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ALT.Labs 3 : Introduction to Deep Learning using Keras and TensorFlow

Home / EVENTS

ALT.Labs 3 : Introduction to Deep Learning using Keras and TensorFlow

The ALT. Labs series aims to give participants of the community a detailed hands-on experience with latest technologies in the current industrial revolution ranging from IoT, Cloud Computing, Robotics, AI & Big (Extreme) Data and Cyber Security. In this series, as a continuation to the data science portion, the trainer/speaker will go through the basics of Deep Learning using Python 3, a powerful and prominent language used widely for data analysis and data science; as well as Keras, a high level library built on top of Tensor Flow.The participants will be taken through the basic theories of Multi Layer Perceptrons, Artificial Neural Network and the concepts of Deep Learning as well as some algorithms. From there, the participants will also be given some real life examples and have a hands-on coding session using Python 3 and its deep learning library.As this will be a hands-on session, the participants are expected to have their own laptop installed with Python 3, Keras and Jupyter Notebook before coming to the session, or they will have an option of running their codes on Google’s Colab.AgendaTimeActivity8:30 AMRegistration9:00 AMSharing Session by the speaker12:00 PMQ&A Session1:00 PMEndRequirements Bring own laptop Have a pre-installed version of Python 3, Keras, Jupyter Notebook; or Github account and Internet access for Google Colab. If you do not already have Python and Jupyter Notebook installed and wish to install on your local machine, we recommend installing it via Anaconda, go to www.Anaconda.com and install latest version of Python 3 depending on your laptop OS. This is an intermediate-advance Data Science co-lab, so participants are expected to have some working knowledge on Python and its libraries such as Pandas, and NumpyWho should attend? Startups Data Science developers Artificial Intelligence developers Machine learning developers

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Home / EVENTS

ALT.Labs 3 : Introduction to Deep Learning using Keras and TensorFlow

Sep 14, 2019 12:00 AM

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12:00 am

MaGIC (Malaysian Global Innovation & Creativity Centre)

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The ALT. Labs series aims to give participants of the community a detailed hands-on experience with latest technologies in the current industrial revolution ranging from IoT, Cloud Computing, Robotics, AI & Big (Extreme) Data and Cyber Security. In this series, as a continuation to the data science portion, the trainer/speaker will go through the basics of Deep Learning using Python 3, a powerful and prominent language used widely for data analysis and data science; as well as Keras, a high level library built on top of Tensor Flow.The participants will be taken through the basic theories of Multi Layer Perceptrons, Artificial Neural Network and the concepts of Deep Learning as well as some algorithms. From there, the participants will also be given some real life examples and have a hands-on coding session using Python 3 and its deep learning library.As this will be a hands-on session, the participants are expected to have their own laptop installed with Python 3, Keras and Jupyter Notebook before coming to the session, or they will have an option of running their codes on Google’s Colab.AgendaTimeActivity8:30 AMRegistration9:00 AMSharing Session by the speaker12:00 PMQ&A Session1:00 PMEndRequirements Bring own laptop Have a pre-installed version of Python 3, Keras, Jupyter Notebook; or Github account and Internet access for Google Colab. If you do not already have Python and Jupyter Notebook installed and wish to install on your local machine, we recommend installing it via Anaconda, go to www.Anaconda.com and install latest version of Python 3 depending on your laptop OS. This is an intermediate-advance Data Science co-lab, so participants are expected to have some working knowledge on Python and its libraries such as Pandas, and NumpyWho should attend? Startups Data Science developers Artificial Intelligence developers Machine learning developers

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