Hertha  Walsh

Hertha Walsh

1602709200

Learning AI/ML: The Hard Way

The Wave and the Curve

Data science, Artificial Intelligence (AI), and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner’s hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.

Finding the Crystal Ball in the Jungle

Prediction and forecasting being my favorite topics, I started finding a way to get into this world of data and algorithms back in early 2019. Another driving force for me to learn AI/ML was my fascination on neural networks that was haunting me since I started learning about computer science. I collected few books, learned some python skills to dive into the crystal ball.

While I was going through the online articles, videos and books, I discovered lots of readily available tools, libraries and APIs for AI/ML. It was like someone who is trying to learn cycling and given a car to drive. Due to my interest in neural networks, I got attracted to most the most interesting sub-set of AI/ML, Deep Learning, which deals with deep neural networks. I couldn’t stop myself from directly jumping into Google Tensorflow (a free Google ML tool) and got overwhelmed by a huge collection of its APIs. I could follow the documentation, write code and even made it work. But there was a problem, I was unable understand why I am doing what I am doing. I was completely drowning with the terms like bios, variance, parameters, feature selection, feature scaling, drop out etc. That’s when I took a break, rewind and learn about the internals of AI/ML rather than just using the APIs and Libs blindly. So, I took the hard way.

On one side, I was allured by the readily available smart AI/ML tools and on the other side, my fascination on neural networks was attracting me to learn it from scratch. Meanwhile, I have spent around a month or two just looking for a path to enter the subject. A huge pool of internet resources made me thoroughly confused in identifying the doorway to the heart of puzzle. I realized, why it is a hard nut for people to learn. Janakiram MSV pointed out the reasons correctly in his article.

However, some were very useful, such as an Introduction to Machine Learning by Prof. Grimson from MIT OpenCourseWare. Though its little long but helpful.

#machine learning #ai #artificial intelligence (ai) #ml #ai guide #ai roadmap

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Buddha Community

Learning AI/ML: The Hard Way
Hertha  Walsh

Hertha Walsh

1602709200

Learning AI/ML: The Hard Way

The Wave and the Curve

Data science, Artificial Intelligence (AI), and Machine Learning (ML), since last five to six years these phrases have made their places in Gartner’s hype cycle curve. Gradually they have crossed the peak and moving toward the plateau. The curve also has few related terms such as Deep Neural Network, Cognitive AutoML etc. This shows that, there is an emerging technology trend around AI/ML which is going to prevail over the software industry during the coming years. Few of their predecessors such as Business Intelligence, Data Mining and Data Warehousing were there even before these years.

Finding the Crystal Ball in the Jungle

Prediction and forecasting being my favorite topics, I started finding a way to get into this world of data and algorithms back in early 2019. Another driving force for me to learn AI/ML was my fascination on neural networks that was haunting me since I started learning about computer science. I collected few books, learned some python skills to dive into the crystal ball.

While I was going through the online articles, videos and books, I discovered lots of readily available tools, libraries and APIs for AI/ML. It was like someone who is trying to learn cycling and given a car to drive. Due to my interest in neural networks, I got attracted to most the most interesting sub-set of AI/ML, Deep Learning, which deals with deep neural networks. I couldn’t stop myself from directly jumping into Google Tensorflow (a free Google ML tool) and got overwhelmed by a huge collection of its APIs. I could follow the documentation, write code and even made it work. But there was a problem, I was unable understand why I am doing what I am doing. I was completely drowning with the terms like bios, variance, parameters, feature selection, feature scaling, drop out etc. That’s when I took a break, rewind and learn about the internals of AI/ML rather than just using the APIs and Libs blindly. So, I took the hard way.

On one side, I was allured by the readily available smart AI/ML tools and on the other side, my fascination on neural networks was attracting me to learn it from scratch. Meanwhile, I have spent around a month or two just looking for a path to enter the subject. A huge pool of internet resources made me thoroughly confused in identifying the doorway to the heart of puzzle. I realized, why it is a hard nut for people to learn. Janakiram MSV pointed out the reasons correctly in his article.

However, some were very useful, such as an Introduction to Machine Learning by Prof. Grimson from MIT OpenCourseWare. Though its little long but helpful.

#machine learning #ai #artificial intelligence (ai) #ml #ai guide #ai roadmap

Xander  Hane

Xander Hane

1617468000

How to Get Started With AI in 2021 and Keep Up with Latest Innovations in ML

This is a complete guide to start and improve your knowledge of machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!

#learn-ai #ai #artificial-intelligence #machine-learning #deep-learning #learn-machine-learning #youtube-transcripts #youtubers #web-monetization

What is Machine learning and Why is it Important?

Machine learning is quite an exciting field to study and rightly so. It is all around us in this modern world. From Facebook’s feed to Google Maps for navigation, machine learning finds its application in almost every aspect of our lives.

It is quite frightening and interesting to think of how our lives would have been without the use of machine learning. That is why it becomes quite important to understand what is machine learning, its applications and importance.

To help you understand this topic I will give answers to some relevant questions about machine learning.

