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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 Course Details
Course Duration
90 Days
Mon-Fri
7.30 am (IST)
Mode of Training
Online
Deep learning Training Course Content
- What is Keras?
- How to Install Keras.
- Theano and TensorFlow Backends for Keras.
- Build Deep Learning Models with Keras.
- Crash Course Overview.
- Multilayer Perceptrons.
- Neurons
- Networks of Neurons.
- Training Networks
- Tutorial Overview.
- Pima Indians Onset of Diabetes Dataset.
- Load Data.
- Define Model
- Compile Model
- Fit Model
- Evaluate Model
- Tie It All Together
- Evaluate Models with Cross-Validation.
- Grid Search Deep Learning Model Parameters
- Iris Flowers Classification Dataset
- Import Classes and Functions.
- Initialize Random Number Generator
- Load The Dataset
- Encode The Output Variable
- Define The Neural Network Model
- Evaluate The Model with k-Fold Cross-Validation
- Boston House Price Dataset.
- Develop a Baseline Neural Network Model
- Lift Performance By Standardizing The Dataset
- Tune The Neural Network Topology
- Crash Course In Convolutional Neural Networks
- The Case for Convolutional Neural Networks
- Building Blocks of Convolutional Neural Networks
- Convolutional Layers
- Pooling Layers
- Fully Connected Layers
- Worked Example
- Convolutional Neural Networks Best Practices
- Handwritten Digit Recognition Dataset.
- Loading the MNIST dataset in Keras
- Baseline Model with Multilayer Perceptrons
- Simple Convolutional Neural Network for MNIST.
- Larger Convolutional Neural Network for MNIST
- Photograph Object Recognition Dataset
- Loading The CIFAR-10 Dataset in Keras
- Simple CNN for CIFAR-10
- Larger CNN for CIFAR-10
- Extensions To Improve Model Performance
- Movie Review Sentiment Classification Dataset
- Load the IMDB Dataset With Keras
- Word Embeddings
- Simple Multilayer Perceptron Model
- One-Dimensional Convolutional Neural Network
- Crash Course In Recurrent Neural Networks
- Support For Sequences in Neural Networks
- Recurrent Neural Networks
- Long Short-Term Memory Networks
- Problem Description: Time Series Prediction
- Multilayer Perceptron Regression
- Multilayer Perceptron Using the Window Method.
- LSTM Network For Regression
- LSTM For Regression Using the Window Method.
- LSTM For Regression with Time Steps.
- LSTM With Memory Between Batches
- Stacked LSTMs With Memory Between Batches
- Simple LSTM for Sequence Classification
- LSTM For Sequence Classification With Dropout
- LSTM and CNN For Sequence Classification