Books on the topic 'Tensorflow/Keras'

Anshik. AI for Healthcare with Keras and Tensorflow 2.0. Apress, 2021. http://dx.doi.org/10.1007/978-1-4842-7086-8.

Deep learning with keras: Implement neural networks with Keras on Theano and Tensorflow. Packt, 2017.

Duerr, Oliver, Beate Sick, and Elvis Murina. Probabilistic Deep Learning: With Python, Keras and TensorFlow Probability. Manning Publications, 2020.

Capelo, Luis. Beginning Application Development with TensorFlow and Keras: Learn to design, develop, train, and deploy TensorFlow and Keras models as real-world applications. Packt Publishing, 2018.

Nandy. Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python. Apress, 2019.

Nandy, Abhishek, and Manisha Biswas. Reinforcement Learning: With Open AI, TensorFlow and Keras Using Python. Apress, 2017.

II, Taweh Beysolow. Applied Reinforcement Learning with Python: With OpenAI Gym, Tensorflow, and Keras. Apress, 2019.

Ballard, Will. Hands-On Deep Learning for Images with TensorFlow: Build intelligent computer vision applications using TensorFlow and Keras. Packt Publishing, 2018.

Fandango, Armando. Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras. Packt Publishing - ebooks Account, 2018.

Paul, Debjyoti, and Charan Puvvala. Video Analytics Using Deep Learning: Building Applications with TensorFlow, Keras, and YOLO. Apress, 2020.

Dadhich, Abhinav. Practical Computer Vision: Extract insightful information from images using TensorFlow, Keras, and OpenCV. Packt Publishing, 2018.

Silaparasetty, Vinita. Deep Learning Projects Using TensorFlow 2: Neural Network Development with Python and Keras. Apress, 2020.

Kalin, Josh. Generative Adversarial Networks Cookbook: Over 100 recipes to build generative models using Python, TensorFlow, and Keras. Packt Publishing, 2018.

Deep Learning for Computer Vision: Expert techniques to train advanced neural networks using TensorFlow and Keras. Packt Publishing, 2018.

Aprende Machine Learning con Scikit-Learn, Keras y TensorFlow : Conceptos, herramientas y técnicas para conseguir sistemas inteligentes . Anaya, 2020.

Practical Deep Learning for Cloud and Mobile: Hands-On Computer Vision Projects Using Python, Keras and TensorFlow. O'Reilly Media, Incorporated, 2019.

Bernico, Mike. Deep Learning Quick Reference: Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras. Packt Publishing, 2018.

Manaswi, Navin Kumar. Deep Learning with Applications Using Python: Chatbots and Face, Object, and Speech Recognition With TensorFlow and Keras. Apress, 2018.

Python Deep Learning: Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow. 2nd ed. Packt Publishing, 2019.

Bali, Raghav, Dipanjan Sarkar, and Tamoghna Ghosh. Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras. Packt Publishing, 2018.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, 2019.

Pattanayak, Santanu. Intelligent Projects Using Python: 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras. Packt Publishing, 2019.

Galea, Alex, and Luis Capelo. Applied Deep Learning with Python: Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions. Packt Publishing, 2018.

R Deep Learning Essentials: A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet, 2nd Edition. Packt Publishing, 2018.

Capelo, Luis, and Harveen Singh Chadha. Applied TensorFlow and Keras Workshop: Develop Your Practical Skills by Working Through a Real-World Project and Build Your Own Bitcoin Price Prediction Tracker. Packt Publishing, Limited, 2020.

Graph, Mark. Deep Learning with Python: The Ultimate Guide to Understand Deep Neural Networks with Python through PyTorch, TensorFlow and Keras. Discover the Ethical Implications of Deep Learning in the New World. Independently published, 2019.