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TensorFlow and the Google Cloud ML Engine for Deep Learning [Video]
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-
Free ChapterIntroduction
-
Installation
-
TensorFlow and Machine Learning
-
Working with Images
-
K-Nearest-Neighbors with TensorFlow
-
Linear Regression with a Single Neuron
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Linear Regression in TensorFlow
-
Logistic Regression in TensorFlow
-
The Estimator API
-
Neural Networks and Deep Learning
- Traditional Machine Learning
- Deep Learning
- Operation of a Single Neuron
- The Activation Function
- Training a Neural Network: Back Propagation
- Lab: Automobile Price Prediction - Exploring the Dataset
- Lab: Automobile Price Prediction - Using TensorFlow for Prediction
- Hyper parameters
- Vanishing and Exploding Gradients
- The Bias-Variance Trade-off
- Preventing Overfitting
- Lab: Iris Flower Classification
-
Classifiers and Classification
-
Convolutional Neural Networks (CNNs)
- Mimicking the Visual Cortex
- Convolution
- Choice of Kernel Functions
- Zero Padding and Stride Size
- CNNs vs DNNs
- Feature Maps
- Pooling
- Lab: Classification of Street View House Numbers - Exploring the Dataset
- Basic Architecture of a CNN
- Lab: Classification of Street View House Numbers - Building the Model
- Lab: Classification of Street View House Numbers - Running the Model
- Lab: Building a CNN Using the Estimator API
-
Recurrent Neural Networks (RNNs)
- Learning From the Past
- Unrolling an RNN Cell Through Time
- Training an RNN - Back Propagation Through Time
- Lab: RNNs for Image Classification
- Vanishing and Exploding Gradients in an RNN
- Long Memory Neurons vs Truncated BPTT
- The Long/Short Term Memory Cell
- A Sequence of Words
- Text in Numeric Form
- Lab: Sentiment Analysis on Rotten Tomatoes Reviews - Exploring the Dataset
- Lab: Sentiment Analysis on Rotten Tomatoes Reviews - Building, Running the Model
-
Unsupervised Learning
- Supervised and Unsupervised Learning
- Expressing Attributes as Numbers
- K-Means Clustering
- Lab: K-Means Clustering with 2-Dimensional Points in Space
- Lab: K-Means Clustering with Images
- Patterns in Data
- Principal Components Analysis
- Autoencoders
- Autoencoder Neural Network Architecture
- Lab: PCA on Stock Data - Matplotlib vs Autoencoders
- Stacked Autoencoders
- Lab: Stacked Autoencoder With Dropout
- Lab: Stacked Autoencoder With Regularization and He Initialization
- Denoising Autoencoders
- Lab: Denoising Autoencoder with Gaussian Noise
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TensorFlow on the Google Cloud
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TensorFlow Using Cloud ML Engine
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Feature Engineering and Hyperparameter Tuning
About this
video
TensorFlow is quickly becoming the technology of choice for deep learning, because of how easy TF makes it to build powerful and sophisticated neural networks. The Google Cloud Platform is a great place to run TF models at scale, and perform distributed training and prediction. This is a comprehensive, from-the-basics course on TensorFlow and building neural networks. It assumes no prior knowledge of Tensorflow, all you need to know is basic Python programming.
Styel and Approach
This course focus is to on learning by doing examples. All code files provided to refer and learn.
- Publication date:
- February 2018
- Publisher
- Packt
- Duration
- 17 hours 28 minutes
- ISBN
- 9781789135688