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Course Skill Level:


Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Developers with basic Python skills

Who should attend & recommended skills

  • This course is designed for developers wanting to demonstrate fundamentals of Deep Learning and neural network methodologies using Keras 2.x.
  • Skill-level: Foundation-level Keras 2.x Projects skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)

About this course

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.

Skills acquired & topics covered

  • 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.
  • Building strong fundamentals of Keras in the area of deep learning and artificial intelligence
  • Applying regression methods to your data and understand how the regression algorithm works
  • Understanding the basic concepts of classification methods and how to implement them in the Keras environment
  • Importing and organizing data for neural network classification analysis
  • The role of rectified linear units in the Keras network architecture
  • Implementing a recurrent neural network to classify the sentiment of sentences from movie reviews
  • Setting the embedding layer and the tensor sizes of a network

Course breakdown / modules

  • Introduction to Keras
  • Keras backend options
  • Installation
  • Model fitting in Keras

  • Defining a regression problem
  • Creating a linear regression model
  • Multiple linear regression concepts
  • Neural networks for regression using Keras

  • Basics of classification problems
  • Different types of classification
  • Pattern recognition using a Keras neural network

  • 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

  • Understanding computer vision concepts
  • Convolutional neural networks
  • Common CNN architecture
  • Implementing a CNN for object recognition

  • Sentiment analysis basic concepts
  • Recurrent neural networks
  • Classifying sentiment in movie reviews using an RNN

  • The basics of forecasting
  • Time series analysis
  • Time series models
  • Long short-term memory in Keras
  • Implementing an LSTM to forecast stock volatility

  • Basic concepts of image recognition
  • Optical character recognition
  • Generative neural networks
  • The Keras autoencoders model
  • Implementing autoencoder Keras layers to reconstruct handwritten digit images

  • Robot control overview
  • The environment for controlling robot mobility
  • Reinforcement learning basics
  • Keras DQNs
  • DQN to control a robot's mobility

  • Natural language processing
  • The Natural Language Toolkit
  • Implementing a DNN to label sentences

  • Deep learning methods
  • Automated machine learning
  • Differentiable neural computer
  • Genetic programming and evolutionary strategies
  • Inverse reinforcement learning