Keras Training

BY
Mindmajix Technologies

Understand the whole range of concepts related to Keras in detail with the Keras Training course.

Mode

Online

Quick Facts

particular details
Medium of instructions English
Mode of learning Self study, Virtual Classroom
Mode of Delivery Video and Text Based
Frequency of Classes Weekdays, Weekends

Course overview

Keras Training Course will help the students to make an overview of Keras which is an open-source software library that creates and gives a Python interface for artificial neural networks. In this course, the learners will dive deep into the numerous aspects of Keras such as predefined neural network layers, Installation of Keras on Docker, the usage of Keras, Multilayer Perceptron in Keras,  Neural Networks, and many more.

Administered by Mindmajix Technologies, Keras Training online course will expose the learners to the  CIFAR-10 images with DL, predefined activation functions, parameters in Neural Networks using GridSearchCV, implementing the Keras for secure application and many along with the practical training through many hands-on practical exercises and use cases. Keras Training certification can be taken by the students through different options of enrolment, namely, the corporate training, the self-study through pre-recorded videos, and online live instructor-led mode.

The highlights

  • 100%  online course
  • Offered by Mindmajix Technologies
  • FREE Demo on Request
  •   Flexible Schedule
  • Online Live and Self-paced Training Options
  • 24/7 Lifetime Support
  • Life-Time Self-Paced Videos Access
  • One-on-One Doubt Clearing
  • Certification Oriented Curriculum 

Program offerings

  • One-on-one doubt clearing sessions
  • Certification oriented curriculum
  • Real-time project use cases
  • 20 hours of labs
  • Free demo on request
  • 24/7 lifetime support
  • 30 hours of sessions
  • Online live and self-paced training options

Course and certificate fees

certificate availability

Yes

certificate providing authority

Mindmajix Technologies

What you will learn

Knowledge of deep learning

Through the Keras Training online certification, the learners can understand Keras Limitations, Keras API,  Deep Convolutional Neural Network (DCNN), deep learning with Keras, Generative Adversarial Networks, and Wavenet, Word Embeddings, Recurrent Neural Networks RNN, and many more.

The syllabus

Basic Overview of Keras

  • Understanding the features of Keras
  • Advantages of Keras
  • Keras Limitations

Keras Installation and API

Installig Keras
  • Installation of dependencies
  • Installation of Theano
  • Installation of TensorFlow
  • Installation of Keras
  • Testing each Installation
Configuring Keras
  • Installation of Keras on Docker
  • Installation of Keras on Google Cloud ML
  • Installation of Keras on Amazon AWS
  • Installation of Keras on Microsoft Azure
Keras API
  • Basic Architecture of Keras
  • Overview of predefined neural network layers
  • Overview of predefined activation functions
  • Understanding Losses Functions
  • Understanding Metrics
  • Some Useful Operations

Overview of Deep Learning with ConvNets

  • Deep Convolutional Neural Network
  • Understanding Deep Convolutional Neural Network (DCNN)
  • Simple Example of DCNN
  • Recognizing CIFAR-10 images with DL

Concept of Generative Adversarial Networks and Wavenet

  • Overview of GAN
  • Keras adversarial GANs for forging MNIST
  • Keras adversarial GANs for forging CIFAR
  • Understanding WaveNet

Word Embeddings

  • Distributed Representations
  • Understanding word2vec
  • GloVe Exploring functionalities
  • Using pre-trained embeddings

Overview of Recurrent Neural Networks RNN

  • Basics of SimpleRNN cells
  • Understanding RNN Topologies
  • Using Gated Recurrent Unit -GRU
  • Concept of Bidirectional RNNs
  • Vanishing and exploding gradients
  • Using Long Short Term Memory -LSTM
  • Understanding Stateful RNNs

Additional Deep Learning Models

  • Dealing with Keras Functional API
  • Understanding Regression Networks
  • Concept of Unsupervised Learning
  • Keras Customization scenario
  • Using Generative Models

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