could be formalized to a reasonable extent.

But in the real world, this quickly becomes intractable because of the number of possible paths you can take. If it tries to brute force a problem by counting the number of walks in a graph, it becomes computationally infeasible. The first step is to define the goals.

Let's say our goal is to get some milk from the grocery store. The next step is to define the preconditions. These preconditions are in reference to the goals.

To get milk from the grocery store, we need to have a mode of transportation and the grocery store should have milk available. After this, we need to define the operators. If my mode of transportation is a car and if the car is low on fuel, then we need to ensure that we can pay the fueling station. We need to ensure that you can pay for the milk at the store. An operator takes care of the conditions and everything that affects them. It consists of actions, preconditions, and the changes resulting from taking actions.

In this case, the action is giving money to the grocery store. Of course, this is contingent upon you having the money in the first place, which is the precondition. By giving them the money, you are changing your money condition, which will result in you getting the milk. GPS will work as long as you can frame the problem like we did just now. The constraint is that it uses the search process to perform its job, which is way too computationally complex and time consuming for any meaningful real-world application.

Building an intelligent agent There are many ways to impart intelligence to an agent. The most commonly used techniques include machine learning, stored knowledge, rules, and so on.

In this section, we will focus on machine learning. In this method, the way we impart intelligence to an agent is through data and training. With machine learning, we want to program our machines to use labeled data to solve a given problem. By going through the data and the associated labels, the machine learns how to extract patterns and relationships.

In the preceding example, the intelligent agent depends on the learning model to run the inference engine. Once the sensor perceives the input, it sends it to the feature extraction block. Once the relevant features are extracted, the trained inference engine performs a prediction based on the learning model.

This learning model is built using machine learning. The inference engine then takes a decision and sends it to the actuator, which then takes the required action in the real world.

There are many applications of machine learning that exist today. It is used in image recognition, robotics, speech recognition, predicting stock market behavior, and so on.

In order to understand machine learning and build a complete solution, you will have to be familiar with many techniques from different fields such as pattern recognition, artificial neural networks, data mining, statistics, and so on.

Before we had machines that could compute, people used to rely on analytical models. These models were derived using a mathematical formulation, which is basically a sequence of steps followed to arrive at a final equation. The problem with this approach is that it was based on human judgment. Hence these models were simplistic and inaccurate with just a few parameters.

We then entered the world of computers. These computers were good at analyzing data. So, people increasingly started using learned models. These models are obtained through the process of training. During training, the machines look at many examples of inputs and outputs to arrive at the equation. These learned models are usually complex and accurate, with thousands of parameters. This gives rise to a very complex mathematical equation that governs the data. Machine Learning allows us to obtain these learned models that can be used in an inference engine.

One of the best things about this is the fact that we don't need to derive the underlying mathematical formula. You don't need to know complex mathematics, because the machine derives the formula based on data.

All we need to do is create the list of inputs and the corresponding outputs. The learned model that we get is just the relationship between labeled inputs and the desired outputs.

Installing Python 3 We will be using Python 3 throughout this book. Make sure you have installed the latest version of Python 3 on your machine.

If you see something like Python 3. If not, installing it is pretty straightforward. Installing on Ubuntu Python 3 is already installed by default on Ubuntu It is a great package installer for Mac OS X and it is really easy to use. Installing on Windows If you use Windows, it is recommended that you use a SciPy-stack compatible distribution of Python 3. Anaconda is pretty popular and easy to use.

The good part about these distributions is that they come with all the necessary packages preinstalled. If you use one of these versions, you don't need to install the packages separately. Installing packages During the course of this book, we will use various packages such as NumPy, SciPy, scikit- learn, and matplotlib. Make sure you install these packages before you proceed.

All these packages can be installed using a one-line command on the terminal. Here are the relevant links for installation:. If you are on Windows, you should have installed a SciPy-stack compatible version of Python 3.

Loading data In order to build a learning model, we need data that's representative of the world. Now that we have installed the necessary Python packages, let's see how to use the packages to interact with data. There are also image datasets available in the scikit-learn package. Summary In this chapter, we learned what AI is all about and why we need to study it. We discussed various applications and branches of AI.

We understood what the Turing test is and how it's conducted. We learned how to make machines think like humans. We discussed the concept of rational agents and how they should be designed. We discussed how to develop an intelligent agent using machine learning.

We covered different types of models as well. We discussed how to install Python 3 on various operating systems. We learned how to install the necessary packages required to build AI applications. We discussed how to use the packages to load data that's available in scikit-learn.

