Tracklist Sex Drive El Diablo Hollywood Whore Glass House Burning Memories A Message From The Count Floor 13 Roulette Truck Norris Interlude Death In My Pocket Candy Waste Love 53666I Think I'm Okay ...

Please note this is a region 2 DVD & region B Blu-ray It will require a region B Blu-ray player to play the Blu-ray & DVD or a Region 2 DVD player for the DVD A former biker & drug dealer single-handedly takes on the nefarious Lord&s Resistance Army in a noble bid to save young Sudanese refugees from a life of violence & suffering Inspired by actual events Marc Forster&s MACHINE GUN PREACHER stars Gerard Butler as Sam Childers a tough-as-nails biker whose heart shatters upon witnessing the harrowing plight of children in Sudan Determined that no child shall be enslaved by the brutal Lord&s Resistance Army Childers turns to God for inspiration & establishes an orphanage for wounded kids & child soldiers When the Lord&s Resistance Army refuses to stand down Childers organizes a militia to protect the children In helping to give the orphans of war-torn Sudan a better future the former outlaw begins to find new meaning in his own life as well Actors Gerard Butler Michelle Monaghan Kathy Baker Michael Shannon Madeline Carroll Souleymane Sy Savane Grant R Krause Reavis Graham Peter Carey Barbara Coven Misty Mills Nicole Michele Sobchack Sidi Henderson & Mike Litak Director Marc Forster Certificate 15 years & over Year 2011 Languages English ...

Please note this is a region 2 DVD & will require a region 2 (Europe) or region Free DVD Player in order to play A former biker & drug dealer single-handedly takes on the nefarious Lord&s Resistance Army in a noble bid to save young Sudanese refugees from a life of violence & suffering Inspired by actual events Marc Forster&s MACHINE GUN PREACHER stars Gerard Butler as Sam Childers a tough-as-nails biker whose heart shatters upon witnessing the harrowing plight of children in Sudan Determined that no child shall be enslaved by the brutal Lord&s Resistance Army Childers turns to God for inspiration & establishes an orphanage for wounded kids & child soldiers When the Lord&s Resistance Army refuses to stand down Childers organizes a militia to protect the children In helping to give the orphans of war-torn Sudan a better future the former outlaw begins to find new meaning in his own life as well ...

' Some bright kid's got a gun & 2000 rounds of live ammo & that gun's no pea-shooter It'll go through a brick wall at a quarter of a mile' Chas Mc Gill has the second-best collection of war souvenirs in Garmouth & he desperately wants it to be the best When he stumbles across the remains of a German bomber crashed in the woods
- its shiny black machine-gun still intact
- he grabs his chance Soon he's masterminding his own war effort with dangerous & unexpected results The Machine Gunners is Robert Westall's gripping first novel for children set during World War Two & winner of the Carnegie Medal Now with a brilliant cover look celebrating its fortieth anniversary

Includes:: a bonus short story
- ' The Haunting of Chas Mc Gill'
- & an extended biography of the author





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Tracklist Now We Die Killers & Kings Ghosts Will Haunt My Bones Night Of Long Knives Sail Into The Black Eyes Of The Dead Beneath The Silt In Comes The Flood Damage Inside Game Over Imaginal Cells Take Me Through The Fire ...

A comprehensive introduction to machine learning that uses probabilistic models & inference as a unifying approach Today's Web-enabled deluge of electronic data calls for automated methods of data analysis Machine learning provides these developing methods that can automatically detect patterns in data & then use the uncovered patterns to predict future data This textbook offers a comprehensive & self-contained introduction to the field of machine learning based on a unified probabilistic approach The coverage combines breadth & depth offering necessary background material on such topics as probability optimization & linear algebra as well as discussion of recent developments in the field including conditional random fields L1 regularization & deep learning The book is written in an informal accessible style complete with pseudo-code for the most important algorithms All topics are copiously illustrated with color images & worked examples drawn from such application domains as biology text processing computer vision & robotics Rather than providing a cookbook of different heuristic methods the book stresses a principled model-based approach often using the language of graphical models to specify models in a concise & intuitive way Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online The book is suitable for upper-level undergraduates with an introductory-level college math background & beginning graduate students ...

A comprehensive introduction to machine learning that uses probabilistic models & inference as a unifying approach Today&s Web-enabled deluge of electronic data calls for automated methods of data analysis Machine learning provides these developing methods that can automatically detect patterns in data & then use the uncovered patterns to predict future data This textbook offers a comprehensive & self-contained introduction to the field of machine learning based on a unified probabilistic approach The coverage combines breadth & depth offering necessary background material on such topics as probability optimization & linear algebra as well as discussion of recent developments in the field including conditional random fields L1 regularization & deep learning The book is written in an informal accessible style complete with pseudo-code for the most important algorithms All topics are copiously illustrated with color images & worked examples drawn from such application domains as biology text processing computer vision & robotics Rather than providing a cookbook of different heuristic methods the book stresses a principled model-based approach often using the language of graphical models to specify models in a concise & intuitive way Almost all the models described have been implemented in a MATLAB software package -- PMTK (probabilistic modeling toolkit) -- that is freely available online The book is suitable for upper-level undergraduates with an introductory-level college math background & beginning graduate students ...

As one of the most comprehensive machine learning texts around this book does justice to the field's incredible richness but without losing sight of the unifying principles Peter Flach's clear example-based approach begins by discussing how a spam filter works which gives an immediate introduction to machine learning in action with a minimum of technical fuss Flach provides case studies of increasing complexity & variety with well-chosen examples & illustrations throughout He covers a wide range of logical geometric & statistical models & state-of-the-art topics such as matrix factorisation & ROC analysis Particular attention is paid to the central role played by features The use of established terminology is balanced with the introduction of new & useful concepts & summaries of relevant background material are provided with pointers for revision if necessary These features ensure Machine Learning will set a new standard as an introductory textbook ...

Now that storage & collection technologies are cheaper & more precise methods for extracting relevant information from large datasets is within the reach any experienced programmer willing to crunch data With this book you'll learn machine learning & statistics tools in a practical fashion using black-box solutions & case studies instead of a traditional math-heavy presentation By exploring each problem in this book in depth
- including both viable & hopeless approaches
- you'll learn to recognize when your situation closely matches traditional problems Then you'll discover how to apply classical statistics tools to your problem Machine Learning for Hackers is ideal for programmers from private public & academic sectors

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A Proven Hands-On Approach for Students without a Strong Statistical Foundation Since the best-selling first edition was published there have been several prominent developments in the field of machine learning including the increasing work on the statistical interpretations of machine learning algorithms Unfortunately computer science students without a strong statistical background often find it hard to get started in this area Remedying this deficiency Machine Learning An Algorithmic Perspective Second Edition helps students understand the algorithms of machine learning It puts them on a path toward mastering the relevant mathematics & statistics as well as the necessary programming & experimentation New to the Second Edition Two new chapters on deep belief networks & Gaussian processes Reorganization of the chapters to make a more natural flow of content Revision of the support vector machine material including a simple implementation for experiments New material on random forests the perceptron convergence theorem accuracy methods & conjugate gradient optimization for the multi-layer perceptron Additional discussions of the Kalman & particle filters Improved code including better use of naming conventions in Python Suitable for both an introductory one-semester course & more advanced courses the text strongly encourages students to practice with the code Each chapter

Includes:: detailed examples along with further reading & problems All of the code used to create the examples is available on the author's website

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