Deep Learning Online Training hyderabad Kukatpally

we provide Deep Learning Training by industry real time experts, Deep Learning is an artificial intelligence function that copycat the workings of the human brain in the processing data and creating a motif for use in decision making. Deep learning is a subspace of machine learning in Artificial Intelligence that has networks capable of learning unverified from data that is unstructured or untagged, Deep Learning Training by VLR Training for more Details contact us 9059868766.

http://www.vlrtraining.in/wp-content/uploads/2018/04/Deep-learning-online-training.jpg

 

 

 

 

 

 

 Deep learning Training Course Details

  • Course Duration:90 Days
  • Mon-Fri 7:30 am (IST)
  • Mode of Training: Classes Room and Online
  • Real-time Trainer
  • After class, we will provide class recording for reference purpose

 

 

Deep Learning  Training Course Content:

Artificial  Neural Networks

1.Introduction to Keras

  • 1.1 What is Keras?
  • 1.2 How to Install Keras.
  • 1.3 Theano and TensorFlow Backends for Keras.
  • 1.4 Build Deep Learning Models with Keras.

2. Multilayer Perceptions.

3.Course In Multilayer Perceptrons

  • 3.1 Crash Course Overview.
  • 3.2 Multilayer Perceptrons.
  • 3.3 Neurons
  • 3.4 Networks of Neurons.
  • 3.5 Training Networks.

4. Develop Your First Neural Network With Keras.

  • 4.1 Tutorial Overview.
  • 4.2 Pima Indians Onset of Diabetes Dataset.
  • 4.3 Load Data.
  • 4.4 Define Model
  • 4.5 Compile Model
  • 4.6 Fit Model
  • 4.7 Evaluate Model

4.8  Tie It All Together.

5. Use Keras Models With Scikit-Learn For General Machine Learning

  • 5.1. Evaluate Models with Cross-Validation.
  • 5.2 Grid Search Deep Learning Model Parameters

6.Project: Multiclass Classification Of Flower Species

  • 6 .1 Iris Flowers Classification Dataset
  • 6.2 Import Classes and Functions.
  • 6.3 Initialize Random Number Generator
  • 6.4 Load The Dataset
  • 6.5 Encode The Output Variable
  • 6.6 Define The Neural Network Model

6.7 Evaluate The Model with k-Fold Cross-Validation.

7.Project: Regression Of Boston House Prices

  • 7.1 Boston House Price Dataset.
  • 7.2 Develop a Baseline Neural Network Model
  • 7.3 Lift Performance By Standardizing The Dataset
  • 7.4 Tune The Neural Network Topology

Convolutional Neural Networks.

  • 8.Crash Course In Convolutional Neural Networks
  • 8.1 The Case for Convolutional Neural Networks
  • 8.2 Building Blocks of Convolutional Neural Networks
  • 8.3 Convolutional Layers
  • 8.4 Pooling Layers
  • 8.5 Fully Connected Layers
  • 8.6 Worked Example.
    8.7 Convolutional Neural Networks Best Practices

9 Project: Handwritten Digit Recognition

  • 9.1 Handwritten Digit Recognition Dataset.
  • 9.2 Loading the MNIST dataset in Keras
  • 9.3 Baseline Model with Multilayer Perceptrons
  • 9.4 Simple Convolutional Neural Network for MNIST.
  • 9.5 Larger Convolutional Neural Network for MNIST

 
10. Project Object Recognition in Photographs

  • 10.1 Photograph Object Recognition Dataset 1
    10.2 Loading The CIFAR-10 Dataset in Keras
  • 10.3 Simple CNN for CIFAR-10
  • 10.4 Larger CNN for CIFAR-10
  • 10.5 Extensions To Improve Model Performance

11.Project: Predict Sentiment From Movie Reviews

  • 11.1 Movie Review Sentiment Classification Dataset
  • 11.2 Load the IMDB Dataset With Keras
  • 11.3 Word Embeddings
  • 11.4 Simple Multilayer Perceptron Model
  • 11.5 One-Dimensional Convolutional Neural Network

 

Recurrent Neural Networks:

  • 12.1 Crash Course In Recurrent Neural Networks
  • 12.1 Support For Sequences in Neural Networks
  • 12.2 Recurrent Neural Networks
  • 12.3 Long Short-Term Memory Networks

13.Time Series Prediction with Multilayer Perceptrons

  •  13.1 Problem Description: Time Series Prediction
  • 13.2 Multilayer Perceptron Regression
  • 13.3 Multilayer Perceptron Using the Window Method.

14.Time Series Prediction with LSTM Recurrent Neural Networks

  • 14.1 LSTM Network For Regression
  • 14.2 LSTM For Regression Using the Window Method.
  • 14.3 LSTM For Regression with Time Steps.
  • 14.4 LSTM With Memory Between Batches
  • 14.5 Stacked LSTMs With Memory Between Batches

14.Project: Sequence Classification of Movie Reviews

  • 15.1 Simple LSTM for Sequence Classification
  • 15.2 LSTM For Sequence Classification With Dropout
  • 15.3 LSTM and CNN For Sequence Classification.

 

VlrTraining Address:
PlotNo 126/b,2nd floor, Street Number 4,
Addagutta Society, Near Jntuh,Pragarthi Nagar Road,
Kukatpally, Hyderabad, Telangana 500072
Name: DeepLearning Online Training
Telephone:9059868766
Opening hours:7 Am to 9 Pm (IST)

 

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.