Matthes - Python Programming: The Crash Course for Python Learn the Secrets of Machine Learning, Data Science Analysis and Artificial Intelligence. Introduction to Deep Learning for Beginners

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Python Programming

The Crash Course to Learn Programming Python Faster and Remember It Longer. Includes Hands-On Projects and Exercises for Machine Learning, Data Science Analysis, and Artificial Intelligence

Table of Contents

Introduction

Congratulations on downloading Python Programming and thank you for doing so.

The following chapters will discuss everything that you need to know in order to get started with Python programming. There may be many different options out there that you can choose from when it comes to coding your programs, but none of them can offer you the versatility, and all of the benefits, that you are going to be able to get with Python, and that is exactly what we are going to discuss when we are in this guidebook.

To start this guidebook, we are going to take a look at a few different topics that are ever more increasingly being discussed in relation to Python. We will spend a few chapters looking at artificial intelligence and deep learning, as well as taking in some information about machine learning before starting our own introduction into the Python language and what it is all about.

After this kind of introduction, it is time to start diving into all of the cool things that Python is able to do with machine learning and deep learning, and what better way to do this than taking a look at some of the different libraries of Python that were designed to work with this topic. We will spend some time looking at the Scikit-Learn library, how to create some neural networks with this library, and the TensorFlow library as well.

To finish this guidebook, we are going to spend our time taking a look at a few of the different algorithms that work with machine learning, and how you can use Python to help you write the codes that are needed to make those algorithms work. Some of the best machine learning algorithms that we are going to explore will include the K-Means clustering, K-Nearest Neighbors, Decision Trees, Linear Classifiers and more.

Machine learning and artificial intelligence are becoming like big buzzwords in the industry, and many people are interested in learning how to use them for their own needs and their own kind of programming. While this is a great thing, many of these same people worry that these topics are going to be too hard to learn and to understand. With the help of the information in this guidebook, you will be able to really work on all of this, even as a beginner, with the help of Python. When you are ready to learn how to do some Python programming, and how it can work with machine learning, deep learning, and artificial intelligence, make sure to check out this guidebook to help you get started!

There are plenty of books on this subject on the market, thanks again for choosing this one! Every effort was made to ensure it is full of as much useful information as possible, please enjoy!

Chapter 1: What is Deep Learning and Artificial Intelligence

There are a lot of different parts that can come with coding and working with computers in our modern world. Many people feel that if they dont know how to do a complicated kind of coding, then they are not going to be able to do any at all. But this is just not true. There are many complex types of programming that you are able to do, and they are easier to complete than you may think. And in tis guidebook, we are going to take some of our time to discuss many of them.

To start out with in this chapter, we are going to take a look at deep learning and artificial intelligence. Both of these are going to be integral parts when we are looking at some of the topics that are in this guidebook, and knowing how to make them work, and what all we are able to do with them can make a difference in our coding and what we are able to do in the process. Some of the things that we need to know concerning deep learning and artificial intelligence will include the following:

What is deep learning

The first topic that we are going to spend some time on in this chapter is going to be deep learning. Deep learning is actually a type of machine learning (we will discuss more about that in the next chapter), that is going to be responsible for training our computer how to perform some of the life tasks that humans do. There are a variety of tasks that could fall into this category, but things like recognizing speech, identifying what is inside a picture, and making predictions can all fit into this idea.

Instead of organizing the data to run through equations that are already defined, deep learning is going to set up some of the basic parameters that you need for the data, and then will train the computer to learn on its own by being able to see the patterns through many different layers of processing.

Lets dive into this a bit more. Deep learning is important here because it is going to be a foundation of AI, or artificial intelligence, and the current interest that we are seeing in this deep learning is due to how it is related to AI. Deep learning and some of the techniques that come with it have been able to improve the ability of machines to describe, detect, recognize, and classify things in a way that was previously thought to only work for humans.

A good example of this is all of the different things that deep learning is already able to do in our world. We can find that many of the algorithms and techniques that work with it are great at describing the content that it is going to look through, at detecting objects, and recognizing the speech patterns of those using it, while recognizing what is inside one of the images that you present to it. There are many pieces of technology that use this, such as fraud detection, facial recognition, and systems like Cortana and Siri to name a few.

There are already a few developments who are working on advancing what we see with deep learning. Some of these are going to include:

1. The improvements that have been done on some of the algorithms have been able to boost up some of the methods that we see with deep learning.
2. There are some newer approaches to deep learning that have ensured that the accuracy of the models is going to stay intact.
3. There are even some neural networks that have new classes that are developed to fit well for some new applications, including the classification of images and the translation of text.
4. There is a ton of information available from companies, much more than was there in the past. This helps us to build up some new neural networks with a lot of deep layers, including streaming data, physicians notes, investigative transcripts, and even some textual data that you could get from social media.
5. There are also some computational advances of distributed cloud computing and graphics processing units which can give us all of the computing power that is needed in order to make sure the algorithms for deep learning can actually be done.

At the same time that all of that is going on, the human to machine interface that we are used to seeing is changing quite a bit as well. The keyboard and the mouse that were traditionally used are now being replaced with things like natural language, touch, gesture, and swipe. This opens up the field to even more AI and deep learning along the way.

This brings up the question of how deep learning can provide us with a lot of opportunities and applications in the process. A lot of power computationally is going to be needed in order to solve some of the deep learning problems, simply because the nature of iterations in these algorithms. This results in the complexity going up as the layers increase as well. And you also need to be able to handle a ton of data at the same time in order to get the networks trained and ready to go.

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