Course Overview:
     Deep Learning is the fastest-growing field in Machine Learning and highly crucial for Artificial Intelligence, using many-layered Deep Neural Networks (DNNs) to make sense of data and enable many practical machine assists. This course introduces students to DeepLearning as a subject within advanced Artificial Intelligence and provides several real-life problem sets that can be solved using Deep Learning neural networks.

Learning Objective:

• Understand the intuition behind Artificial Neural Networks
• Understand the intuition behind Convolutional Neural Networks
• Apply Artificial Neural Networks in practice
• Apply Convolutional Neural Networks in practice
• Understand the intuition behind Recurrent Neural Networks
• Apply Recurrent Neural Networks in practice

Prerequisites:
Basic high school mathematics.

Who Should Attend:
Students, Data Analysts, Developers, Business Owners, Engineers, Product Architects, Enterpreneurs or any individual who wishes to leverage on powerful Deep Learning tools to add value, wherever they are.

Exam Format:

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

Deep Learning is the fastest-growing field in Machine Learning. it uses many-layered Deep Neural Networks (DNNs) to make sense of data such as images, sound and text, and enable many practical machine assists.

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

Day 2:
Building a CNN
• Getting the Python liabraries
• Constructing a CNN
• Using the Image classification dataset
• Predicting the class of an image

Recurrent Neural Networks (RNN) Intuition
• What are RNN's?
• Vanishing Gradient problem
• LSTMs
• Practical intuition
• LSTM variations

Evaluating Performance of a CNN
• Evaluating the CNN
• Improving the CNN
• Tuning the CNN

Building a RNN
• Getting the python libraries
• Constructing RNN
• Using the stock prediction dataset
• Predicting stock price

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

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

Evaluating Performance of a RNN
• Evaluating the RNN
• Improving the RNN
• Tuning the RNN

Day 3:
Natural Language Processsing and Word Embeddings
• Word representation
• Word embeddings
• Word2Vec
• Sentiment Classification

Hands-On Exercise
•Participants will be asked to used attention-based sequence models and evaluate their effectiveness
• Participants will be asked to improve the accuracy of their attention-based models

Swquence models and attention mechanism
• Picking the next word or sentence
• Beam search
• What is an attention model
• Speech recognition
• Trigger word detection
• Working with advanced NLP models - GPT - 3

Building a Deep Q Learning Neural Network (DQN)
• Getting the python libraries
• Constructing the DQN
• Optimizing a DQN
• Working with OpenAI Gy

Reinforcement Learning
• What is reinforcement learning?
• K-Armed Bandit Problem - exploration / exploitation trade-off
• Markov Processes
• Policies and value functions
• Dynamic programming
• Q learning and Deep Q learning

Hands-On Exercise
• Participants will be asked to build the DQN from the previous exercis
• Participants will be asked to improve the accuracy of their DQN

Evaluating Performance of a DQN
• Evaluating the DQN
• Improving the DQN
• Tuning the DQN

Payment can be made by:

    1. Cash or Credit Card or Bank Cheque payable to “Software Park Thailand #2” (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) via fax or email to fax no. 02-583-2884 or email ttd@swpark.or.th

        2.1 Siam Commercial Bank, Chaengwattana Branch
             Saving Account Number: 324-2-56262-0
             Account Name: Software Park Thailand#2

        2.2 Krungsri Bank, Chaengwattana (Software Park) Branch
             Saving Account Number: 329-1-34850-3
             Account Name: Software Park Thailand#2

Notes:
- 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.

For more information, contact our course coordinator on:

Name: Kotchaphan Aokdeelert

Tel: +66-2583-9992 Ext.1425

Fax: +66-2583-2884

Email: kotchaphan.aokdeelert or ttd@swpark.or.th

You are encouraged to use the course schedule as a guide to plan your training. The schedule is accessible at www.swpark.or.th for more information.