When Eli Manning joined the NFL after being chosen with the first pick in the 2004 draft, many people compared him to his older brother Peyton. After Eli led his New York Giants to victory in the 2007-2008 Super Bowl, people stopped wondering if he was as good as his brother. When Eli was named Super Bowl MVP and the Giants won the big game again in 2011-2012, people began comparing him to football's all-time great quarterbacks.

Talk about having big shoes to fill. Eli Manning - the younger brother of record-breaking Peyton and the son of legendary quarterback Archie - was the NFL’s number-one draft pick in 2004. At first, the media criticized his inconsistent performance as the New York Giants’ quarterback, but he silenced all doubters when he led his team to an unlikely Super Bowl victory in 2008. Find out how Eli proved that he’s a champion quarterback in his own right.

Peyton and Eli Manning are now NFL superstars, but they are still kids in Family Huddle. Readers of all ages will follow along as Eli and Petyon pile into the car with older brother Cooper for a trip to visit their grandparents. Their dad, former NFL star Archie Manning, isat the wheel. The boys joke around and play football at every opportunity. Readers learn about the famous family and football too, as the boys run fun plays like the buttonhook, quarterback sneak, and hook and ladder.Family and football have always been a big deal in the Manning family. Family Huddle is based on some of the Mannings' memories from their days in Louisiana and Mississippi.

A brief look at the life and career of football player Eli Manning.

Ichiro Suzuki was the first Japanese position player (non-pitcher) to make it into the American Major Leagues. People thought that the Japanese couldn’t handle the power and speed of American pitchers. Ichiro proved them wrong. Now in his fourth season, Ichiro has shown that he can hit anything thrown his way and is as good, if not better than many of his American contemporaries. His love of the game, amazing skill and crowd pleasing antics have won him a following of fans around the world.

Born in 1934, Hank Aaron faced many roadblocks because of his race. In fact, his school did not even have a baseball team. When Jackie Robinson broke the race barrier and entered the major leagues, Aaron was given hope that his dream of playing professional baseball could someday come true. Aaron became one of baseball’s best players—winning the World Championship, breaking Babe Ruth’s home run record, and being inducted into the Baseball Hall of Fame.

Although Tony Dungy was passed over in the 1977 NFL draft, Coach Chuck Noll of the Pittsburgh Steelers recognized his athleticism and understanding of the game and signed him as a free agent. Later, at the age of 25, Dungy began his NFL coaching career with the Steelers. He went on to coach the Tampa Bay Buccaneers and then considered retiring from football. Instead, he accepted a coaching job with the Indianapolis Colts and in 2007 became the first African American head coach to win the Super Bowl. Find out how Dungy’s intellect, strength of character, and leadership qualities have made him a champion on and off the field.

“We finally have the definitive treatise on PyTorch! It covers the basics and abstractions in great detail. I hope this book becomes your extended reference document.” —Soumith Chintala, co-creator of PyTorch Key Features Written by PyTorch’s creator and key contributors Develop deep learning models in a familiar Pythonic way Use PyTorch to build an image classifier for cancer detection Diagnose problems with your neural network and improve training with data augmentation Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About The Book Every other day we hear about new ways to put deep learning to good use: improved medical imaging, accurate credit card fraud detection, long range weather forecasting, and more. PyTorch puts these superpowers in your hands. Instantly familiar to anyone who knows Python data tools like NumPy and Scikit-learn, PyTorch simplifies deep learning without sacrificing advanced features. It’s great for building quick models, and it scales smoothly from laptop to enterprise. Deep Learning with PyTorch teaches you to create deep learning and neural network systems with PyTorch. This practical book gets you to work right away building a tumor image classifier from scratch. After covering the basics, you’ll learn best practices for the entire deep learning pipeline, tackling advanced projects as your PyTorch skills become more sophisticated. All code samples are easy to explore in downloadable Jupyter notebooks. What You Will Learn Understanding deep learning data structures such as tensors and neural networks Best practices for the PyTorch Tensor API, loading data in Python, and visualizing results Implementing modules and loss functions Utilizing pretrained models from PyTorch Hub Methods for training networks with limited inputs Sifting through unreliable results to diagnose and fix problems in your neural network Improve your results with augmented data, better model architecture, and fine tuning This Book Is Written For For Python programmers with an interest in machine learning. No experience with PyTorch or other deep learning frameworks is required. About The Authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. Thomas Viehmann is a Machine Learning and PyTorch speciality trainer and consultant based in Munich, Germany and a PyTorch core developer. Table of Contents PART 1 - CORE PYTORCH 1 Introducing deep learning and the PyTorch Library 2 Pretrained networks 3 It starts with a tensor 4 Real-world data representation using tensors 5 The mechanics of learning 6 Using a neural network to fit the data 7 Telling birds from airplanes: Learning from images 8 Using convolutions to generalize PART 2 - LEARNING FROM IMAGES IN THE REAL WORLD: EARLY DETECTION OF LUNG CANCER 9 Using PyTorch to fight cancer 10 Combining data sources into a unified dataset 11 Training a classification model to detect suspected tumors 12 Improving training with metrics and augmentation 13 Using segmentation to find suspected nodules 14 End-to-end nodule analysis, and where to go next PART 3 - DEPLOYMENT 15 Deploying to production

Roberto Clemente grew up dreaming of playing professional baseball in the United States. By the time he was out of high school, it looked like his dream might just come true. Clemente was drafted by a major league team and headed north. He had to overcome a language barrier, racism, and segregation along the way. But he didn’t let these challenges affect his top-notch performance on the field. His career was cut short when he died in a tragic plane crash while bringing aid to earthquake victims in Nicaragua. Follow this heavy hitter on his amazing journey to stardom and success.