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Introduction to Deep Learning with NVIDIA GPUs

Home / EVENTS

Introduction to Deep Learning with NVIDIA GPUs

Course Overview Artificial intelligence (AI) is growing exponentially.We are living in an era where self-driving cars are clocking up millions of miles, with great accuracy, and where Google Deepmind's AlphaGo beat the world champion at Go - a game where intuition plays a key role.But as AI advances, the problems become even more complex to solve. Only Deep Learning can solve such complex problems and that's why it's at the heart of AI today. While companies like Amazon, Google, and Facebook are pouring billions into Deep Learning projects, what about the rest of us - where do we start?This course aims to introduce students to Deep Learning as a subject within advanced AI and provides real-life problem sets that can be solved using Deep Learning neural networks.Learning ObjectivesLearn the fundamental concepts in Deep Learning and gain an understanding of the intuition and application of: Neural Networks Convolutional Neural Networks Recurrent Neural Networks Self-Organizing Maps Boltzmann Machines AutoEncodersWho Should Attend? Anyone interested in Deep Learning Students who have at least high school knowledge in math and who want to start learning Deep Learning Any intermediate-level enthusiasts who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets Any students in college who want to start a career in Data Science Any data analysts who want to level up in Deep Learning Any people who are not satisfied with their job and who want to become a Data Scientist Any people who want to create added value to their business by using powerful Deep Learning tools Any business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business Any Entrepreneur who want to create disruption in an industry using the most cutting edge Deep Learning algorithmsCourse OutlineDay 11. What is Deep Learning and what are Neural Networks? (30 min) Deep Learning as a branch of AI Neural networks and their history and relationship to neurons Creating a neural network in Python2. Artificial Neural Networks (ANN) Intuition (60 min) 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 BackpropagationBREAK (15 min)3. Building an ANN (60 min) Getting the python libraries Constructing ANN Using the bank customer churn dataset Predicting if customer will leave or not4. Evaluating Performance of an ANN (60 min) Evaluating the ANN Improving the ANN Tuning the ANNLUNCH (60 min)5. Hands-On Exercise (60 min) Participants will be asked to build the ANN from the previous exercise Participants will be asked to improve the accuracy of their ANN6. Convolutional Neural Networks (CNN) Intuition (60 min) What are CNN’s? Convolution operation ReLU Layer Pooling Flattening Full Connection Softmax and Cross-entropyBREAK (15 min)7. Building a CNN (60 min) Getting the python libraries Constructing a CNN Using the Image classification dataset Predicting the class of an imageDay 21. Evaluating Performance of a CNN (60 min) Evaluating the CNN Improving the CNN Tuning the CNN2. Hands-On Exercise (60 min) Participants will be asked to build the CNN from the previous exercise Participants will be asked to improve the accuracy of their CNNBREAK (15 min)3. Recurrent Neural Networks (RNN) Intuition (60 min) What are RNNs? Vanishing Gradient problem LSTMs Practical intuition LSTM variationsLUNCH (60 min)4. Building a RNN (60 min) Getting the python libraries Constructing RNN Using the stock prediction dataset Predicting stock price5. Evaluating Performance of a RNN (60 min) Evaluating the RNN Improving the RNN Tuning the RNN6. Hands-On Exercise (60 min) Participants will be asked to build the RNN from the previous exercise Participants will be asked to improve the accuracy of their RNNDay 31. Image Classification with DIGITS (120 min) How to leverage deep neural 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 application2. Object Detection with DIGITS (120 min) Train and evaluate an image segmentation networkLUNCH (60 min)3. Neutral Network Deployment with DIGITS and TensorRT (120 min) 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 process4. Closing Q & A SessionPrerequisite Basic high school mathematicsRequired Software Anaconda for Python (version 3.x) Optionally Sublime Text

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RM 3000 per pax (Early Bird Discount: RM 1500 per pax)

February 21, 2018
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Home / EVENTS

