- Understand the intuition behind Artificial Neural Networks
- Apply Artificial Neural Networks in practice
- Understand the intuition behind Convolutional Neural Networks
- Apply Convolutional Neural Networks in practice
- Understand the intuition behind Recurrent Neural Networks
- Apply Recurrent Neural Networks in practice
- Understand the intuition behind Self-Organizing Maps
- Apply Self-Organizing Maps in practice
- Understand the intuition behind Boltzmann Machines
- Apply Boltzmann Machines in practice
- Understand the intuition behind AutoEncoders
- Apply AutoEncoders in practice
- Deep Learning as a branch of AI
- Neural networks and their history and relationship to neurons
- Creating a neural network in Python
- 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
- Getting the python libraries
- Constructing ANN
- Using the bank customer churn dataset
- Predicting if customer will leave or not
- Evaluating the ANN
- Improving the ANN
- Tuning the ANN
- Participants will be asked to build the ANN from the previous exercise
- Participants will be asked to improve the accuracy of their ANN
- What are CNN’s?
- Convolution operation
- ReLU Layer
- Pooling
- Flattening
- Full Connection
- Softmax and Cross-entropy
- Getting the python libraries
- Constructing a CNN
- Using the Image classification dataset
- Predicting the class of an image
- Evaluating the CNN
- Improving the CNN
- Tuning the CNN
- Participants will be asked to build the CNN from the previous exercise
- Participants will be asked to improve the accuracy of their CNN
- What are RNN’s?
- Vanishing Gradient problem
- LSTMs
- Practical intuition
- LSTM variations
- Getting the python libraries
- Constructing RNN
- Using the stock prediction dataset
- Predicting stock price
- Evaluating the RNN
- Improving the RNN
- Tuning the RNN
- Participants will be asked to build the RNN from the previous exercise
- Participants will be asked to improve the accuracy of their RNN
- 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
- Train and evaluate an image segmentation network
- 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
- 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 people 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 wants to create disruption in an industry using the most cutting edge Deep Learning algorithms
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Introduction to Deep Learning with NVIDIA GPUs
Session Summary
Artificial intelligence is growing exponentially. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence today. While companies like Amazon, Google, and Facebook are pouring billions into Deep Learning projects, what about the rest of us? This course introduces students to Deep Learning as a subject within advanced Artificial Intelligence and provides several real-life problem sets that can be solved using Deep Learning neural networks.
What will you learn?
Course Outline
Day 1
A. What is Deep Learning and what are Neural Networks? (30 min)
B. Artificial Neural Networks (ANN) Intuition (60 min)
C. BREAK (15 min)
D. Building an ANN (60 min)
E. Evaluating Performance of an ANN (60 min)
F. LUNCH (60 min)
G. Hands-On Exercise (60 min)
H. Convolutional Neural Networks (CNN) Intuition (60 min)
I. BREAK (15 min)
J. Building a CNN (60 min)
Day 2
A. Evaluating Performance of a CNN (60 min)
B. Hands-On Exercise (60 min)
C. BREAK (15 min)
D. Recurrent Neural Networks (RNN) Intuition (60 min)
E. LUNCH (60 min)
F. Building a RNN (60 min)
G. Evaluating Performance of a RNN (60 min)
H. Hands-On Exercise (60 min)
Day 3
A. Image Classification with DIGITS (120 min)
B. Object Detection with DIGITS (120 min)
C. LUNCH (60 min)
D. Neutral Network Deployment with DIGITS and TensorRT (120 min)
Who Should Attend?
(Day 1 and 2)
Tarun Sukhani
Tarun has 16 years of both academic and industry experience as a data scientist over the course of his career. Starting off as an EAI consultant in the USA, Tarun was involved in a number of integration/ETL projects for a variety of Fortune 500 and Global 1000 clients, such as BP Amoco, Praxair, and GE Medical Systems.
While completing his Master's degree in Data Warehousing, Data Mining, and Business Intelligence at Loyola University Chicago GSB in 2005, Tarun also worked as a BI consultant for a number of Fortune 500 clients at Revere Consulting, a Chicago-based boutique IT firm focusing on Data Warehousing/Mining projects. Tarun continues to work within the BI space, most recently focusing his time on Deep/Reinforcement Learning projects within the Fintech sector.
Tarun Sukhani has worked on parametric statistical modeling as well within the Data Science and Big Data Science space, using tools such as SciPy in Python and R and R/Hadoop for Big Data projects.
(Day 3)
Jia Qing Yap
Jia Qing is the Executive Lead of OpenSourceSDC where they are democratising access to contributing and testing autonomous driving code, through opening up testing vehicles for anyone to test their code on.
He looks at novel ways of using deep learning for the autonomous driving problem, be it Generative Adversarial Networks to generate life-like synthetic data for training models, or using Long-short Term Memory Neural Networks to capture the temporal nature of steering data.
He believes in going deep into the mathematics of machine learning, and is a teaching assistant for the General Assembly data science course.
Pre-requisites
DLI Workshop Attendee Instructions: You must bring your own laptop to this workshop.
Pre-requisites to join:
3-Day Introduction to Deep Learning Workshop (21-23 Feb)
Required: Basic high school mathematics knowledge, no prior Deep Learning knowledge.
[You will receive a Beginner Level certificate from NVIDIA Deep Learning Institute once you have completed the 3-day programme with inclusive participation of the 1-day NVIDIA Deep Learning Lab]
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1-Day NVIDIA Deep Learning Fundamentals Workshop (23 Feb)
Required: MUST have technical background and basic understanding of Deep Learning concepts
[You will receive a Certificate in Deep Learning Fundamentals by NVIDIA Deep Learning Institute upon completion of this 1-day workshop]
Limited to 20 seats only.
Program Fee
RM 3000 per person.
Enjoy our early bird discounts of 50%, using the following promo code!
(Promo Code: cnimagic)
Available on a first-come first-serve basis only.
This course is HRDF-claimable.
Note :
Seats are limited to 20 participants only
There will be NO Breakfast and Lunch F&B provided
You can buy your breakfast, lunch and coffee at MaGIC Chillax Cafe and Coffee machine at Level 1
Payment, Cancellations and Refunds
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