Our one hidden layer multilayered perceptron gave an accuracy of 84.45 on training data and 92.1 on test data. It is good but not good enough. The MNIST database is used as a benchmark for classification problems in machine learning. Next, we see how using TensorFlow's built-in optimizer affects network performance.
- Preface
- TensorFlow - An Introduction
- Introduction
- Installing TensorFlow
- Hello world in TensorFlow
- Understanding the TensorFlow program structure
- Working with constants, variables, and placeholders
- Performing matrix manipulations using TensorFlow
- Using a data flow graph
- Migrating from 0.x to 1.x
- Using XLA to enhance computational performance
- Invoking CPU/GPU devices
- TensorFlow for Deep Learning
- Different Python packages required for DNN-based problems
- Regression
- Neural Networks - Perceptron
- Convolutional Neural Networks
- Advanced Convolutional Neural Networks
- Introduction
- Creating a ConvNet for Sentiment Analysis
- Inspecting what filters a VGG pre-built network has learned
- Classifying images with VGGNet, ResNet, Inception, and Xception
- Recycling pre-built Deep Learning models for extracting features
- Very deep InceptionV3 Net used for Transfer Learning
- Generating music with dilated ConvNets, WaveNet, and NSynth
- Answering questions about images (Visual Q&A)
- Classifying videos with pre-trained nets in six different ways
- Recurrent Neural Networks
- Introduction
- Neural machine translation - training a seq2seq RNN
- Neural machine translation - inference on a seq2seq RNN
- All you need is attention - another example of a seq2seq RNN
- Learning to write as Shakespeare with RNNs
- Learning to predict future Bitcoin value with RNNs
- Many-to-one and many-to-many RNN examples
- Unsupervised Learning
- Autoencoders
- Reinforcement Learning
- Mobile Computation
- Introduction
- Installing TensorFlow mobile for macOS and Android
- Playing with TensorFlow and Android examples
- Installing TensorFlow mobile for macOS and iPhone
- Optimizing a TensorFlow graph for mobile devices
- Profiling a TensorFlow graph for mobile devices
- Transforming a TensorFlow graph for mobile devices
- Generative Models and CapsNet
- Distributed TensorFlow and Cloud Deep Learning
- Introduction
- Working with TensorFlow and GPUs
- Playing with Distributed TensorFlow: multiple GPUs and one CPU
- Playing with Distributed TensorFlow: multiple servers
- Training a Distributed TensorFlow MNIST classifier
- Working with TensorFlow Serving and Docker
- Running Distributed TensorFlow on Google Cloud (GCP) with Compute Engine
- Running Distributed TensorFlow on Google CloudML
- Running Distributed TensorFlow on Microsoft Azure
- Running Distributed TensorFlow on Amazon AWS
- Learning to Learn with AutoML (Meta-Learning)
- TensorFlow Processing Units
There's more...
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