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HRDF Approved Training Provider in Malaysia - Modular Fast Track Skill-Based Trainings

Deep Learning and Machine Learning with TensorFlow

Tensorflow is the most popular and powerful open source machine learning/deep learning framework developed by Google for everyone. Tensorflow has many powerful Machine Learning API such as Neural Network, Convolutional Neural Network (CNN), Recurrent Neural Network (RNN),  Word Embedding, Seq2Seq, Generative Adversarial Networks (GAN), Reinforcement Learning, and Meta Learning. This course will show you how to build deep learning applications using Tensorflow.

Course Highlights

  • Installing TensorFlow
  • Math Operations with TensorFlow
  • Neural Networks with TensorFlow
  • Deep Learning with Tensorflow
  • Image Recognition with Convolutional Neural Network (CNN)
  • Text Analysis with Recurrent Neural Network (RNN)
  • Keras
  • Eager Mode

HRDF SBL Claimable for Employers Registered with HRDF

HRDF claimable

Course Code: M442

Course Booking

MYR1,600.00

Course Date

Course Time

* Required Fields

Course Cancellation/Reschedule Policy

We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% to participants.
Note the venue of the training is subject to changes due to class size and availability of the classroom.
Note the minimal class size to start a class is 3 Pax.


Course Details

Day 1

Module 1 Overview of Machine Learning and Tensorflow

  • Overview of AI and Machine Learning
  • What is TensorFlow?
  • Tensor and Data Types
  • Install and Run TensorFlow

Module 2 Basic Tensorflow Operations

  • Graph and Session
  • Math Operations
  • Matrix
  • Placeholder
  • Variable

Module 3 Datasets

  • MNIST Handwritten Digits Dataset
  • CIFAR Image Dataset
  • One Hot Encoding/Decoding

Module 4 Tensorflow for Regression

  • Loss Function and Optimizer
  • Machine Learning Steps
  • ML on Linear Regression

Module 5 Tensorflow for Neural Network

  • What is Neural Network (NN)
  • Activation Functions
  • Deep Learning vs Machine Learning
  • Tensorflow NN for Handwritten Digit Classification
  • Softmax and Cross Entropy

Module 6 Keras for Neural Network

  • What is Keras
  • Keras for Handwritten Digit Classification
  • Visualizing Loss and Accuracy with Keras
  • Save and Load Model
  • Visualizing with Keras Tensorboard Callbacks

Day 2

Module 7 Convolutional Neural Network (CNN)

  • What is CNN?
  • CNN Architecture
  • Convolution Layers
  • Pooling and Dropout Layers
  • Tensorflow CNN for Handwritten Digit Classification
  • Keras CNN for Handwritten Digit Classification

Module 8 Recurrent Neural Network (RNN)

  • Sequential Data
  • What is RNN?
  • Types of RNN
  • How to train a RNN
  • Long Term Dependencies
  • LSTM and GRU Cells
  • Tensorflow RNN on IMDB datasets
  • Keras RNN on IMDB datasets

Module 9 Transfer Learning

  • What is Transfer Learning
  • Using Pre-Trained Models for Image Classification
  • Fine Tuning Pre-Trained Models for Image Classification

Module 10 TF.Data and Estimators (Optional)

  • What is TF.Data?
  • ETL Pipeline
  • TFRecords
  • What is Estimator?
  • Feature Columns and Input Function
  • Regression Estimator
  • Classification Estimator

Course Admin

Prerequisite

This is a intermediate course. The following knowledge is asumed:

  • Basic Python

Software Requirement

This course will use Google Colab for training. Please ensure you have a Google account.

