'Dive into Deep Learning' book cover
Dive into Deep Learning, an open source, interactive book provided in a unique form factor that integrates text, mathematics and code, recently added a new chapter on attention mechanisms.

Amazon team adds key programming frameworks to Dive into Deep Learning book

With PyTorch and TensorFlow incorporated, the authors hope to gain a wider audience.

Over the past few years, a team of Amazon scientists has been developing a book that is gaining popularity with students and developers attracted to the booming field of deep learning, a subset of machine learning focused on large-scale artificial neural networks. Called Dive into Deep Learning, the book arrives in a unique form factor, integrating text, mathematics, and runnable code. Drafted entirely through Jupyter notebooks, the book is a fully open source living document, with each update triggering updates to the PDF, HTML, and notebook versions.

Its authors are Aston Zhang, an AWS senior applied scientist; Zachary Lipton, an AWS scientist and assistant professor of Operations Research and Machine Learning at Carnegie Mellon University; Mu Li, AWS principal scientist; and Alex Smola, AWS vice president and distinguished scientist.

Recently the authors added two programming frameworks to their book: PyTorch and TensorFlow. That gives the book—originally written for MXNet—even broader appeal within the open-source machine-learning community of students, developers, and scientists. The book also is incorporated into Amazon Machine Learning University courseware.

Amazon Science spoke to the authors previously about their book, and we recently reconnected with them to learn about the significance of the new frameworks they’ve added to their book.

Q. What’s the significance of adding PyTorch and TensorFlow implementations to Dive into Deep Learning?

Mu Li: The book is designed to teach people different algorithms used in machine learning. A big asset of the book is the fact we provide all the coding information. Originally, we used MXNet because it’s a major interface and easy to learn. But then we started getting a lot of requests for PyTorch and TensorFlow implementations. So, we decided to add them to the book.

Aston Zhang: Another factor is that for machine learning practitioners, it’s not enough to know just one framework. That’s because a researcher may propose a new model or algorithm and provide implementation in only one framework. So, if you don’t know that framework, you can’t work with the model. Dive into Deep Learning now provides a way to address those different implementations. It fixes a pain point for our readers.

Zachary Lipton: Like any good product, you have to pay attention to what people are doing. And the audience available for a book that's only available in one framework is somewhat limited. Already, a great team from IIT Roorkee asked us if they could translate the code portions of our book, yielding a PyTorch version, and we gave it our blessing. We knew that a massive audience of students and practitioners would be excited for the PyTorch and TensorFlow versions.

Q. How does the change make the book better?

Alex Smola: The book is basically a collection of Jupyter notebooks – you can read the book in your web browser and run every code example in real time. Because we support multiple frameworks, we can have multiple code paths within each notebook, so you can compare both the implementations, and the results that they give side by side. That’s very powerful as a teaching tool.

Mu Li: We feel that by adding PyTorch and TensorFlow to Dive into Deep Learning, we’ve made it the best textbook to learn about and execute machine learning and deep learning. It’s a textbook, but also teaches you how to implement the code. Another thing is that some people already using PyTorch want to systematically learn deep learning. Now they can run different algorithms from scratch and learn how to do that in different frameworks.

Zachary Lipton: Nobody can survive as a professional in machine learning without having the skills to work with multiple frameworks. You might learn in MXNet or TensorFlow, but then switch jobs, and need to rapidly port those skills over to a place that uses PyTorch when you’re not familiar with it. In general, it’s important for people to learn several languages.

Q. Is any one of the frameworks superior to the others?

Alex Smola: Each of them has some advantages over the other and given the state of the open-source landscape, those advantages constantly shift. They’re all competing with each other for which is the fastest, most usable, has the best data loaders and so on. At one point, people argued that philosophy was best written in German, and music best written in Italian. If you want to have the widest audience, you don’t want to limit yourself to one approach to doing things.

Aston Zhang: We’re not asking our readers to use just one framework. We provide three implementations. Readers can click on each framework, learn how it works, and decide what works best for them. If you’re a new user, you can see the subtle differences between the three and can compare their speed. Also, we separate text and code—the text is framework-neutral, but in the code book we ask people to contribute material. We’ve had people from Google, Alibaba, IIT students and others add material. For the first few chapters, Anirudh Dagar and Yuan Tang have contributed most of the PyTorch and TensorFlow adaptations.. Many others have also helped with the adaptations to these frameworks.

Zachary Lipton: The book is starting to be useful as a Rosetta stone of sorts to allow practitioners to see what the best strategy is to solve the same problem in multiple frameworks— MXNet, PyTorch, TensorFlow—without having to chase down incompatible and idiosyncratic variants on GitHub.

