Machine Learning with TensorFlow PDF

DESCRIPTION

TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine. Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters : exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own. What's Inside : Matching your tasks to the right machine-learning and deep-learning approaches ; Visualizing algorithms with TensorBoard ; Understanding and using neural networks.

INFORMATION

TensorFlow 2.0 is designed to make building neural networks for machine learning easy, which is why TensorFlow 2.0 uses an API called Keras. The book 'Deep Learning in Python' by Francois Chollet, creator of Keras, is a great place to get started. Read chapters 1-4 to understand the fundamentals of ML from a programmer's perspective.

Machine Learning with TensorFlow. GETTING STARTED. Appx A Installation. Ch 1 A machine learningodyssey. Ch 2 TensorFlowessentials. CORE ALGORITHMS. Ch 3 Linear regressionand beyond. Ch 4 An introduction to classification. Ch 5 Automatically clusteringdata. Ch 6 Hidden Markovmodels. NEURAL NETWORKS. Ch 7 A peek into autoencoders. Ch 8 Reinforcement Learning. Ch 9 Convolutionalneural networks ...

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