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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
Verlag:
PublishDrive
Veröffentlicht:
2019
Druckseiten:
ca. 213
Sprache:
English
Medientyp:
eBook