Q. Was it challenging to add the different frameworks to the book?

Mu Li: Yes! PyTorch and MXNet are similar, but TensorFlow is pretty different. Fortunately, TensorFlow 2.0 is very different from TensorFlow 1.0, and closer to MXNet.

Alex Smola: The proper tuning and refinement of the models took quite a while to ensure the implementations for TensorFlow on modern convolutional neural networks were just as good as the ones in PyTorch and MXNet. That’s due to the different ways in how the frameworks implement things. And we still need to back-port the content into Mandarin. This isn’t a trivial endeavor, because there currently isn’t great tooling available to synch the text with the source code.

Q. What has been the response to the additions to Dive into Deep Learning?

Mu Li: Very good. In the past three months, compared with the prior three, we’ve seen about a 40 percent increase in users.

Q. What motivates you to continue improving Dive into Deep Learning?

Alex Smola: We write books because we want to teach and share content. It’s also our way of saying “thanks” to the machine-learning community. The book is a key enabler for spreading knowledge about machine learning and its applications much more widely. We want to make it easy for people to come in, learn about machine learning, and then surprise us with their additions to the book.

Zach Lipton: I don't think anyone involved in the project thinks of it as a book that will someday be finished, in the traditional sense. Having everything online, we can update and add material much, much more quickly than if it were made from dead trees.

Aston Zhang: Every day we get feedback from users around the world. Their comments, suggestions, encouragement, and endorsement motivates me to continue improving our book.

