Monday, December 4, 2023

What Kind Of Math Is Used In Machine Learning

What Is A Tensor And How Is It Used In Machine Learning

Mathematics for Machine Learning [Full Course] | Essential Math for Machine Learning | Edureka

In machine learning, there is much discussion around tensors being the cornerstone data structure. Tensor is a type of data structure used in linear algebra that can be used for arithmetic operations like matrices and vectors. In 2015, researchers at Google came up with TensorFlow, which is now being used in building Machine Learning Software. TensorFlow helps engineers to translate new approaches to artificial intelligence into practical code. Anyone new to TensorFlow has many doubts about tensors. They need to search and read a lot to develop an understanding. This article saves you much time and effort as it explains tensors role in machine learning for beginners.

If you are new to machine learning, it is advisable to take up a machine learning course to understand the basics before learning about tensors.

Well Why Do We Need To Learn Ml What Is Ml

• Machine learning is a specific field of AI where a system learns to find patterns in examples in order to make predictions.
• Computers learning how to do a task without being explicitly programmed to do so.

Or, in a more friendly definition, Machine Learning Algorithms are those that can tell you something interesting about the data , without you having to write any custom code specific to the problem. Instead of writing code explicitly, we feed data to these ML algorithms and they build their own logic based on the data and its patterns.

An example, again, is that you can make an ML model to automatically detect and delete them Good morning wishes posters/images with striking accuracy.

And thats just the tip of the iceberg. Theres a lot more that is done using ML. If you see your daily usage, everything from Google Search prediction, Autocorrect, weather prediction, Google assistant , facial recognition; requires and implements ML in one way or another.

So I guess youd know by now what can ML do.

So heres one on that:

And memes might as well be one good way to get started with ML, and this blog might help.

For those of you who are already Machine Learning Enthusiasts, youd have no difficulty relishing these meticulously made mesmerizing ML memes.

If youre someone who doesnt know much about ML, heres what Andrew Ngs got to say:

So the first question, again, What is ML?

We saw the definition already, well, heres a memers take on this:

So, we now present, this:

So What Is Deep Learning

No, not this .

So Deep learning is a specific type of machine learning using a technique known as a neural network which connects multiple models together to solve even more complex types of problems.

There are different types and Models of ML.

One of the most basic ones is Linear Regression or Regression:

Regression is one of the most important and broadly used machine learning and statistics tools out there. It allows you to make predictions from data by learning the relationship between features of your data and some observed, continuous-valued response. Regression is used in a massive number of applications ranging from predicting stock prices to understanding gene regulatory networks.

Then theres k-means for Clustering algorithms.

Now, clustering means determining how closely related items are to each other, and arranging them to form clusters of related data items. K-means algorithm is an iterative algorithm that tries to partition the dataset into k pre-defined distinct non-overlapping subgroups where each data point belongs to only one group.

See how easily you can grasp this concept using this meme .

Most importantly, theres Neural Network.

Neural networks are a set of algorithms, modelled loosely after the human brain, that are designed to recognize patterns. Thats it. Some nodes or say neurons connected to each other which pass information like the ones in the brain do. Here each neuron processes the info and passes on to the next one.

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The Most Used Machine Learning Applications In Real World

Isnt it true that Machine Learning has now made life a bit easier for us? Well not really, but still it has become a very important part in todays world. This technology is an all-rounder technology. Do you the reason behind it? The reason is that wherever it is used, that field becomes advanced.

Even the devices that you use are now becoming more powerful. These devices include computers and other electronics. This Machine Learning article talks about the various applications of Machine Learning.

Here, we will be looking at various areas of research. We use these Machine Learning applications in our regular lives as well. Without these Machine Learning applications, things would be very difficult for us.

Machine Learning has now expanded to great extents. It now caters to the customers needs at any time. This creates a lot of demand for it in the market. So, let us have a look at some important Machine Learning applications.

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Can Java Be Used For Machine Learning And Data Science

The world is drooling over Artificial Intelligence. From research institutions to corporate houses, every organization aims to create AI-driven systems to build their enterprise. Machine Learning, or more commonly known as ML, is a sub-array of AI. With ML, you can teach the machines to behave like humans, i.e. develop brains in a machine. The result is automated machines that know-how and what is to be done. One commonly used place for AI & ML is Maps. Have you noticed that it shows you the route with the least traffic and the best route? That happens through ML along with other technologies.

Another hot thing in the technological sphere is Big Data and its management. Big data is a terminology utilized for data of all types. It incorporates structured, semi-structured, and unstructured data. Be it any type of organization, you will always have a lot of data related to operations, finance, marketing, manufacturing, sales, etc.

