AI News, How can I learn machine learning and deep learning if I have an intense day job?

How can I learn machine learning and deep learning if I have an intense day job?

I would recommend here Python as a language and would recommend below links: Having aced these requirements, you can at long last begin considering Machine Learning.

Most machine learning ventures have a fundamentally the same as work process: STEP 1.) Fabricate your machine learning fundamentals by studying some material regarding the subject: a.) Andrew Ng’s Machine Learning lectures are a great start: Lecture Collection |

e.) “Tthe best machine learning introduction I’ve seen so far.” STEP 2.) Take an online course The main thing I advise somebody who needs to get into machine learning is to take Andrew Ng’s online course.

Courses on Machine Learning for Beginners and Advanced STEP 3.) Some book suggestions My suggested subsequent step is to get a decent ML book (my run down beneath), read the principal introduction sections, and after that bounce to whatever part incorporates an algorithm, you are interested.

You can, in any case, begin with a simple one, for example, L2-regularized Logistic Regression, or k-means, yet you ought to likewise drive yourself to actualize all the more intriguing ones, for example, SVMs.

In any case, other than algorithms, it is additionally critical to know how to set up your data (feature selection, transformation, and compression) and how to assess your models.

It condenses a large portion of the rudiments while presenting the scikit-learn library, which can prove to be useful for execution and further examinations: STEP 5.) Play with some enormous datasets that are openly accessible.

Likewise, by chipping away at a product group you will rapidly figure out how the science and hypothesis of machine learning vary from the training.

4 Steps for Learning Deep Learning

Firstly, if you need some basic information or convincing on why Deep Learning is having a significant impact, check out the following video by Andrew Ng (Optional but recommended) Start with Andrew Ng’s Class on machine learning

His course provides an introduction to various Machine Learning algorithms are out there and, more importantly, the general procedures/methods for machine learning, including data preprocessing, hyper-parameter tuning, etc.

Recent work in combining attention mechanism in LSTM Recurrent Neural networks with external writable memory has meant some interesting work in building systems that can understand, store and retrieve information in a question &

There’re many research variants, datasets, benchmarks, etc that have stemmed from this work, for example, Metamind’s Dynamic Memory Networks for Natural Language Processing Made famous by AlphaGo, the Go-playing system that recently defeated the strongest Go players in history.

Andrej Karpathy’s The Unreasonable Effectiveness of Recurrent Neural Networks While discriminatory models try to detect, identify and separate things, they end up looking for features which differentiate and do not understand data at a fundamental level.

Here are some pointers to help you with continuous learning See ChristosChristofidis/awesome-deep-learning, a curated list of awesome Deep Learning tutorials, projects and communities for more fun

7 Steps to Mastering Machine Learning With Python

This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way.

Fortunately, due to its widespread popularity as a general purpose programming language, as well as its adoption in both scientific computing and machine learning, coming across beginner's tutorials is not very difficult.

If you have no knowledge of programming, my suggestion is to start with the following free online book, then move on to the subsequent materials: If you have experience in programming but not with Python in particular, or if your Python is elementary, I would suggest one or both of the following: And for those looking for a 30 minute crash course in Python, here you go: Of course, if you are an experienced Python programmer you will be able to skip this step.

Gaining an intimate understanding of machine learning algorithms is beyond the scope of this article, and generally requires substantial amounts of time investment in a more academic setting, or via intense self-study at the very least.

The good news is that you don't need to possess a PhD-level understanding of the theoretical aspects of machine learning in order to practice, in the same manner that not all programmers require a theoretical computer science education in order to be effective coders.

For example, when you come across an exercise implementing a regression model below, read the appropriate regression section of Ng's notes and/or view Mitchell's regression videos at that time.

good approach to learning these is to cover this material: This pandas tutorial is good, and to the point: You will see some other packages in the tutorials below, including, for example, Seaborn, which is a data visualization library based on matplotlib.

Machine Learning

Supervised learning algorithms are trained using labeled examples, such as an input where the desired output is known.

The learning algorithm receives a set of inputs along with the corresponding correct outputs, and the algorithm learns by comparing its actual output with correct outputs to find errors.

Through methods like classification, regression, prediction and gradient boosting, supervised learning uses patterns to predict the values of the label on additional unlabeled data.

Popular techniques include self-organizing maps, nearest-neighbor mapping, k-means clustering and singular value decomposition.

10 Free Must-Read Books for Machine Learning and Data Science

What better way to enjoy this spring weather than with some free machine learning and data science ebooks?

The list begins with a base of statistics, moves on to machine learning foundations, progresses to a few bigger picture titles, has a quick look at an advanced topic or 2, and ends off with something that brings it all together.

The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms.

The many topics include neural networks, support vector machines, classification trees and boosting--the first comprehensive treatment of this topic in any book.

