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 ContentChapter 1: What is Deep learning?Chapter 2: Machine Learning vs Deep LearningChapter 3: What is TensorFlow?Chapter 4: Comparison of Deep Learning LibrariesChapter 5: How to Download and Install TensorFlow Windows and MacChapter 6: Jupyter Notebook TutorialChapter 7: Tensorflow on AWSChapter 8: TensorFlow Basics: Tensor, Shape, Type, Graph, Sessions & OperatorsChapter 9: Tensorboard: Graph Visualization with ExampleChapter 10: NumPyChapter 11: PandasChapter 12: Scikit-LearnChapter 13: Linear RegressionChapter 14: Linear Regression Case StudyChapter 15: Linear Classifier in TensorFlowChapter 16: Kernel MethodsChapter 17: TensorFlow ANN (Artificial Neural Network)Chapter 18: ConvNet(Convolutional Neural Network): TensorFlow Image ClassificationChapter 19: Autoencoder with TensorFlowChapter 20: RNN(Recurrent Neural Network) TensorFlow
Author: Krishna Rungta
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