• Build a strong foundation in neural networks and deep learning with Python libraries
• Explore advanced deep learning techniques and their applications...

Work with advanced topics in deep learning, such as optimization algorithms, hyper-parameter tuning, dropout, and error analysis as well as strategies to address typical problems encountered when training deep neural networks. You’ll begin by studying the activation functions mostly with a single neuron (ReLu, sigmoid, and...

• Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow
• Explore deep neural...

Calculus has been used in solving many scientific and engineering problems. For optimization problems, however, the differential calculus technique sometimes has a drawback when the objective function is step-wise, discontinuous, or multi-modal, or when decision variables are discrete rather than continuous. Thus, researchers have...

• Implement AI techniques to build smart applications using Deeplearning4j
• Perform big data analytics to derive quality insights using Spark MLlib
• Create self-learning systems using...

• Focuses on more efficient natural language processing using TensorFlow
• Covers NLP as a field in its own right to improve understanding for choosing TensorFlow...

Learn how to solve challenging machine learning problems with TensorFlow, Google’s revolutionary new software library for deep learning. If you have some background in basic linear algebra and calculus, this practical book introduces machine-learning fundamentals by showing you how to design systems capable of detecting objects...

Mathematica Cookbook helps you master the application's core principles by walking you through real-world problems. Ideal for browsing, this book includes recipes for working with numerics, data structures, algebraic equations, calculus, and statistics. You'll also venture into exotic territory with recipes for data...