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Artificial Intelligence Application - Intermediate Level (September)

Home / EVENTS

Artificial Intelligence Application - Intermediate Level (September)

Get an Early Bird Discount up to 80% off by sending us an email for Promotional Code to soepromo@mymagic.myDiscount is for a limited time and limited number of tickets only!Session Summary Organizations are using deep learning and AI at every stage of growth, from startups to Fortune 500s. Deep learning, the fastest growing field in AI, is empowering immense progress in emerging markets and will be instrumental in ways we haven’t even imagined.Today’s advanced deep neural networks use algorithms, big data, and the computational power of the GPU to change this dynamic. Machines are now able to learn at a speed, accuracy, and scale that are driving true artificial intelligence and AI Computing.What Will You Learn? Understand how to design, train, and deploy neural network-powered machine learning in your applications. Explore widely used open-source frameworks and NVIDIA’s latest GPU-accelerated deep learning platforms.Course BreakdownThe session will encompass the following content: DAY 1: What is Deep Learning and what are Neural Networks? Deep Learning as a branch of AI Neural networks and their history and relationship to neurons Creating a neural network in Python Artificial Neural Networks (ANN) Intuition Understanding the neuron and neuroscience The activation function (utility function or loss function) How do NN’s work? How do NN’s learn? Gradient descent Stochastic Gradient descent Backpropagation Building an ANN Getting the python libraries Constructing ANN Using the bank customer churn dataset Predicting if customer will leave or not Evaluating Performance of an ANN Evaluating the ANN Improving the ANN Tuning the ANN Hands-On Exercise Participants will be asked to build the ANN from the previous exercise Participants will be asked to improve the accuracy of their ANN Convolutional Neural Networks (CNN) Intuition What are CNN’s? Convolution operation ReLU Layer Pooling Flattening Full Connection Softmax and Cross-entropy Building a CNN Getting the python libraries Constructing a CNN Using the Image classification dataset Predicting the class of an image DAY 2: Evaluating Performance of a CNN Evaluating the CNN Improving the CNN Tuning the CNN Hands-On Exercise Participants will be asked to build the CNN from the previous exercise Participants will be asked to improve the accuracy of their CNN Recurrent Neural Networks (RNN) Intuition What are RNN’s? Vanishing Gradient problem LSTMs Practical intuition LSTM variations Building a RNN Getting the python libraries Constructing RNN Using the stock prediction dataset Predicting stock price Evaluating Performance of a RNN Evaluating the RNN Improving the RNN Tuning the RNN Hands-On Exercise Participants will be asked to build the RNN from the previous exercise Participants will be asked to improve the accuracy of their RNN Day 3 Image Classification with DIGITS How to leverage deep neutral networks (DNN) within the deep learning workflow Process of data preparation, model definition, model training and troubleshooting, validation testing and strategies for improving model performance using GPUs. Train a DNN on your own image classification application Object Detection with DIGITS Train and evaluate an image segmentation network Neutral Network Deployment with DIGITS and TensorRT Uses a trained DNN to make predictions from new data. Show different approaches to deploying a trained DNN for inference. Learn about the role of batch size in inference performance as well as virus optimisations that can be made in the inference process.Pre-Requisites Exposure to Business Intelligence Exposure to Data Storage Solutions/Databases Exposure to introductory level in AIRegister today to secure your seats!If you have any enquiry about the course, please email us at justask@mymagic.myPlease be informed that we will get back to you within 3 working days.**Please be informed that food and beverage will not be provided during this training.

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Home / EVENTS

Artificial Intelligence Application - Intermediate Level (September)

Sep 19, 2018 12:00 AM

-

12:00 am

Vietnam Room (2nd Floor, MaGIC)

RM1,800 (before promotional price)

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Get an Early Bird Discount up to 80% off by sending us an email for Promotional Code to soepromo@mymagic.myDiscount is for a limited time and limited number of tickets only!Session Summary Organizations are using deep learning and AI at every stage of growth, from startups to Fortune 500s. Deep learning, the fastest growing field in AI, is empowering immense progress in emerging markets and will be instrumental in ways we haven’t even imagined.Today’s advanced deep neural networks use algorithms, big data, and the computational power of the GPU to change this dynamic. Machines are now able to learn at a speed, accuracy, and scale that are driving true artificial intelligence and AI Computing.What Will You Learn? Understand how to design, train, and deploy neural network-powered machine learning in your applications. Explore widely used open-source frameworks and NVIDIA’s latest GPU-accelerated deep learning platforms.Course BreakdownThe session will encompass the following content: DAY 1: What is Deep Learning and what are Neural Networks? Deep Learning as a branch of AI Neural networks and their history and relationship to neurons Creating a neural network in Python Artificial Neural Networks (ANN) Intuition Understanding the neuron and neuroscience The activation function (utility function or loss function) How do NN’s work? How do NN’s learn? Gradient descent Stochastic Gradient descent Backpropagation Building an ANN Getting the python libraries Constructing ANN Using the bank customer churn dataset Predicting if customer will leave or not Evaluating Performance of an ANN Evaluating the ANN Improving the ANN Tuning the ANN Hands-On Exercise Participants will be asked to build the ANN from the previous exercise Participants will be asked to improve the accuracy of their ANN Convolutional Neural Networks (CNN) Intuition What are CNN’s? Convolution operation ReLU Layer Pooling Flattening Full Connection Softmax and Cross-entropy Building a CNN Getting the python libraries Constructing a CNN Using the Image classification dataset Predicting the class of an image DAY 2: Evaluating Performance of a CNN Evaluating the CNN Improving the CNN Tuning the CNN Hands-On Exercise Participants will be asked to build the CNN from the previous exercise Participants will be asked to improve the accuracy of their CNN Recurrent Neural Networks (RNN) Intuition What are RNN’s? Vanishing Gradient problem LSTMs Practical intuition LSTM variations Building a RNN Getting the python libraries Constructing RNN Using the stock prediction dataset Predicting stock price Evaluating Performance of a RNN Evaluating the RNN Improving the RNN Tuning the RNN Hands-On Exercise Participants will be asked to build the RNN from the previous exercise Participants will be asked to improve the accuracy of their RNN Day 3 Image Classification with DIGITS How to leverage deep neutral networks (DNN) within the deep learning workflow Process of data preparation, model definition, model training and troubleshooting, validation testing and strategies for improving model performance using GPUs. Train a DNN on your own image classification application Object Detection with DIGITS Train and evaluate an image segmentation network Neutral Network Deployment with DIGITS and TensorRT Uses a trained DNN to make predictions from new data. Show different approaches to deploying a trained DNN for inference. Learn about the role of batch size in inference performance as well as virus optimisations that can be made in the inference process.Pre-Requisites Exposure to Business Intelligence Exposure to Data Storage Solutions/Databases Exposure to introductory level in AIRegister today to secure your seats!If you have any enquiry about the course, please email us at justask@mymagic.myPlease be informed that we will get back to you within 3 working days.**Please be informed that food and beverage will not be provided during this training.

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