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Deep Learning and Machine Learning with TensorFlow 2

Course Information

  • Sessions 2 days
  • Duration 15 hrs
  • Level Intermediate
  • Assessment NA

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

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Certification

  • Certificate of Completion from Tertiary Infotech - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Infotech.

Deep Learning and Machine Learning with TensorFlow

Course Code: C430

105 Review(s) | Add Your Review

What's This Course About

Elevate your Machine and Deep Learning knowledge with our meticulously crafted TensorFlow course at Tertiary Courses. Beginning with fundamental TensorFlow 2 operations, participants will be introduced to the expansive world of Neural Networks, with hands-on learning in both Regression and Classification domains. Delve deeper into specialized arenas, understanding the intricacies of Convolutional Neural Networks for Vision and exploring the vast applications of Recurrent Neural Networks for Sequential Data.

Our training goes beyond just the foundational elements. Experience the power of Transfer Learning and unlock new horizons with TensorFlow Hub, ensuring you're not just familiar, but proficient with the wide-ranging functionalities TensorFlow offers. Guided by seasoned experts and enriched with practical sessions, this course is your definitive step towards mastering TensorFlow-driven Deep and Machine Learning.

Funding Options

No funding is available for this course.

Course Fee

$600.00 (GST-exclusive)
$654.00 (GST-inclusive)

Course Date

Course Time

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Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Post-Course Support

  • We provide free consultation related to the subject matter after the course.
  • Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

Cancellation & Reschedule Policy

  • You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
  • Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

Course Details

What You'll Learn

Topic 1 Introduction to Deep Learning

Machine Learning vs Deep Learning

Deep Learning Methodology

Overview of Tensorflow Keras

Install and Run Tensorflow Keras

Basic Tensorflow Keras Operations

Topic 2 Neural Network for Regression

What is Neural Network (NN)?

Loss Function and Optimizer

Build a Neural Network Model for Regression

Topic 3 Neural Network for Classification

One Hot Encoding and SoftMax

Cross Entropy Loss Function

Build a Neural Network Model for Classification

Topic 4 Convolutional Neural Network (CNN)

Introduction to Convolutional Neural Network?

ImageDataGenerator

Image Classification Model with CNN

Data Augmentation and Dropout

Topic 5 Transfer Learning

Introduction to Transfer Learning

Applications of Pre-Trained Models

Fine Tuning Pre-Trained Models

Topic 6 Recurrent Neural Network (RNN)

Introduction to Recurrent Neural Network (RNN)

LSTM and GRU

Build a RNN Model for Time Series Forecasting

Build a RNN Model for Sentiment Analysis

Course Info

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

Minimum Entry Requirement

Knowledge and Skills

  • Able to operate using computer functions
  • Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

  • Positive Learning Attitude
  • Enthusiastic Learner

Experience

  • Minimum of 1 year of working experience.

Target Age Group: 21-65 years old

Minimum Software/Hardware Requirement

Software:

You can download and install the following software:

Hardware: Windows and Mac Laptops

Job Roles

Job Roles

  • Machine Learning Engineer
  • Data Scientist
  • Deep Learning Researcher
  • AI Developer
  • Neural Network Designer
  • Computer Vision Engineer
  • NLP Engineer (branching into deep learning)
  • AI Product Manager (technical understanding)
  • Robotics Engineer (with AI components)
  • Bioinformatics Scientist (deep learning applications)
  • Medical Imaging Specialist (AI-focused)
  • Game Developer (AI-driven features)
  • Predictive Analytics Specialist
  • AI/ML Educator or Trainer
  • Autonomous Systems Developer.

Trainers

Trainers

Richard Wan

Richard Wan is an ACLP-certified lecturer and software consultant with over 40 years of experience in software and hardware development, spanning AI, computer vision, and machine learning. He began his programming career with 8-bit computing in the late 1970s and went on to earn his M.Sc. in Electrical Engineering (Computer Vision) from the University of Wisconsin–Madison. His professional contributions include co-founding multiple high-tech companies, pioneering digital publishing technologies, and leading AI-driven software development in healthcare, defense, and manufacturing.

Richard has taught a wide range of technical courses, including machine learning with Scikit-Learn, deep learning with TensorFlow and PyTorch, and computer vision with OpenCV. In predictive analytics, he emphasizes the use of PyTorch for building deep learning models that can forecast trends, detect anomalies, and classify outcomes. His teaching approach blends decades of hands-on development with structured, beginner-friendly instruction, equipping learners with practical skills to transform data into prediction.

Review

Customer Reviews (105)

Might Recommend Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Nil (Posted on 4/29/2018)
Might Recommend Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Nil (Posted on 4/19/2018)
Might Recommend Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Get a larger training venue or limit the number of people in the course! (Posted on 3/29/2018)
Might Recommend Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
More emphasis of why it works than how it works (Posted on 3/21/2018)
Will Recommend Review by Course Participant/Trainee
1. Do you find the course meet your expectation?
2. Do you find the trainer knowledgeable in this subject?
3. How do you find the training environment
Probably to include/cover more on Keras examples and adding exercises on transfer learning (e.g. retrain the top few layers and freeze the rest) just as using image/speech data. Would be good to cover a bit on using the TensorFlow GPU version to run real application problems, giving the students a glimpse of how to start if using the GPU to run. (Posted on 3/21/2018)

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