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Keras 2.x Projects

  • Course Code: Data Science - Keras 2.x Projects
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: This course is geared for those who wants to Demonstrate fundamentals of Deep Learning and neural network methodologies using Keras 2.x.

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level Keras 2.x Projects skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Demonstrate fundamentals of Deep Learning and neural network methodologies using Keras 2.x. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

Keras 2.x Projects explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. To begin with, you will quickly set up a deep learning environment by installing the Keras library. Through each of the projects, you will explore and learn the advanced concepts of deep learning and will learn how to compute and run your deep learning models using the advanced offerings of Keras. You will train fully-connected multilayer networks, convolutional neural networks, recurrent neural networks, autoencoders and generative adversarial networks using real-world training datasets. The projects you will undertake are all based on real-world scenarios of all complexity levels, covering topics such as language recognition, stock volatility, energy consumption prediction, faster object classification for self-driving vehicles, and more. By the end of this course, you will be well versed with deep learning and its implementation with Keras. You will have all the knowledge you need to train your own deep learning models to solve different kinds of problems. 

Working in a hands-on learning environment, led by our Keras 2.x Projects expert instructor, students will learn about and explore: 

  • Experimental projects showcasing the implementation of high-performance deep learning models with Keras. 
  • Use-cases across reinforcement learning, natural language processing, GANs and computer vision. 
  • Build strong fundamentals of Keras in the area of deep learning and artificial intelligence 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Apply regression methods to your data and understand how the regression algorithm works 
  • Understand the basic concepts of classification methods and how to implement them in the Keras environment 
  • Import and organize data for neural network classification analysis 
  • Learn about the role of rectified linear units in the Keras network architecture 
  • Implement a recurrent neural network to classify the sentiment of sentences from movie reviews 
  • Set the embedding layer and the tensor sizes of a network 

Audience & Pre-Requisites 

This course is designed for developers wants to Demonstrate fundamentals of Deep Learning and neural network methodologies using Keras 2.x 

Pre-Requisites:  Students should have familiar with  

  • Basics of Keras  
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

  1. Getting Started with Keras 
  • Getting Started with Keras 
  • Introduction to Keras 
  • Keras backend options 
  • Installation 
  • Model fitting in Keras 
  1. Modeling Real Estate Using Regression Analysis 
  • Modeling Real Estate Using Regression Analysis 
  • Defining a regression problem 
  • Creating a linear regression model 
  • Multiple linear regression concepts 
  • Neural networks for regression using Keras 
  1. Heart Disease Classification with Neural Networks 
  • Heart Disease Classification with Neural Networks 
  • Basics of classification problems 
  • Different types of classification 
  • Pattern recognition using a Keras neural network 
  1. Concrete Quality Prediction Using Deep Neural Networks 
  • Concrete Quality Prediction Using Deep Neural Networks 
  • Basic concepts of ANNs 
  • Multilayer neural networks 
  • Implementing multilayer neural networks in Keras 
  • Building a Keras deep neural network model 
  • Improving the model performance by removing outliers 
  1. Fashion Article Recognition Using Convolutional Neural Networks 
  • Fashion Article Recognition Using Convolutional Neural Networks 
  • Understanding computer vision concepts 
  • Convolutional neural networks 
  • Common CNN architecture 
  • Implementing a CNN for object recognition 
  1. Movie Reviews Sentiment Analysis Using Recurrent Neural Networks 
  • Movie Reviews Sentiment Analysis Using Recurrent Neural Networks 
  • Sentiment analysis basic concepts 
  • Recurrent neural networks 
  • Classifying sentiment in movie reviews using an RNN 
  1. Stock Volatility Forecasting Using Long Short-Term Memory 
  • Stock Volatility Forecasting Using Long Short-Term Memory 
  • The basics of forecasting 
  • Time series analysis 
  • Time series models 
  • Long short-term memory in Keras 
  • Implementing an LSTM to forecast stock volatility 
  1. Reconstruction of Handwritten Digit Images Using Autoencoders 
  • Reconstruction of Handwritten Digit Images Using Autoencoders 
  • Basic concepts of image recognition 
  • Optical character recognition 
  • Generative neural networks 
  • The Keras autoencoders model 
  • Implementing autoencoder Keras layers to reconstruct handwritten digit images 
  1. Robot Control System Using Deep Reinforcement Learning 
  • Robot Control System Using Deep Reinforcement Learning 
  • Robot control overview 
  • The environment for controlling robot mobility 
  • Reinforcement learning basics 
  • Keras DQNs 
  • DQN to control a robot’s mobility 
  1. Reuters Newswire Topics Classifier in Keras 
  • Reuters Newswire Topics Classifier in Keras 
  • Natural language processing 
  • The Natural Language Toolkit 
  • Implementing a DNN to label sentences 
  1. What is Next? 
  • What is Next? 
  • Deep learning methods 
  • Automated machine learning 
  • Differentiable neural computer 
  • Genetic programming and evolutionary strategies 
  • Inverse reinforcement learning 

Student Materials: Each student will receive a Student Guide with course notes, code samples, software tutorials, diagrams and related reference materials and links (as applicable). Our courses also include step by step hands-on lab instructions and and solutions, clearly illustrated for users to complete hands-on work in class, and to revisit to review or refresh skills at any time. Students will also receive the project files (or code, if applicable) and solutions required for the hands-on work. 

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