How you utilize and manage this data is the work of data scientists. Machines absorb the information that is further utilized and adopted in AI is all related to Big Data. Hence, to dive into AI, you will have to be accustomed to ML and Big data. Data science, ML, big data, and AI are all interlinked and synchronized.

Why Machine Learning Matters

With the rise in big data, machine learning has become a key technique for solving problems in areas, such as:

• Computational finance, for and algorithmic trading
• Image processing and computer vision, for face recognition, motion detection, and object detection
• Computational biology, for tumor detection, drug discovery, and DNA sequencing
• Energy production, for price and load forecasting
• Automotive, aerospace, and manufacturing, for predictive maintenance
• Natural language processing, for voice recognition applications;

Did You Know You Could Learn Enough Math To Transition Into A New Career In Ml And Ai In As Little As 12

I’m a Physicist by training. I spent over 16 years in Academia learning to be a Physicist, carrying out research in Physics, and publishing peer-reviewed papers in Physics. Becoming a Physicist meant learning a lot of Math – because mathematics is how Physical processes are described and modeled. I took courses on differential and integral calculus, differential equations, tensor calculus , complex numbers, linear algebra, statistics, numerical analysis, and more. I regularly used linear and polynomial;regression, logistic;regression,;neural networks, function minimization, gradient descent, simplex methods, matrix math, vectorization, visualization of multi-dimensional data, etc.

Mathematics and computer programming were essential to my work. Take a look at my;MS Thesis;and;PhD Thesis;that;Simon Fraser University;has made available online.

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When Should You Learn Machine Learning Mathematics

Agreeably, mathematics is not the most fun way to start machine learning education, especially if youre self-learning. Fortunately, as I said at the beginning of this article, you dont need to begin your machine learning education by poring over double integrals, partial derivatives, and mathematical equations that span a pages width.

You can start with some of the more practical resources on data science and machine learning. A good introductory book is Principles of Data Science, which gives you a good overview of data science and machine learning fundamentals along with hands-on coding examples in Python and light mathematics. Hands-on Machine Learningand Python Machine Learning are two other books that are a little more advanced and also give deeper coverage of the mathematical concepts. Udemys Machine Learning A-Z is an online course that combines coding with visualization in a very intuitive way.

I would recommend starting with one or two of the above-mentioned books and courses. They will give you a working knowledge of the basics of machine learning and deep learning and prepare your mind for the mathematical foundations. Once you know have a solid grasp of different machine learning algorithms, learning the mathematical foundations becomes much more pleasant.

Essential Mathematics For Machine Learning

The Mathematics of Machine Learning

Nowadays, machine learning is one of the most trending technologies among researchers, industries and enthusiastic learners because of making human life easier. It is being widely used in almost all areas of the real world, from Google Assistant to self-driving cars. It is about developing models that can automatically extract important information and patterns from data. But here, an important question arises: what is the magic behind ML, and the answer is mathematics. Mathematics is the core of designing ML algorithms that can automatically learn from data and make predictions. Therefore, it is very important to understand the Maths before going into the deep understanding of ML algorithms.

Mathematics has always been a good friend for some people and a phobia or anxiety for some people. Many students don’t find interest in mathematics around the globe as they think that topics covered in mathematics are less or not relevant to practical or real-world problems. But with the growth of machine learning, people are getting motivated to learn mathematics as it is directly used in designing ML algorithms. It is also very helpful to learn the concepts behind this. In this topic, we will learn all the essential concepts of Mathematics that are used in Machine Learning.

Note: It is not required to go deep in learning Mathematics for working with simple machine learning models; rather, knowing essential Maths concepts is enough to understand how it is applied in ML.

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Training A Recurrent Neural Network

The backpropagation algorithm of an artificial neural network is modified to include the unfolding in time to train the weights of the network. This algorithm is based on computing the gradient vector and is called back propagation in time or BPTT algorithm for short. The pseudo-code for training is given below. The value of \$k\$ can be selected by the user for training. In the pseudo-code below \$p_t\$;is the target value at time step t:

• Repeat till stopping criterion is met:
• Set all \$h\$ to zero.
• Repeat for t = 0 to n-k
• Forward propagate the network over the unfolded network for \$k\$ time steps to compute all \$h\$ and \$y\$.
• Compute the error as: \$e = y_-p_\$
• Backpropagate the error across the unfolded network and update the weights.
• Without Further Ado The Top 10 Machine Learning Algorithms For Beginners:

1. Linear Regression

In machine learning, we have a set of input variables that are used to determine an output variable . A relationship exists between the input variables and the output variable. The goal of ML is to quantify this relationship.