In the next chapter, we will learn about supervised learning and how to build models for classification and regression. By the end of this chapter, you will know about these topics:. What is the difference between supervised and unsupervised learning? What is classification?

How to preprocess data using various methods What is label encoding? What is a confusion matrix? What are Support Vector Machines and how to build a classifier based on that? What is linear and polynomial regression? How to build a linear regressor for single variable and multivariable data How to estimate housing prices using Support Vector Regressor.

Supervised versus unsupervised learning One of the most common ways to impart artificial intelligence into a machine is through machine learning. The world of machine learning is broadly divided into supervised and unsupervised learning. There are other divisions too, but we'll discuss those later. Classification and Regression Using Supervised Learning.

Supervised learning refers to the process of building a machine learning model that is based on labeled training data. For example, let's say that we want to build a system to automatically predict the income of a person, based on various parameters such as age, education, location, and so on.

To do this, we need to create a database of people with all the necessary details and label it. By doing this, we are telling our algorithm what parameters correspond to what income. Based on this mapping, the algorithm will learn how to calculate the income of a person using the parameters provided to it.

Unsupervised learning refers to the process of building a machine learning model without relying on labeled training data. In some sense, it is the opposite of what we just discussed in the previous paragraph.

Since there are no labels available, you need to extract insights based on just the data given to you. For example, let's say that we want to build a system where we have to separate a set of data points into multiple groups.

The tricky thing here is that we don't know exactly what the criteria of separation should be. Hence, an unsupervised learning algorithm needs to separate the given dataset into a number of groups in the best way possible. In this chapter, we will discuss supervised classification techniques.

The process of classification is one such technique where we classify data into a given number of classes. During classification, we arrange data into a fixed number of categories so that it can be used most effectively and efficiently.

In machine learning, classification solves the problem of identifying the category to which a new data point belongs. We build the classification model based on the training dataset containing data points and the corresponding labels. For example, let's say that we want to check whether the given image contains a person's face or not.

We would build a training dataset containing classes corresponding to these two classes: face and no-face. We then train the model based on the training samples we have. This trained model is then used for inference. A good classification system makes it easy to find and retrieve data. This is used extensively in face recognition, spam identification, recommendation engines, and so on. The algorithms for data classification will come up with the right criteria to separate the given data into the given number of classes.

We need to provide a sufficiently large number of samples so that it can generalize those criteria. If there is an insufficient number of samples, then the algorithm will overfit to the training data. This means that it won't perform well on unknown data because it fine-tuned the model too much to fit into the patterns observed in training data. This is actually a very common problem that occurs in the world of machine learning. It's good to consider this factor when you build various machine learning models.

Preprocessing data We deal with a lot of raw data in the real world. Machine learning algorithms expect data to be formatted in a certain way before they start the training process. In order to prepare the data for ingestion by machine learning algorithms, we have to preprocess it and convert it into the right format. Let's see how to do it. Create a new Python file and import the following packages: import numpy as np from sklearn import preprocessing.

We will be talking about several different preprocessing techniques. Let's start with binarization:. Binarization Mean removal Scaling Normalization. Binarization This process is used when we want to convert our numerical values into boolean values.

Let's use an inbuilt method to binarize input data using 2. If you run the code, you will see the following output: Binarized data: [[ 1. Mean removal Removing the mean is a common preprocessing technique used in machine learning.

It's usually useful to remove the mean from our feature vector, so that each feature is centered on zero. We do this in order to remove bias from the features in our feature vector. The preceding line displays the mean and standard deviation of the input data.

As seen from the values obtained, the mean value is very close to 0 and standard deviation is 1. Scaling In our feature vector, the value of each feature can vary between many random values. So it becomes important to scale those features so that it is a level playing field for the machine learning algorithm to train on. We don't want any feature to be artificially large or small just because of the nature of the measurements. If you run the code, you will see the following printed on your Terminal: Min max scaled data: [[ 0.

Each row is scaled so that the maximum value is 1 and all the other values are relative to this value. Normalization We use the process of normalization to modify the values in the feature vector so that we can measure them on a common scale.

In machine learning, we use many different forms of normalization. Some of the most common forms of normalization aim to modify the values so that they sum up to 1. L1 normalization, which refers to Least Absolute Deviations, works by making sure that the sum of absolute values is 1 in each row. L2 normalization, which refers to least squares, works by making sure that the sum of squares is 1.

