Deep Learning Training:
What is Deep Learning:
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
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.
3.Course In Multilayer Perceptrons
3.1 Crash Course Overview.
3.2 Multilayer Perceptrons.
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:
- 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.