Actions
Engagement
  • Author Author: rscasny
  • Date Created: Date Created
  • Last Updated Last Updated:
  • Views 2985 views
  • Likes 0 likes
  • Comments 32 comments
Related
Recommended

What is Your Interest in AI and Machine Learning?

I am preparing my schedule for topics in 2019 for ESSENTIALS, as well as other programs such as RoadTests and/or Webinars. I have discussed with others about offering a several part series on artificial intelligence or machine learning. I think it's topic that everyone should have knowledge in. My intent was that the first module might be more general knowledge, but the subsequent ones would be much more in depth -- perhaps more like a tutorial.

 

I am not seeing a lot of content on this topic on the community. Never much in the way of discussions. Hence, this poll. If you have the time, please respond. Thanks.

 

Randall Scasny

-element14

Parents

  • AI has been around for more than 40 years, going through many phases of hopes and disillusions. It is rather a vast area, but the new boost of successful research and interest in the academic and industrial sectors is mostly due to the progresses in the branches of Machine and Deep Learning techniques. The picture below shows how AI, Machine Learning and Deep Learning relate to each other (in a simplistic form):

    image

    There are several factors that have contributed, and are essential, to the progress made in the this field:

     

    - the tremendous improvement in processing power (thanks to the Moore's Law)

    - the availability of a vast amount of data to be consumed (the famous Big Data)

    - the improvement of the techniques used to process the data

     

    Personally, I believe the most fascinating and exciting area, and the most active currently, is the study and application of Deep Learning (DL) techniques.

     

    Unlike the typical AI approach of using some "pre-coded" logic, to solve problems, Deep Learning techniques leverage the ability of some neural network to "learn" how to solve a problem by using training datasets.

     

    It is definitely a fascinating field, and it can offer a plethora of possible applications to solve many different problems in many diverse fields.

     

    To anybody who likes to start learning about all this, Andrew Ng's courses on Coursera (Machine Learning base courses and the Deep Learning specialization courses) are great, although they do require some commitment (time and money).

     

    Alternatively, there are several books published by Packt Publishing on the topic, which are available for free (although they require you to sign up to their website). You can find some of the books here (check in the "Free Machine Learning eBooks" section).    

     

    And, if you fancy to get your hands dirty, there are some DL frameworks you can use to implement your first DL project. The one I found easier to start with is Caffe | Deep Learning Framework. It can be used with Python, which makes a good rapid development environment.

     

    Fabio

     

    P.S: I have a vested interest in this area, as currently I'm doing some R&D on Deep Learning applied to prediction algoritms...

Comment

  • AI has been around for more than 40 years, going through many phases of hopes and disillusions. It is rather a vast area, but the new boost of successful research and interest in the academic and industrial sectors is mostly due to the progresses in the branches of Machine and Deep Learning techniques. The picture below shows how AI, Machine Learning and Deep Learning relate to each other (in a simplistic form):

    image

    There are several factors that have contributed, and are essential, to the progress made in the this field:

     

    - the tremendous improvement in processing power (thanks to the Moore's Law)

    - the availability of a vast amount of data to be consumed (the famous Big Data)

    - the improvement of the techniques used to process the data

     

    Personally, I believe the most fascinating and exciting area, and the most active currently, is the study and application of Deep Learning (DL) techniques.

     

    Unlike the typical AI approach of using some "pre-coded" logic, to solve problems, Deep Learning techniques leverage the ability of some neural network to "learn" how to solve a problem by using training datasets.

     

    It is definitely a fascinating field, and it can offer a plethora of possible applications to solve many different problems in many diverse fields.

     

    To anybody who likes to start learning about all this, Andrew Ng's courses on Coursera (Machine Learning base courses and the Deep Learning specialization courses) are great, although they do require some commitment (time and money).

     

    Alternatively, there are several books published by Packt Publishing on the topic, which are available for free (although they require you to sign up to their website). You can find some of the books here (check in the "Free Machine Learning eBooks" section).    

     

    And, if you fancy to get your hands dirty, there are some DL frameworks you can use to implement your first DL project. The one I found easier to start with is Caffe | Deep Learning Framework. It can be used with Python, which makes a good rapid development environment.

     

    Fabio

     

    P.S: I have a vested interest in this area, as currently I'm doing some R&D on Deep Learning applied to prediction algoritms...

Children
No Data