- Bachelor’s Degree in Computer Science or related field.
- Equivalent experience to a Bachelor’s degree based on 3 years of work experience for every 1 year of education
- 5+ years professional experience in software development.
- Experience with modern programming languages (Java, C#, Python) and open-source technologies.
Are you intrigued or passionate about Machine Learning (ML) and ready to build a new customer-centric product? The Amazon SageMaker team is looking for talented front-end engineers to help us build the next generation of ML tools.
With SageMaker, developers and data scientists have the ability to build, train, and deploy machine learning models quickly. As a fully-managed cloud service, SageMaker covers the entire data science workflow from data preparation and exploratory data analysis to model building and inference; our charter is to make data science and machine learning understandable, affordable, scalable, and accessible to everyone. The foundation of the SageMaker user experience is the industry standard, open-source Jupyter Notebook.
As a front-end engineering developer on the SageMaker team, you’ll be responsible for driving key deliverables within a new team building out interactive ML applications.
- Work closely with UX designers and product managers to develop friendly UI experiences.
- Work closely with other engineers to architect and develop the best technical design.
- Develop/maintain operational rigor for the frontend of a fast-growing AWS service.
- Own and deliver key features within a new interactive ML application
- Help develop the engineers of an existing “two pizza” scrum team.
- Collaborate with other SageMaker SDEs for features that cut across SageMaker.
- Engage with customers and other AWS partners.
- Help with hiring.
You’ll be well supported with by a group with deep technical chops, including multiple senior and principal engineers and scientists.
- Experience building tools for data scientists or developers.
- Attuned design sense so can collaborate with UX designers and hold a high bar with “backend” SDE’s.
- Experience with with CI/CD in a frontend context.
- Experience establishing and leveraging web analytics.
- Machine learning knowledge and experience.
- Knowledge of professional software engineering practices & best practices for the full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations.
- Ability to take a project from scoping requirements through actual launch of the project.
- Experience in communicating with users, other technical teams, and management to collect requirements, describe software product features, and technical designs.
- Deep hands-on technical expertise in full-stack development.
What’s behind the machine learning hype? In this non-technical course, you’ll learn everything you’ve been too afraid to ask about machine learning. There’s no coding required. Hands-on exercises will help you get past the jargon and learn how this exciting technology powers everything from self-driving cars to your personal Amazon shopping suggestions. How does machine learning work, when can you use it, and what is the difference between AI and machine learning? They’re all covered. Gain skills in this hugely in-demand and influential field, and discover why machine learning is for everyone!
This course will introduce the key elements of machine learning to the business leaders. We will focus on the key insights and base practices how to structure business questions as modeling projects with the machine learning teams. You will understand the different types of models, what kind of business questions they help answer, or what kind of opportunities they can uncover, also learn to identify situations where machine learning should NOT be applied, which is equally important. You will understand the difference between inference and prediction, predicting probability and amounts, and how using unsupervised learning can help build meaningful customer segmentation strategy.
Edureka’s Deep Learning in TensorFlow with Python Certification Training is curated by industry professionals as per the industry requirements & demands. You will master the concepts such as SoftMax function, Autoencoder Neural Networks, Restricted Boltzmann Machine (RBM) and work with libraries like Keras & TFLearn. The course has been specially curated by industry experts with real-time case studies.
Hands-On Mathematics for Deep Learning: Build a solid mathematical foundation for training efficient deep neural networks
A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures
Understand linear algebra, calculus, gradient algorithms, and other concepts essential for training deep neural networks
Learn the mathematical concepts required to understand how deep learning models function
Use deep learning for problems related to vision, image, text, and sequence applications
Time Series is an exciting and important part of Data Analysis. Time Series Data is more readily available than most forms of data and answers questions that cross-sectional data struggle to do. It also has more real world application in the prediction of future events. However it is not generally found in a traditional data science toolkit. There is also limited centralized resources on the applications of Time Series, especially using traditional programming languages such as Python.
The AI-Powered Enterprise: Harness the Power of Ontologies to Make Your Business Smarter, Faster, and More Profitable
Learn how to develop and employ an ontology, the secret weapon for successfully using artificial intelligence to create a powerful competitive advantage in your business.
The AI-Powered Enterprise examines two fundamental questions: First, how will the future be different as a result of artificial intelligence? And second, what must companies do to stake their claim on that future?
Deep learning is revolutionizing how machine translation systems are built today. This book introduces the challenge of machine translation and evaluation – including historical, linguistic, and applied context — then develops the core deep learning methods used for natural language applications. Code examples in Python give readers a hands-on blueprint for understanding and implementing their own machine translation systems. The book also provides extensive coverage of machine learning tricks, issues involved in handling various forms of data, model enhancements, and current challenges and methods for analysis and visualization. Summaries of the current research in the field make this a state-of-the-art textbook for undergraduate and graduate classes, as well as an essential reference for researchers and developers interested in other applications of neural methods in the broader field of human language processing.
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book’s web site.
Hands-On One-shot Learning with Python: Learn to implement fast and accurate deep learning models with fewer training samples using PyTorch
Get to grips with building powerful deep learning models using PyTorch and scikit-learn
Learn how you can speed up the deep learning process with one-shot learning
Use Python and PyTorch to build state-of-the-art one-shot learning models
Explore architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learning
Cybernetics, Cognition and Machine Learning Applications: Proceedings of ICCCMLA 2019 (Algorithms for Intelligent Systems)
This book provides a collection of selected papers presented at the International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA 2019), which was held in Goa, India, on 16–17 August 2019. It covers the latest research trends and advances in the areas of data science, artificial intelligence, neural networks, cognitive science and machine learning applications, cyber-physical systems, and cybernetics.
Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter: Build scalable real-world projects to implement end-to-end neural networks on Android and iOS
Learn how to deploy effective deep learning solutions on cross-platform applications built using TensorFlow Lite, ML Kit, and Flutter
Work through projects covering mobile vision, style transfer, speech processing, and multimedia processing
Cover interesting deep learning solutions for mobile
Build your confidence in training models, performance tuning, memory optimization, and deploying neural networks through every project
Machine learning is going to be something that you are going to use so that you can discover why and how you are getting the outcomes that you are getting with the program that you are using.
With machine learning, you are going to have the option of putting the data in that you want into the program and getting the results that you want to get. You are going to better understand where you made a mistake so that you can go back in and fix it.