Tensorflow is the most popular Deep Learning Library out there. It has fantastic graph computations feature which helps data scientist to visualize his designed neural network using TensorBoard. This Machine learning library supports both Convolution as well as Recurrent Neural network. It supports parallel processing on CPU as well as GPU. Prominent machine learning algorithms supported by TensorFlow are Deep Learning Classification, wipe & deep, Boston Tree amongst others. The book is very hands-on and gives you industry ready deep learnings practices.
Here is what is covered in the book –
Table Of Content
Chapter 1: What is Deep learning?
Chapter 2: Machine Learning vs Deep Learning
Chapter 3: What is TensorFlow?
Chapter 4: Comparison of Deep Learning Libraries
Chapter 5: How to Download and Install TensorFlow Windows and Mac
Chapter 6: Jupyter Notebook Tutorial
Chapter 7: Tensorflow on AWS
Chapter 8: TensorFlow Basics: Tensor, Shape, Type, Graph, Sessions & Operators
Chapter 9: Tensorboard: Graph Visualization with Example
Chapter 10: NumPy
Chapter 11: Pandas
Chapter 12: Scikit-Learn
Chapter 13: Linear Regression
Chapter 14: Linear Regression Case Study
Chapter 15: Linear Classifier in TensorFlow
Chapter 16: Kernel Methods
Chapter 17: TensorFlow ANN (Artificial Neural Network)
Chapter 18: ConvNet(Convolutional Neural Network): TensorFlow Image Classification
Chapter 19: Autoencoder with TensorFlow
Chapter 20: RNN(Recurrent Neural Network) TensorFlow
Tensorflow is the most popular Deep Learning Library out there. It has fantastic graph computations feature which helps data scientist to visualize his designed neural network using TensorBoard. This Machine learning library supports both Convolution as well as Recurrent Neural network. It supports parallel processing on CPU as well as GPU. Prominent machine learning algorithms supported by TensorFlow are Deep Learning Classification, wipe & deep, Boston Tree amongst others. The book is very hands-on and gives you industry ready deep learnings practices.
Here is what is covered in the book –
Table Of Content
Chapter 1: What is Deep learning?
Chapter 2: Machine Learning vs Deep Learning
Chapter 3: What is TensorFlow?
Chapter 4: Comparison of Deep Learning Libraries
Chapter 5: How to Download and Install TensorFlow Windows and Mac
Chapter 6: Jupyter Notebook Tutorial
Chapter 7: Tensorflow on AWS
Chapter 8: TensorFlow Basics: Tensor, Shape, Type, Graph, Sessions & Operators
Chapter 9: Tensorboard: Graph Visualization with Example
Chapter 10: NumPy
Chapter 11: Pandas
Chapter 12: Scikit-Learn
Chapter 13: Linear Regression
Chapter 14: Linear Regression Case Study
Chapter 15: Linear Classifier in TensorFlow
Chapter 16: Kernel Methods
Chapter 17: TensorFlow ANN (Artificial Neural Network)
Chapter 18: ConvNet(Convolutional Neural Network): TensorFlow Image Classification
Chapter 19: Autoencoder with TensorFlow
Chapter 20: RNN(Recurrent Neural Network) TensorFlow
TensorFlow in 1 Day: Make your own Neural Network
364
TensorFlow in 1 Day: Make your own Neural Network
364
Product Details
| BN ID: | 2940156407682 |
|---|---|
| Publisher: | PublishDrive |
| Publication date: | 05/20/2019 |
| Sold by: | PUBLISHDRIVE KFT |
| Format: | eBook |
| Pages: | 364 |
| File size: | 5 MB |
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