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Amazon’s commitment to The Climate Pledge motivates us to work both inside and outside of our business operations to enact carbon emissions mitigation. Along with the emissions reduction initiatives within our business operations, we have a robust strategy aligned with climate science for tackling climate change outside of our business, beginning with nature-based solutions. For example, in April 2021 Amazon announced its leading role in the LEAF Coalition – an initiative that aims to raise global climate ambition and contribute to halting tropical and subtropical deforestation and forest degradation by 2030. As we continue to develop and innovate, Amazon is seeking an experienced Research Scientist to help us identify, evaluate, and guide research efforts in nature-based solutions as well as emerging areas. The ideal candidate will have a deep understanding of carbon cycles and be able to evaluate projects and initiatives across a wide range of natural and technological climate mitigation measures from a first-principles perspective. They will have expertise in one or more types of climate solutions, but more importantly will understand the broad field of solutions.The successful candidate will be part of the team executing on Amazon’s commitment to The Climate Pledge. They will need to work with others to create a system to evaluate nature-based and technological climate solution projects, and make recommendations to senior leadership. This will require strong writing skills and an ability to translate technical material for a general audience.Key Responsibilities:· Evaluate nature-based and technological climate solution projects using a scientifically rigorous first-principles approach.· Evaluate project and research proposals.· Collaborate with internal and external teams to scope, execute, and evaluate highly technical initiatives related to nature-based and technological climate solution projects.· Translate findings into reports and presentations that can be shared internally and externally.· Respond to time-critical questions from multiple business teams and communicate professionally with senior business leaders.
US, WA, Seattle
Would you like to join a fast-paced team creating cutting edge econometrics and machine learning tools used to enhance the lives of Amazon Prime members worldwide? If so, the Prime Economics and Machine learning team is looking for you! We are seeking a talented economist to help us build the next generation of counterfactual modeling tools in Prime. This team is comprised of economists, applied scientists, software/data engineers, and product managers. We work closely with senior Prime leadership to model the implications of company-wide strategic decisions and expect our models to increasingly power customer-facing software systems.A day in the lifeYou build econometric models of Prime customer behavior from scratch. This involves exploring data to motivate modeling assumptions and proposing identification strategies, statistical estimators, and finite/large sample inference strategies. This science usually combines tools from machine learning, reduced-form causal inference, and structural econometrics, often leading to the creation of novel estimators. You vet your work with a team of econometricians, PhD computer scientists/applied mathematicians/statisticians, and well-known academics who serve as Amazon Scholars. You build science using the latest distributed computing infrastructures (e.g., PySpark), partnering with our data/software engineers and product managers to turn this science into scalable, system-facing, high-impact software.About the hiring groupWhat makes our team unique is three things. First, it has a broad mandate to model customer behavior (given that Prime is Amazon’s flagship membership program). Second, we build cutting-edge customer-level simulations using a variety of statistical tools (ML/econometrics/causal inference). This simulation capability is fairly unique across economics/ML-building teams at Amazon, creating green-field scientific and engineering challenges to invent solutions for. Third, given Prime's central role within the company, we have extensive access to senior company leaders, since we often provide timely insights for important strategic decisions. In other words, Prime science serves a "central brains" function for Amazon.Job responsibilitiesMULTIPLE POSITIONS AVAILABLEEntity: Amazon.com Services LLC, an Amazon.com CompanyTitle: EconomistWorksite: Seattle, WAPosition Responsibilities:Work directly with a team of economists and senior management on key business problems faced by Amazon Prime and Prime benefit teams. Apply the frontier of economic thinking to market design, pricing, forecasting, program evaluation, online advertising and other areas. Build econometric models using company data. Apply economic or econometric theory to solve business problems. Develop new techniques to process large data sets, address quantitative problems, and contribute to the design of automated systems. Apply tools from applied micro-econometrics (e.g. experimental design, difference-in-difference, regression discontinuity, and IV) or forecasting (essential time series models). Leverage big data tools for data extraction. Write up and present analyses for distribution to various levels of management at Amazon.Amazon.com is an Equal Opportunity-Affirmative Action Employer – Minority / Female / Disability / Veteran / Gender Identity / Sexual Orientation #0000Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Seattle
Do you want to know about new movies and TV shows that you'll enjoy the day they come out? So do millions of our customers, world wide. Did you know we have a wide range of niche content including Natural Park documentaries, Kung-fu movies, or Korean dramas on our service? No? Well, we're looking to change that. Come be part of history, as we fulfill Prime Video's vision of being customer's first place to find something to watchA day in the lifeWe're using cutting edge approaches such as graph convolutional networks (GCNs) to connect information about our videos and customers from a variety of information sources, acquiring and processing data sets on a scale that only a few companies in the world can match. This will enable us to recommend titles effectively, even when we don't have a large behavioral signal (to tackle the cold-start title problem). It will also allow us to find our customer's niche interests, helping them discover groups of titles that they didn't even know existed. We are looking for creative & customer obsessed machine learning scientists who can apply the latest research, state of the art algorithms and ML to build highly scalable page personalization solutions. You'll be a research leader in the space and a hands-on ML practitioner, guiding and collaborating with talented teams of engineers and scientists and senior leaders in the Prime Video organization. You will also have the opportunity to publish your research at internal and external ML conferences (e.g. https://dl.acm.org/citation.cfm?id=3292500.3330675).About the hiring groupPrime Video Recommendation science team owns different aspects of personalization algorithms, from user-item relevance, item-item relevance, and page composition, to name a few. We work closely with the engineering teams to put our models in production.Job responsibilities· Develop ML models for various recommendation systems using deep learning, online learning, and optimization methods· Work closely with engineers and product managers to expand the depth of our product insights with data, create a variety of experiments, and determine the highest-impact projects to include in planning roadmaps· Provide technical and scientific guidance to your team members· Stay up-to-date with advancements and the latest modeling techniques in the field· Publish your research findings in top conferences and journalsAmazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Bellevue
