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 learning 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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Rafbók: 20 maj 2019