In Linear Regression, the relationship between the input variables and output variable is expressed as an equation of the form y = a + bx. Thus, the goal of linear regression is to find out the values of coefficients a and b. Here, a is the intercept and b is the slope of the line.

Figure 1 shows the plotted x and y values for a data set. The goal is to fit a line that is nearest to most of the points. This would reduce the distance between the y value of a data point and the line.

2. Logistic Regression

Linear regression predictions are continuous values , logistic regression predictions are discrete values after applying a transformation function.

Logistic regression is best suited for binary classification: data sets where y = 0 or 1, where 1 denotes the default class. For example, in predicting whether an event will occur or not, there are only two possibilities: that it occurs or that it does not . So if we were predicting whether a patient was sick, we would label sick patients using the value of 1 in our data set.

Logistic regression is named after the transformation function it uses, which is called the logistic function h= 1/ . This forms an S-shaped curve.

3. CART

Figure 3: Parts of a decision tree.

4. Naïve Bayes

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Introduction To Mathematics For Machine Learning

Aspiring Machine Learning Engineers often tend to ask What is the use of Mathematics for Machine Learning when we have computers to do it all?. Well, that is true. Our computers have become capable enough to do the math in split seconds where we would take minutes or hours to perform the calculations. But in reality, its not the ability to solve the math. Rather, it is the eye of how the math needs to be applied.

You need to analyze the data and infer information so that you can create a model that learns from the data. Math can help you in so many ways that it becomes mind-boggling that someone could hate this subject. Of course, doing math by hand is something I hate too but knowing how I use math is enough to explain my love for math.;

Allow me to extend this love to you guys too because I wont be teaching you just the Mathematics for Machine Learning but the various applications you can use it for, in real life!

Probability For Machine Learning

Probability concepts required for machine learning are elementary , but it still requires intuition. It is often used in the form of distributions like Bernoulli distributions, Gaussian distribution, probability density function and cumulative density function. We use them to carry out hypothesis testing where an understanding of probability is quite essential.

You will find many data scientists, even seasoned veterans, who cannot explain the true meaning of the infamous alpha value and the p-value. They are often treated as some unknown strangers who arrived from Pluto, and nobody even cares to ask. You can learn more p-value here.

But the most interesting part in probability is the Bayes theorem. Since our high school, we have been encountering this theorem in many different places. Heres the formula:

We typically get past this formula by simply feeding in the numbers and calculating the answers. But have you ever wondered what Bayes theorem actually tells us, what exactly is the meaning of posterior probability? Why do we even calculate it in the first place?

Lets consider an example :

This is our friend Bob. Being his classmate, we think that he is an introvert guy who often keeps to himself. We believe that he doesnt like making friends.

So, P is called the prior. In this case, we will call it our assumption that Bob rarely likes to make new friends.

Now, he meets Ed in his college.

Unlike Bob, Ed is a laid back guy who is eager to make new friends.

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Why Is Machine Learning Important

In recent years, Machine learning has been used to automate tasks that were considered tasks that could be done only by humans.

These tasks included text generation, image recognition, playing computer games and more.

Machine Learning and AI experts thought that it would take 10 years for a machine to beat the worlds best player at the board game Go. This was in 2014. Enter Googles DeepMind.

They proved them wrong then and there. They showed the world that even in complex board game such as Go, machines could learn the ideal move at the appropriate time.

A little further down the timeline, the OpenAI team developed the Dota Bot which was capable to beating the worlds best Dota team.

The advances in the field of machines playing games are massive.

The economy and our living in general are going to be impacted by machine learning in more ways than you can imagine.

The possibility of work tasks and entire industries being automated arises. This will definitely change the whole job market landscape in a big way.

This is the ideal time to learn machine learning as many companies are hiring data scientists and engineers to get into the machine learning and AI space.

Statistics In Machine Learning

Statistics helps in drawing logical conclusions from the given data. It is a crucial concept that every machine learning engineer/scientist must learn to understand the working of classifications algorithms like logistic regression, distributions, discrimination analysis, and hypothesis testing in Machine learning. It helps in performing the following task:

• It is a collection of tools that helps to identify the goal from the available data and information.
• Statistics helps to understand the data and transform the sample observations into meaningful information.
• No system in the world has perfect data stored and readily available as needed. Every system has data anomalies like incomplete, corrupted data, etc. Statistical concepts will be your best friend to help in such complex situations.
• It helps in finding answers to the questions such as, “Who scored the maximum & minimum in a cricket tournament?” “Which technology is on-trend in 2021?”, and many more.

Some fundamental concepts of Statistics needed for ML are given below:

• Combinatorics
• Conditional and Joint Distributions.

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