The Last Mile Global Fleet and Products (GFP) team’s mission is to provide the vehicles and services that enable Amazon to delight customers through our Last Mile operations. Amazon is continually striving to innovate and provide best-in-class delivery experiences through the introduction of pioneering new products. GFP is on track to purchase and deploy 100,000 electric vehicles (EVs) by 2030 as part of Amazon’s Climate Pledge.Are you inspired by...· The rapid growth and expansion of electric vehicles into the market?· Being part of a effort to drastically reduce carbon emissions?· Working on tough problems at a scale and pace never done before?Then come join our team and help us make history!As a Sr. Research Scientist on the GFP Energy Systems team, you’ll apply your knowledge of electric vehicles (EVs) and couple that with a background in modeling, data science, and software development to help us predict how our EVs will perform in the field. You’ll develop a deep understanding of how our EVs use energy while in use, and provide guidance and recommendations to our deployment and business teams to help ensure our fleet is successful as we scale. This role is inherently cross-functional and requires working closely with software teams, research teams, operations teams, and product management teams to help deploy our EVs into an electrified Last Mile network.#lastmileAmazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.
US, WA, Seattle
Amazon’s Sustainability Science and Innovations (SSI) organization creates the tools and data to map, measure, and model Amazon's environmental and social impacts at enormous scale.The Role:We're looking for an outstanding Science Manager who combines technical, research, and team management capabilities with a demonstrated ability to get the right things done quickly and effectively.In this role you will lead a team of scientists and will also be responsible for conducting assessments of environmental and social issues across Amazon, evaluating the sustainability impacts of supply chains, from manufacturing, to transportation, to consumer use, to end-of-life. This candidate will also possess excellent communication, negotiation and influencing skills to drive consensus across multiple stakeholders.This is a unique opportunity for someone who wants to combine their passion for sustainability, solving customer problems using data, and building cutting edge machine learning solutions. You will be expected to dive deep into large-scale economic problems, enable measurable actions on the Sustainable economy, and work closely with a large team of scientists and economists. We are particularly interested in candidates with experience building predictive models with Amazon data.Key Responsibilities:· Manage sustainability-related research projects through all stages: framing, data collection, analysis, modeling and visualization, and reporting.· Lead and develop a team of scientists.· Create tools and methods to harvest and continuously update data to provide sustainability insights at scale.· Respond to time critical questions from multiple business teams.· Professionally communicate to senior business leaders.· Work closely with software engineering teams to drive real-time model implementations and new feature creations.· Lead the early investigative / inception phase of strategic sustainability initiatives and effectively influence, negotiate, and communicate with stakeholders to enable hand off of those projects for implementation.
US, VA, Arlington
Amazon’s Global Learning & Development (GLD) Learning Science and Engineering team is growing quickly and is looking for a skilled, driven, applied scientist to develop solutions and innovate at the intersection of human and machine learning.The Learning Science and Engineering org is reinventing workplace learning by building the programs, products, technologies and mechanisms that make learning effective and scalable for Amazon employees. We use machine learning to augment human decision-making to accelerate and personalize learning and skill development. We have a passion for raising the bar on learning and learning design at Amazon, and simultaneously contributing to the science of human and machine learning. We partner with multiple businesses across Amazon to explore ways to help Amazon employees grow their knowledge and capabilities. We publish research and present our work at internal and external conferences.We are looking for someone with a passion for learning and experience applying machine learning to augment human decision-making. The role will continuously improve learning programs using an array of creative approaches which drive productivity, innovation, and operational excellence. As an Applied Scientist, you will be working with scientists, engineers, learning designers, product managers, and stakeholders to deliver products or features of products that accelerate learning.Specific responsibilities include:· Design, develop, and implement models and solutions to support collaborative learning environments that can help accelerate human learning.· Deliver Machine Learning solutions to improve learning experiences that can help learners learn more effectively and efficiently.· Deliver data-driven solutions to assist learning designers to scale up best design practices.· Deliver innovative and impactful solutions that can solve customer pain points.· Deliver scientific findings about human learning to the broader science communities.
US, WA, Seattle
Prime Video is disrupting traditional media with an ever-increasing selection of movies, TV shows, Emmy Award-winning original content, add-on subscriptions including HBO and Showtime, and live events like Thursday Night Football and Major League Baseball. We are a premier provider of digital entertainment worldwide and we continue to grow very quickly! We need your passion, innovative ideas, and creativity to help continue to deliver on our ambitious goals.Do you want to join an innovative team of scientists who use machine learning and statistical techniques to help Amazon provide the best customer experience ? Do you want to build advanced algorithmic systems that help millions of customer every day? If yes, then you may be a great fit to join our Amazon Prime Video team.A day in the lifeAs an applied scientist, you will apply your knowledge of deep learning to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable digging in to customer requirements as you are drilling into design with development teams and developing production ready learning models.You will work with internal and external stakeholders, cross-functional partners, and end-users around the world at all levels.About the hiring groupWe are expanding our scene understanding team to drive compliance automation and exceptional customer experience using machine learning, computer vision, audio processing, and natural language understanding. Automation of video understanding at scale is our mission and passion. We need to solve problems across many cultures and languages. we have a huge amount of human-labelled data, and operation team to generate labels across many languages. Our team innovates, with many novel patents, inventions, and papers in the motion picture and television industry. We are highly motivated to extend the state of the art.Job responsibilitiesAs an applied scientist, you will apply your knowledge of deep learning to concrete problems that have broad cross-organizational, global, and technology impact. Your work will focus on training and evaluating models and deploying them to production where we continuously monitor and evaluate. You will work on large engineering efforts that solve significantly complex problems facing global customers. You will be trusted to operate with independence and are often assigned to focus on areas with significant impact on audience satisfaction. You must be equally comfortable digging in to customer requirements as you are drilling into design with development teams and developing production ready learning models. You consistently bring strong, data-driven business and technical judgment to decisions. This is a greenfield with no "off-the-shelf algorithms" that can perform the job. We experiment a lot and it is a must to learn and be curios. You will be encouraged to see the big picture, be innovative, and positively impact millions of customersWe embrace the challenges of a fast paced market and evolving technologies, paving the way to universal availability of content. You will be encouraged to see the big picture, be innovative, and positively impact millions of customers. This is a young and evolving business where creativity and drive will have a lasting impact on the way video is enjoyed worldwide.Amazon is committed to a diverse and inclusive workplace. Amazon is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status. For individuals with disabilities who would like to request an accommodation, please visit https://www.amazon.jobs/en/disability/us.