Prerequisites:

• Basic high school mathematics knowledge, no Prior Deep Learning knowledge. Basic Python understanding can be used for some exercise.

Who Should Attend:

• Anyone interested in to learn more about Deep Learning, or kickstart a career as a Data Scientist. This includes Students, Data Analysts, Business Owners, Entrepreneurs or any individual who wishes to leverage on powerful Deep Learning tools to add value wherever they are.

• Duration: 1 Hour
• Number of Questions: 30 Multiple Choice
• Passing Score: 70%

• Introduction to Deep Learning
• Getting Started with Deep Learning
• Approaches to Object Detection using DIGITS
• Deep Learning for Image Segmentation
• Deep Learning Network Deployment
• Medical Image Segmentation using DIGITS
• Introduction to Deep Learning with and MXNET
• Introduction to RNNs
• Signal Processing using DIGITS
• Deep Learning with Electronic Health Record

DAY 1:

• Introduction to Deep Learning:
  • This session will provide an overview of deep learning, its history, and its applications. It will also cover the basics of neural networks and the different types of deep learning algorithms.
• Fundamentals of Neural Networks:
  • This session will cover the fundamentals of neural networks, including the structure of a neural network, the different types of layers and the activation functions used in deep learning.
• Building a Neural Network:
  • This session will cover the process of building a neural network from scratch, including the steps of data preprocessing, model selection, and model training.

DAY 2:

• Convolutional Neural Networks:
  • This session will cover the fundamentals of convolutional neural networks, including the structure of a convolutional neural network, the different types of layers, and the activation functions used in deep learning.
• Recurrent Neural Networks:
  • This session will cover the fundamentals of recurrent neural networks, including the structure of a recurrent neural network, the different types of layers and the activation functions used in deep learning.
• Generative Adversarial Networks:
  • This session will cover the fundamentals of generative adversarial networks, including the structure of a generative adversarial network, the different types of layers and the activation functions used in deep learning.

DAY 3:

• Deep Learning Applications:
  • This session will cover the different applications of deep learning, including computer vision, natural language processing, and reinforcement learning.
• Deploying a Deep Learning Model:
  • This session will cover the process of deploying a deep learning model, including the steps of model selection, model training, and model deployment.
• Deep Learning Best Practices:
  • This session will cover the best practices for deep learning, including data preprocessing, model selection, and model training.

Payment can be made by:

1. Cash or Credit Card or Bank Cheque payable to "สำนักงานพัฒนาวิทยาศาสตร์และเทคโนโลยีแห่งชาติ" (a post-dated cheque is not accepted) on the first day of the service or within the last day of the service.

2. Account transfer and send the proof of the payment (the deposit slip) to email ttd@swpark.or.th

• Withholding tax (3%) is exempt.
• Should you need to withdraw, you must send the notice of the withdrawal in writing no later than 7 working days before the commencement date. The cancellation less than 7 days will be subject to a fine of 40% of the fee.
• Software Park Thailand reserves the rights to cancel courses due to unforeseen circumstances.