The book also contains a number of R labs with detailed explanations on how to implement the various methods in real life settings, and should be a valuable resource for a practicing data scientist.

Avrim Blum, John Hopcroft, and Ravindran Kannan While traditional areas of computer science remain highly important, increasingly researchers of the future will be involved with using computers to understand and extract usable information from massive data arising in applications, not just how to make computers useful on specific well-defined problems.

With this in mind we have written this book to cover the theory likely to be useful in the next 40 years, just as an understanding of automata theory, algorithms, and related topics gave students an advantage in the last 40 years.

The textbook is laid out as a series of small steps that build on each other until, by the time you complete the book, you have laid the foundation for understanding data mining techniques.

But building a machine learning system requires that you make practical decisions: Historically, the only way to learn how to make these 'strategy' decisions has been a multi-year apprenticeship in a graduate program or company.

Machine Learning for Programmers

When you think forward into the future, once you have captured this elusive understanding of machine learning, what does your job look like?

There is a requirement for the user to be able to freehand draw shapes in the software, and for the software to figure out which shape it is and turn it into a crisp unambiguous version and label it appropriately.

You quickly see that the best (and only viable?) way to solve this problem is to devise and train a predictive model and embed it in your software product.

There are variations (such as whether the model is static or updated, and whether it is local or called remotely via an API), but that’s just detail.

What’s key in this scenario is that you have the experience to notice a problem that is best solved with a predictive model and the skills to devise, train and deploy it.

For example, as a part of regular pre-release system testing, you must demonstrate that the accuracy of the model (when validated on historical data) has the same or better skill than the previous version.

You will be expected to build a deep understanding of one specific predictive model and use your experience and skill to improve and verify its accuracy as part of your routine duties.

Although machine learning is a fascinating area, to a developer machine learning algorithms are just another bag of tricks, like multi-threading or 3d graphics programming.

They start with definitions and move on to mathematical descriptions of concepts and algorithms of ever increasing complexity.

Looking back, you realize you were not taught one thing about modern software development practices, languages, tooling, or anything that you can use in your pursuit of creating and delivering software.

Thankfully, programming has been around long enough, is popular enough and is important enough to the economy that we have found other ways to give budding young (or old) programmers the skills they need to actually do the thing they want to do –

It does not make sense to load up a budding programmer’s head with theory on computability or computational complexity, or even deep details of algorithms and data structures.

machine learning, should they really have to go and spend a bunch of years and tens or hundreds of thousands of dollars to get the requisite math and higher degrees?

The reason is, like a computer science course never making it to the subjects that cover the practical concerns of developing and delivering software, machine learning courses and books fall well short.

An approach where you focus on the actual result you want: working real machine learning problems from end-to-end using modern and “best of breed”

Once you know some tooling, it is relatively easy to blast a problem with a machine learning algorithm and call it “done“.

Machine learning tools and libraries come and go, but at any single point in time you have to use something that best maps onto your chosen process of delivering results.

For example, in the scenarios listed above, I would advise the following best of breed tools: In reality, these three tools bleed across the three scenarios depending on the specifics of a situation.

You also need to keep your ear to the ground and jump to newer better tools if and when they are available, forever adapting them to your repeatable process.

Working with image and text data are new and different fields in their own right (computer vision and natural language processing respectively) that require you to learn specialized methods and tooling to those fields.

By this I mean, write up what you did and what you learned into some kind of standalone document so that you can refer back and leverage the results on future and following projects.

portfolio of public GitHub repositories is fast becoming the resume in the hiring process at companies that actually care about skills and delivering results.

Just like development where you don’t need to know a thing about computability or Big O notation to write code and ship useful and reliable software, you can work through machine learning problems end-to-end without a background in statistics, probability and linear algebra.

In the pursuit of getting better results and more accurate predictions, you will draw from any resources you can find, learning just enough to extract the nuggets of wisdom for you to apply on your problem.

If you heart is set on getting that higher degree, why not just start working on machine learning problems first and take a look at a degree in a few weeks or months after you have a small portfolio of completed projects built up.

I love doing research, but I love working real problems and delivering results that clients actually care about a whole lot more.

Also, I was working machine learning problems before I started the degree, I just didn’t realize I already had the resources and a path in front of me.

Before you go off and buy a desktop supercomputer or rent very large EC2 instances, it might be worth spending some time learning how to get the most from these algorithms on smaller better-understood datasets.

If you get caught up in machine learning competitions you will gladly sacrifice a month of evening television to squeeze a few more percent from your algorithm.

That being said, if you start small with a clear process and a best of breed tool, you can work a dataset from end-to-end in an hour or two, perhaps spread over one or two nights.

A few of these and you have a beachhead on a portfolio of completed machine learning projects that you can begin to leverage on larger and more interesting problems.

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