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Deep Learning with Keras

Keras is a popular high level programming framework for deep learning that simplifies the process of building deep learning applications. Instead of coding in low level TensorFlow and provide all the details, Keras provides a simplified programming interface wrapper over Tensorflow. In this course, you will learn how use Keras for deep learning and machine learning,  CNN, RNN, and explore many powerful pre-trained deep learning models included in Keras.

  • Deep Neural Network (DNN) with Keras
  • Convolutional Neural Network (CNN) for for Image Processing with Keras
  • Transfer Learning with Keras
  • Keras Tensorboard Callbacks
  • Recursive Neural Network (RNNs) for Text Processing with Keras
  • Deployment to Google Cloud Platform

HRDF SBL Claimable for Employers Registered with HRDF

HRDF claimable

Course Code: M550

Course Booking

MYR880.00

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

Module 1 Getting Started on Keras

  • What is Keras
  • Keras vs TensorFlow
  • Install and Run Keras

Module 2 Neural Networks

  • What is Neural Network ?
  • Basic Concepts of Machine Learning
  • Regression Application
  • Classification Application

Module 3 Visualisation with Tensorboard

  • What is Tensorboard
  • Keras Callbacks

Module 4 Convolutional Neural Network with Keras

  • Basics of Convolutional Neural Network (CNN)
  • Applications of CNN
  • Image Classification with CNN

Module 5 Transfer Learning with Kears

  • What is Transfer Learning
  • Pre-Trained Image Recognition Models
  • Fine Tuning Pre-Trained Models

Module 6 Recurrent Neural Network with Keras

  • What is Recurrent Neural Network (RNN)
  • Applications of RNN
  • LSTM and GRU
  • Image Classification with RNN

Module 7 Deployment

  • Deploy model at Google Cloud Platform
  • Google Cloud API Services

Who Should Attend

  • Data Scientists
  • Data Analysts
  • Engineers

Prerequisite

Basic Python knowledge is assumed

Trainers

Data Science TrainerDr. Aanand is a Full Stack Data Scientist who once had a torrid love affair with Physics. He has consulted and published in the area of Public Health, Electricity Markets, Telecom, BFSI, Advertising & Communication Strategies and Digital & Social Media Technologies. He has worked on assignments with international agencies such as International Monetary Fund, World Bank, Royal Netherland Embassy etc. besides MNCs like Tata Consultancy Services, Kie Square Consulting and several government organizations of national importance.

He regularly conducts general training programs in Python (Pandas, NumPy, SciPy, Matplotlib, Bokeh), R (dplyr, rstanarm, knitR, ggplot2), Data Visualization (Tableau, D3.js) and Machine Learnng (Reinforced Learning, Scikit Learn) and specialized training programs on Structural Equation Modeling and SAP Hana.

He holds a doctorate in Operations Research from Indian Institute of Management Ahmedabad and a post graduate in Physics from University of Mumbai. He has advanced training in mathematical programming including optimization, advanced multivariate data analysis, and simulation techniques. When he is not teaching or consulting he can be found meditating or heading for an adventurous trek.

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.

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