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Deep Learning Course Content


Artifical  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.

  1. Multilayer Perceptrons.

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 .

  1. 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

 

  1. 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

 

  1. 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:

  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.

 

  1. 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

 

  1. 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 .

 

Updated: April 26, 2018 — 5:14 pm

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