Introduction to Deep Learning with NVIDIA GPUs

Feb 21, 2018 12:00 AM

-

12:00 am

MaGIC (Malaysian Global Innovation & Creativity Centre)

RM 3000 per pax (Early Bird Discount: RM 1500 per pax)

Register Now

Course Overview Artificial intelligence (AI) is growing exponentially.We are living in an era where self-driving cars are clocking up millions of miles, with great accuracy, and where Google Deepmind's AlphaGo beat the world champion at Go - a game where intuition plays a key role.But as AI advances, the problems become even more complex to solve. Only Deep Learning can solve such complex problems and that's why it's at the heart of AI today. While companies like Amazon, Google, and Facebook are pouring billions into Deep Learning projects, what about the rest of us - where do we start?This course aims to introduce students to Deep Learning as a subject within advanced AI and provides real-life problem sets that can be solved using Deep Learning neural networks.Learning ObjectivesLearn the fundamental concepts in Deep Learning and gain an understanding of the intuition and application of: Neural Networks Convolutional Neural Networks Recurrent Neural Networks Self-Organizing Maps Boltzmann Machines AutoEncodersWho Should Attend? Anyone interested in Deep Learning Students who have at least high school knowledge in math and who want to start learning Deep Learning Any intermediate-level enthusiasts who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets Any students in college who want to start a career in Data Science Any data analysts who want to level up in Deep Learning Any people who are not satisfied with their job and who want to become a Data Scientist Any people who want to create added value to their business by using powerful Deep Learning tools Any business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business Any Entrepreneur who want to create disruption in an industry using the most cutting edge Deep Learning algorithmsCourse OutlineDay 11. What is Deep Learning and what are Neural Networks? (30 min) Deep Learning as a branch of AI Neural networks and their history and relationship to neurons Creating a neural network in Python2. Artificial Neural Networks (ANN) Intuition (60 min) 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 BackpropagationBREAK (15 min)3. Building an ANN (60 min) Getting the python libraries Constructing ANN Using the bank customer churn dataset Predicting if customer will leave or not4. Evaluating Performance of an ANN (60 min) Evaluating the ANN Improving the ANN Tuning the ANNLUNCH (60 min)5. Hands-On Exercise (60 min) Participants will be asked to build the ANN from the previous exercise Participants will be asked to improve the accuracy of their ANN6. Convolutional Neural Networks (CNN) Intuition (60 min) What are CNN’s? Convolution operation ReLU Layer Pooling Flattening Full Connection Softmax and Cross-entropyBREAK (15 min)7. Building a CNN (60 min) Getting the python libraries Constructing a CNN Using the Image classification dataset Predicting the class of an imageDay 21. Evaluating Performance of a CNN (60 min) Evaluating the CNN Improving the CNN Tuning the CNN2. Hands-On Exercise (60 min) Participants will be asked to build the CNN from the previous exercise Participants will be asked to improve the accuracy of their CNNBREAK (15 min)3. Recurrent Neural Networks (RNN) Intuition (60 min) What are RNNs? Vanishing Gradient problem LSTMs Practical intuition LSTM variationsLUNCH (60 min)4. Building a RNN (60 min) Getting the python libraries Constructing RNN Using the stock prediction dataset Predicting stock price5. Evaluating Performance of a RNN (60 min) Evaluating the RNN Improving the RNN Tuning the RNN6. Hands-On Exercise (60 min) Participants will be asked to build the RNN from the previous exercise Participants will be asked to improve the accuracy of their RNNDay 31. Image Classification with DIGITS (120 min) How to leverage deep neural 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 application2. Object Detection with DIGITS (120 min) Train and evaluate an image segmentation networkLUNCH (60 min)3. Neutral Network Deployment with DIGITS and TensorRT (120 min) 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 process4. Closing Q & A SessionPrerequisite Basic high school mathematicsRequired Software Anaconda for Python (version 3.x) Optionally Sublime Text

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