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"Programming Language Concepts uses a functional programming language (F#) as the metalanguage in which to present all concepts and examples, and thus has an operational flavour, enabling practical experiments and exercises. It includes basic concepts such as abstract syntax, interpretation, stack machines, compilation, type checking, and garbage collection techniques, as well as the more advanced topics on polymorphic types, type inference using unification, co- and contravariant types, continuations, and backwards code generation with on-the-fly peephole optimization."

"This self-contained textbook covers fundamental aspects of sequence analysis in evolutionary biology, including sequence alignment, phylogeny reconstruction, and coalescent simulation. It addresses these aspects through a series of over 400 computer problems, ranging from elementary to research level to enable learning by doing. Students solve the problems in the same computational environment used for decades in science ? the UNIX command line. This is available on all three major operating systems for PCs: Microsoft Windows, Mac-OSX, and Linux. To learn using this powerful system, students analyze sample sequence data by applying generic tools, bioinformatics software, and over 40 programs specifically written for this course. The solutions for all problems are included, making the book ideal for self-study. Problems are grouped into sections headed by an introduction and a list of new concepts and programs. By using practical computing to explore evolutionary concepts and sequence data, the book enables readers to tackle their own computational problems."

"Process modeling style focuses on other aspects of process modeling beyond notation that are very important to practitioners. Many people who model processes focus on the specific notation used to create their drawings. While that is important, there are many other aspects to modeling, such as naming, creating identifiers, descriptions, interfaces, patterns, and creating useful process documentation. Experience author John Long focuses on those non-notational aspects of modeling, which practitioners will find invaluable."

"Build deep learning applications, such as computer vision, speech recognition, and chatbots, using frameworks such as TensorFlow and Keras. This book helps you to ramp up your practical know-how in a short period of time and focuses you on the domain, models, and algorithms required for deep learning applications. Deep Learning with Applications Using Python covers topics such as chatbots, natural language processing, and face and object recognition. The goal is to equip you with the concepts, techniques, and algorithm implementations needed to create programs capable of performing deep learning. This book covers intermediate and advanced levels of deep learning, including convolutional neural networks, recurrent neural networks, and multilayer perceptrons. It also discusses popular APIs such as IBM Watson, Microsoft Azure, and scikit-learn. You will: Work with various deep learning frameworks such as TensorFlow, Keras, and scikit-learn. Build face recognition and face detection capabilities Create speech-to-text and text-to-speech functionality Make chatbots using deep learning. "