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Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python
Learn TensorFlow 2.0: Implement Machine Learning and Deep Learning Models with Python
Learn how to use TensorFlow 2.0 to build machine learning and deep learning models with complete examples. 

The book begins with introducing TensorFlow 2.0 framework and the major changes from its last release. Next, it focuses on building Supervised Machine Learning models using TensorFlow 2.0.
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Learn Python Programming: The no-nonsense, beginner's guide to programming, data science, and web development with Python 3.7, 2nd Edition
Learn Python Programming: The no-nonsense, beginner's guide to programming, data science, and web development with Python 3.7, 2nd Edition

Learn the fundamentals of Python (3.7) and how to apply it to data science, programming, and web development. Fully updated to include hands-on tutorials and projects.

Key Features

  • Learn the fundamentals of Python programming with interactive projects
  • Apply Python to data...
Python Projects for Beginners: A Ten-Week Bootcamp Approach to Python Programming
Python Projects for Beginners: A Ten-Week Bootcamp Approach to Python Programming

Immerse yourself in learning Python and introductory data analytics with this book’s project-based approach. Through the structure of a ten-week coding bootcamp course, you’ll learn key concepts and gain hands-on experience through weekly projects.

Each chapter in this book is presented as a full week of...

Reinforcement Learning with TensorFlow: A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym
Reinforcement Learning with TensorFlow: A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym

Leverage the power of reinforcement learning techniques to develop self-learning systems using TensorFlow

Key Features

  • Explore reinforcement learning concepts and their implementation using TensorFlow
  • Discover different problem-solving methods for reinforcement...
Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems
Python Deep Learning Projects: 9 projects demystifying neural network and deep learning models for building intelligent systems

Insightful projects to master deep learning and neural network architectures using Python and Keras

Key Features

  • Explore deep learning across computer vision, natural language processing (NLP), and image processing
  • Discover best practices for the training of deep neural...
Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks
Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks
Fun guide to learning Bayesian statistics and probability through unusual and illustrative examples.

Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian
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GIScience Teaching and Learning Perspectives (Advances in Geographic Information Science)
GIScience Teaching and Learning Perspectives (Advances in Geographic Information Science)
This volume uniquely links educational theories and the practice of GIScience in higher education contexts to guide classroom practice, present effective practical implementations from peers, and provide resources and strategies for effective teaching methods. The book offers a comprehensive exploration of GIScience education, including...
Numerical Methods of Mathematics Implemented in Fortran (Forum for Interdisciplinary Mathematics)
Numerical Methods of Mathematics Implemented in Fortran (Forum for Interdisciplinary Mathematics)
This book systematically classifies the mathematical formalisms of computational models that are required for solving problems in mathematics, engineering and various other disciplines. It also provides numerical methods for solving these problems using suitable algorithms and for writing computer codes to find solutions. For discrete...
Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library
Natural Language Processing with TensorFlow: Teach language to machines using Python's deep learning library

Write modern natural language processing applications using deep learning algorithms and TensorFlow

Key Features

  • Focuses on more efficient natural language processing using TensorFlow
  • Covers NLP as a field in its own right to improve understanding for choosing TensorFlow...
Applying Predictive Analytics: Finding Value in Data
Applying Predictive Analytics: Finding Value in Data

This textbook presents a practical approach to predictive analytics for classroom learning. It focuses on using analytics to solve business problems and compares several different modeling techniques, all explained from examples using the SAS Enterprise Miner software. The authors demystify complex algorithms to show how they can be...

Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem
Hands-On Markov Models with Python: Implement probabilistic models for learning complex data sequences using the Python ecosystem

Unleash the power of unsupervised machine learning in Hidden Markov Models using TensorFlow, pgmpy, and hmmlearn

Key Features

  • Build a variety of Hidden Markov Models (HMM)
  • Create and apply models to any sequence of data to analyze, predict, and extract valuable...
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python
Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms...
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