In general, L1 normalization technique is considered more robust than L2 normalization technique. L1 normalization technique is robust because it is resistant to outliers in the data. A lot of times, data tends to contain outliers and we cannot do anything about it. We want to use techniques that can safely and effectively ignore them during the calculations.

If we are solving a problem where outliers are important, then maybe L2 normalization becomes a better choice. If you run the code, you will see the following printed on your Terminal: L1 normalized data: [[ 0.

Label encoding When we perform classification, we usually deal with a lot of labels. These labels can be in the form of words, numbers, or something else. The machine learning functions in sklearn expect them to be numbers. So if they are already numbers, then we can use them directly to start training.

But this is not usually the case. In the real world, labels are in the form of words, because words are human readable. We label our training data with words so that the mapping can be tracked. To convert word labels into numbers, we need to use a label encoder. Label encoding refers to the process of transforming the word labels into numerical form. This enables the algorithms to operate on our data.

LabelEncoder encoder. You can check the mapping to see that the encoding and decoding steps are correct. Logistic Regression classifier Logistic regression is a technique that is used to explain the relationship between input variables and output variables.

The input variables are assumed to be independent and the output variable is referred to as the dependent variable. The dependent variable can take only a fixed set of values. These values correspond to the classes of the classification problem.

Our goal is to identify the relationship between the independent variables and the dependent variables by estimating the probabilities using a logistic function. This logistic function is a sigmoid curve that's used to build the function with various parameters. It is very closely related to generalized linear model analysis, where we try to fit a line to a bunch of points to minimize the error. Instead of using linear regression, we use logistic regression. Logistic regression by itself is actually not a classification technique, but we use it in this way so as to facilitate classification.

It is used very commonly in machine learning because of its simplicity. Let's see how to build a classifier using logistic regression. Make sure you have Tkinter package installed on your system before you proceed. Create a new Python file and import the following packages. We will be importing a function from the file utilities.

We will be looking into that function very soon. We will train the classifier using this labeled data. Train the classifier using the data that we defined earlier: Train the classifier classifier. We need to define this function before we can use it. We will be using this multiple times in this chapter, so it's better to define it in a separate file and import the function.

This function is given in the utilities. Create a new Python file and import the following packages: import numpy as np import matplotlib. We also defined the minimum and maximum values of X and Y directions that will be used in our mesh grid.

This grid is basically a set of values that is used to evaluate the function, so that we can visualize the boundaries of the classes. Create the figure, pick a color scheme, and overlay all the points: Create a plot plt. Choose a color scheme for the plot plt.

Overlay the training points on the plot plt. Specify the boundaries of the plots using the minimum and maximum values, add the tick marks, and display the figure: Specify the boundaries of the plot plt. Specify the ticks on the X and Y axes plt. The reason is that C imposes a certain penalty on misclassification, so the algorithm customizes more to the training data. You should be careful with this parameter, because if you increase it by a lot, it will overfit to the training data and it won't generalize well.

If you compare with the earlier figure, you will see that the boundaries are now better. Some styles failed to load. Help Create Join Login. Application Development. IT Management. Project Management. Resources Blog Articles. Menu Help Create Join Login. Open Source Commercial. Freshness Recently updated Mit einem Experten sprechen. Linode offers predictable flat fee pricing, which is universal across all 11 of its data centers. Neural Network Intelligence is an open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

NNI Neural Network Intelligence is a lightweight but powerful toolkit to help users automate feature engineering, neural architecture search, hyperparameter tuning and model compression.

The tool manages automated machine learning AutoML experiments, dispatches and runs experiments The purpose of this project is to provide a comprehensive and yet simple course in Machine Learning using Python.

Machine Learning, as a tool for Artificial Intelligence , is one of the most widely adopted scientific fields.

A considerable amount of literature has been published on Machine Learning. The purpose of this project is to provide the most important aspects of Machine Learning by presenting a series of simple and yet comprehensive tutorials using Python. In this project, we built our DeepFaceLab The leading software for creating deepfakes. DeepFaceLab is an open-source deepfake system that enables users to swap the faces on images and on video.

It offers an imperative and easy-to-use pipeline that even those without a comprehensive understanding of the deep learning framework or model implementation can use; and yet also provides a flexible and loose coupling structure for those who want to Keras Python-based neural networks API.

Python Deep Learning library. Qooper helps start and grow mentorship and development programs in companies, universities, and non-profits. Automate profile collection, mentor-matching, training, follow-ups, check-ins and generate reports.

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