But before we answer these questions, it is important to first know about the history of machine learning.

A Brief History of Machine Learning

You might think that machine learning is a relatively new topic, but no, the concept of machine learning came into the picture in 1950, when Alan Turing (Yes, the one from Imitation Game) published a paper answering the question “Can machines think?”.

In 1957, Frank Rosenblatt designed the first neural network for computers, which is now commonly called the Perceptron Model.

In 1959, Bernard Widrow and Marcian Hoff created two neural network models called Adeline, that could detect binary patterns and Madeline, that could eliminate echo on phone lines.

In 1967, the Nearest Neighbor Algorithm was written that allowed computers to use very basic pattern recognition.

Gerald DeJonge in 1981 introduced the concept of explanation-based learning, in which a computer analyses data and creates a general rule to discard unimportant information.

During the 1990s, work on machine learning shifted from a knowledge-driven approach to a more data-driven approach. During this period, scientists began creating programs for computers to analyse large amounts of data and draw conclusions or “learn” from the results. Which finally overtime after several developments formulated into the modern age of machine learning.

Now that we know about the origin and history of ml, let us start by answering a simple question - What is Machine Learning?

#machine-learning #machine-learning-uses #what-is-ml #supervised-learning #unsupervised-learning #reinforcement-learning #artificial-intelligence #ai

Roberta  Ward

Roberta Ward

1595344320

Wondering how to upgrade your skills in the pandemic? Here's a simple way you can do it.

Corona Virus Pandemic has brought the world to a standstill.

Countries are on a major lockdown. Schools, colleges, theatres, gym, clubs, and all other public places are shut down, the country’s economy is suffering, human health is on stake, people are losing their jobs and nobody knows how worse it can get.

Since most of the places are on lockdown, and you are working from home or have enough time to nourish your skills, then you should use this time wisely! We always complain that we want some ‘time’ to learn and upgrade our knowledge but don’t get it due to our ‘busy schedules’. So, now is the time to make a ‘list of skills’ and learn and upgrade your skills at home!

And for the technology-loving people like us, Knoldus Techhub has already helped us a lot in doing it in a short span of time!

If you are still not aware of it, don’t worry as Georgia Byng has well said,

“No time is better than the present”

– Georgia Byng, a British children’s writer, illustrator, actress and film producer.

No matter if you are a developer (be it front-end or back-end) or a data scientisttester, or a DevOps person, or, a learner who has a keen interest in technology, Knoldus Techhub has brought it all for you under one common roof.

From technologies like Scala, spark, elastic-search to angular, go, machine learning, it has a total of 20 technologies with some recently added ones i.e. DAML, test automation, snowflake, and ionic.

How to upgrade your skills?

Every technology in Tech-hub has n number of templates. Once you click on any specific technology you’ll be able to see all the templates of that technology. Since these templates are downloadable, you need to provide your email to get the template downloadable link in your mail.

These templates helps you learn the practical implementation of a topic with so much of ease. Using these templates you can learn and kick-start your development in no time.

Apart from your learning, there are some out of the box templates, that can help provide the solution to your business problem that has all the basic dependencies/ implementations already plugged in. Tech hub names these templates as xlr8rs (pronounced as accelerators).

xlr8rs make your development real fast by just adding your core business logic to the template.

If you are looking for a template that’s not available, you can also request a template may be for learning or requesting for a solution to your business problem and tech-hub will connect with you to provide you the solution. Isn’t this helpful 🙂

Confused with which technology to start with?

To keep you updated, the Knoldus tech hub provides you with the information on the most trending technology and the most downloaded templates at present. This you’ll be informed and learn the one that’s most trending.

Since we believe:

“There’s always a scope of improvement“

If you still feel like it isn’t helping you in learning and development, you can provide your feedback in the feedback section in the bottom right corner of the website.

#ai #akka #akka-http #akka-streams #amazon ec2 #angular 6 #angular 9 #angular material #apache flink #apache kafka #apache spark #api testing #artificial intelligence #aws #aws services #big data and fast data #blockchain #css #daml #devops #elasticsearch #flink #functional programming #future #grpc #html #hybrid application development #ionic framework #java #java11 #kubernetes #lagom #microservices #ml # ai and data engineering #mlflow #mlops #mobile development #mongodb #non-blocking #nosql #play #play 2.4.x #play framework #python #react #reactive application #reactive architecture #reactive programming #rust #scala #scalatest #slick #software #spark #spring boot #sql #streaming #tech blogs #testing #user interface (ui) #web #web application #web designing #angular #coronavirus #daml #development #devops #elasticsearch #golang #ionic #java #kafka #knoldus #lagom #learn #machine learning #ml #pandemic #play framework #scala #skills #snowflake #spark streaming #techhub #technology #test automation #time management #upgrade

Murray  Beatty

Murray Beatty

1598606037

This Week in AI | Rubik's Code

Every week we bring to you the best AI research papers, articles and videos that we have found interesting, cool or simply weird that week.

#ai #this week in ai #ai application #ai news #artificaial inteligance #artificial intelligence #artificial neural networks #deep learning #machine learning #this week in ai