Who Should Attend

  • Data Scientists
  • Data Analysts
  • Engineers

Trainers

Machine Learning TrainerAmir Othman is a software engineer by profession. Being educated in Bauhaus Universität Weimar and Hochschule Ulm, he brings experiences from different facades of the world.With expertise in web technology, natural language processing and machine learning, he is a freelance data scientist. Some of his works include two international news aggregator : www.kronologimalaysia.com and www.diezeitachse.de

He also holds an impressive port folio for data visualizations, primarily focusing on web based techniques. After the realization set in that, he does not want to make it as an electronic engineer, he took the decision to go and try something new out of pure curiosity and thirst for new adventure. It didn't come quite handy but he has changed his major three times. It's not a surprise that he studied artificial intelligence and found himself all over again. He has a strong passion for machine learning and data science, fueled by the drive to learn and the endurance for growth beyond the ordinary.

Tensorflow TrainerMuhammad Samer Sallam is a software engineering and data Scientist with more than 3 years’ experience in the field of machine learning/ deep learning. He has a great passion for data science, intelligent-seeming algorithms and web technologies to develop smart web products improving human life. His interest led him to achieve comprehensive experiences in C#, Python, MATLAB, HTML & CSS, Javascript, and Mysql.His Work experiences are as below:


  • Control and Automation Engineer at Damascus International Airport,
  • Damascus (Syria) Research and Development Team Leader at Rachis Systems, Kuala Lumpur (Malaysia)
  • Former Machine Learning Specialist in Abundent, Kuala Lumpur (Malaysia)

Customer Reviews (11)

Will RecommendReview by mohd nazri
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 prepared content, since we have problem downloading and running the example on windows machine (Posted on 02/04/2019)
Will RecommendReview by wan suziana wan sulaiman
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
Good reference information (Posted on 02/04/2019)
Will RecommendReview by Shukeri
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
A small surau for participants (Posted on 16/01/2019)
Will RecommendReview by Irfan Noordin
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
A room with windows (Posted on 20/11/2018)
Might RecommendReview by Chew Yoong Loong
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 practical exercise (Posted on 18/10/2018)
Will RecommendReview by Megat Danial
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 practical exercises and increase the day of training (Posted on 18/10/2018)
Will RecommendReview by Tuan Muhammad Naeem bin Tuan Rashid
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
maybe a one day project in which students can try to create their own model (Posted on 16/08/2018)
Will RecommendReview by samsuri
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
For me, I like to understand from top to bottom. Due to intensive training, not enough time for detailed explanation (Posted on 15/08/2018)
Might RecommendReview by Iskandar
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
Helpful if trainer run through a case study from how to collect data, right to use the trained model in application

The office manager need to improve his skill in communicating with participants. In my opinion he was rude in answering my simple question about lunch option. Next time don't give out lunch for free. Give us option to pay and we can pay, no issue (Posted on 15/08/2018)
Will RecommendReview by Tham Mau Luen
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
Before this course, I had zero knowledge about deep learning. I still could catch up with this course since I hold a bachelor degree in Telecomunication Engineering and PhD in Signal Processing. But still sometimes I need to think deeply to understand to some content of this course. That being said, the instructor Dr. Bharat is very knowledgeable and he could answer any questions I threw at him. One improvement I suggest is that those python codes (for initiliazation purpose such as import os and debug etc) can be provided in the slide so that the participants can save the time for typing similar codes again and again.

My initial schedule for Deep Learning and Machine Learning with TensorFlow had been confirmed on 18-19 December 2017. I arrived at the training venue and waited from 10.15 am to 11am. Unfortunately, the organiser informed me that the instructor ( I don't know who but he/she is not Dr. Bharat) was unable to come. This is disappointing as I had already applied my training leave and budget approval from my institutions. Thankfully, my institution is "near" (well just 1 hour) from the training venue and I can resume my work. My suggestion is that organizer SHOULD double/triple/multiple confirm with the instructor one day /few hours before the training session. In case there is emergency, they can notify us immediately. (Posted on 26/12/2017)
Will RecommendReview by Wong
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
Would be nice to include some test codes for participants to test their installation of all tutorial-relevant software/packages in the installation guide prior to training commencement to help reduce occurrences of compatibility-related errors which could be time-consuming to troubleshoot during the training.

Interesting class. Trainer provided good information on the topic as well as recent developments in the field. (Posted on 28/08/2017)

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