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Deep Learning with TensorFlow Table of Contents

Table of Content


  • Software 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

Setting Up the Deep Learning Playground

  • 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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