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TensorFlow in 1 Day (eBook)

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  • 55,747 Words
  • 364 Pages

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


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