Deep Learning Foundation Certificate: Certification by iTrain Malaysia
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DEEP LEARNING FOUNDATION CERTIFICATE

3-Day Instructor-Led Course | HRDF Claimable!

In Partnership With

Deep Learning 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 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.

Learning Objectives:

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

Who Should Attend & Prerequisites:

Who Should Attend: Anyone interested to learn more about Deep Learning, or kickstart a career as a Data Scientist. This includes Students, Data Analysts, Developers, Business Owners, Engineers, Product Architects, Entrepreneurs or any individual who wishes to leverage on powerful Deep Learning tools to add value, wherever they are.

Prerequisites: Students must have knowledge in basic high-school mathematics

Required Software: Anaconda for Python (version 3.x) Optionally Sublime Text

 

Course Outline

WHAT YOU’LL LEARN: DAILY SCHEDULE

Day 1

  • 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

Day 2

  • 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 RNNfrom the previous exercise
  • Participants will be asked to improve the accuracy of their RNN

Day 3

  • Word representation
  • Word embeddings
  • Word2Vec
  • Sentiment Classification

  • 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

  • Participants will be asked to use attention-based sequence models and evaluate their effectiveness
  • Participants will be asked to improve the accuracy of their attention-based models

  • Getting the Python libraries
  • Constructing the DQN
  • Working with OpenAI Gym
  • Optimising a DQN

  • 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

  • Participants will be asked to build the DQN from the previous exercise
  • Participants will be asked to improve the accuracy of their DQN

  • Evaluating the DQN
  • Improving the DQN
  • Tuning the DQN

“Everything about the course was great. Thanks!”

Mohd Izzudin bin Razali, Universiti Teknologi Malaysia

“Good and clear introduction to the basic operation of neural networks.”

Hoo Wan Hong, TMAS

“The trainer is very good, he knows deeply about the subject and topics and answered our questions well.”

Nur Shafiranisa binti Shaharum, Universiti Putra Malaysia

FAQ

YOUR QUESTIONS, ANSWERED.

You bet it is! Our Certification Body for this course is iTrain Asia Pte Ltd, the region’s top Certifications Tech Provider headquartered in Singapore, with branch offices in Malaysia and Indonesia.

This is a 3-day course at an instructor-led training centre.

Yes, participants should bring their own laptops to class as they will not be provided with one.

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