Table of Content
Module 1
INSTALLATION
- The Course Overview
- Installation
KERAS INTRODUCTION
- Introduction
- Keras Backends
- Design and Compile a Model
- Model Training, Evaluation, and Prediction
- Training with Data Augmentation
- Training with Transfer Learning and Data Augmentation
SCALING DEEP LEARNING USING KERAS AND TENSORFLOW
- Introduction to TensorFlow
- Introduction to TensorBoard
- Types of Parallelism in Deep Learning – Synchronous and Asynchronous
- Distributed TensorFlow
- Configuring Keras to use TensorFlow for Distributed Problems
TRAINING, TUNING, AND SERVING OUR MODEL IN THE CLOUD
- Introduction
- Introduction to Google Cloud Machine Learning Engine
- Datasets, Feature Columns, and Estimators
- Representing Data in TensorFlow
- Quick Dive into TensorFlow Estimators
- Creating Data Input Pipelines
- Setting Up Our Estimator
- Packaging Our Model
- Training with Google Cloud ML Engine
- Hyperparameter Tuning in the Cloud
- Deploying Our Model for Prediction
- Creating Our Prediction API
- Wrapping Up
- Course Summary
Module 2
SETTING UP THE DEEP LEARNING PLAYGROUND
- The Course Overview
- TensorFlow for Building Deep Learning Models
- Basic Syntaxes, Function Optimization, Variables, and Placeholders
- TensorBoard for Visualization
TRAINING DEEP FEED-FORWARD NEURAL NETWORKS WITH TENSORFLOW
- Start by Loading the Imported Dataset
- Building the Layers of the Neural Network in TensorFlow
- Optimizing the Softmax Cross Entropy Function
- Using DNN Predicting Whether Breast Cancer Cells Are Benign or Not
APPLYING CNN ON TWO REAL DATASETS
- Importing the Two Datasets Using TensorFlow and Sklearn API
- Writing the TensorFlow Code to Add Convolutional and Pooling Layers
- Using tf.train.AdamOptimizer API to Optimize CNN
- Implementing CNN to Create a Face Recognition System
EXERCISE RNN TO SOLVE TWO TIME SERIES PROBLEMS
- Understanding the RNN and the Need for LSTM
- Implementing RNN
- Monthly Riverflow Prediction of Turtle River in Ontario
- Implement LSTM Project to Predict Decimal Number of Given Binary Representation
USING AUTOENCODERS TO EFFICIENTLY REPRESENT DATA
- Encoder and Decoder for Efficient Data Representation
- TensorFlow Code Using Linear Autoencoder to Perform PCA on a 4D Dataset
- Using Stacked Autoencoders for Representation on MNIST Dataset
- Build a Deep Autoencoder to Reduce Latent Space of LFW Face Dataset
GENERATIVE ADVERSARIAL NETWORKS FOR CREATING SYNTHETIC DATASET
- Generative Adversarial Networks for Creating Synthetic Dataset
- Downloading and Setting Up the (Microsoft Research Asia) Geolife Project Dataset
- Coding the Generator and Discriminator Using TensorFlow
- Training GANs to Create Synthetic GPS Based Trajectories
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