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6 years of experience in building and managing AI Systems.
* I develop end-to-end AI systems from requirements analysis, and data gathering to deployment, implementing new methods/research papers, and turning projects into research outcomes. I have achieved substantial performance in DL/ML/RL models for CV and NLP domain problems.
* Implementing and deploying projects handling and maintaining scalability, research papers as well as POCs.
* Project planning, requirements gathering, and analysing requirements to define the system's architecture, and implementation timeline.
* Provide mentoring to junior developers for ML projects.
Website: [roshantanisha.github.io/?ref=codementor](http://roshantanisha.github.io/?ref=codementor)
Summary:
I like to work on technology that is smart, simple and sophisticated. This sums up the vast knowledge required to work on projects to excel in a working product. I like to train Deep Neural Networks and understand them well.
I have mentored many students for their AI careers, teaching them Machine Learning and Mathematics. I am a mentor for the RFS (Reach for the Stars) Programme by the Aga Khan Education Board for India. I am an alumnus of this program as well.
I have a cumulative experience of 6 years working in the product and service-based industry for creating Machine Learning projects.
I have done some innovative work that I am proud of and am continuing to do so. I try my best to contribute my expertise to the project I am working on.
Highly Experienced in Machine Learning, Deep Learning, Advanced Deep Learning, Artificial Intelligence, and Algorithms, including models in the production environment, and deploying ML models. Working with top Indian colleges like BITS, niche NLP and CV, real-estate startups, MNCs, and Fortune top 20 companies, working with sensitive anonymized datasets, and creating state-of-the-art models are some of my achievements. I have strong and correct knowledge of Deep Learning concepts from the above experiences.
Working in the IT sector for more than 10 years.
With over 10 -years of freelancing and professional experience taught me all kinds of frameworks and languages, ranging from React, Angular, Vue, Node js, Express js, React Native, Redux, Rx JS, JavaScript, Typescript, WordPress, PHP, MySQL, MongoDB, Firebase, Sqlite, Postgresql, ES5+, Python3, Machine Learning, Deep Learning, RNN, CNN, C/C#/C++, .Net, Assembly, VBA, VB, Excel Macro, Java, Spring Boot Micro-services, R, Shiny, STATA, MATLAB, Google Sheet, App Script, Bubble io, etc.
Over my long career, I have come across all kinds of challenges and gained vast experience with different kinds of industries like portals, medical industry, perception exercise, employee management, etc.
I have good experience with frontend development using React JS along with Typescript, Firebase, GraphQL, React Native, Native Script, React, Material UI, Ionic, Node.js, Web Sockets, and real-time communication.
I have also worked on containerization technologies like Kubernetes, Docker, AWS, GCP, Azure etc.
I am very good with backend technologies as well like Java Spring boot, microservices, MySQL, Postgres, Elasticsearch, AWS/Google integration.
Please contact me to get the result done in a professional way.
I help companies strategize, architect, and execute cloud products using deep learning solutions applied to their data. I have spoken at numerous conferences about systems, languages, and neural network architectures. My expertise lies in people, processes, and products for machine learning. I am currently based in Los Angeles, California (PST Timezone). Currently working on HPC infrastructure for training and deploying LLM and researching innovative methods to generate code using LLMs
I make the dumbest thing smart. I am passionate about solving problems with possible Machine Learning modelling. I am keen on learning new algorithms. I believe sharing knowledge will increase my understanding of the subject in hand.
Currently, I am working in Amazon, London on the personalization of Subscription page.
I was working with Zalando SE in the Pricing & Forecasting Team from May 2018-August 2019 and before that I worked as a Machine Learning Engineer at Zomato. My major areas of interests are Deep Learning and Natural Language Processing.
I completed undergrad from IIIT-Allahabad. Some of my notable projects in the Deep Learning includes Synthesizing Insights and actionable items from user opinions and reviews, Photo Classification tailored to Food search and Discovery platforms and EyeQ (Image Quality and Aesthetics determination).
I dream to pursue Artificial Intelligence as an independent researcher in future.
Find my content here at https://amitk.org
See the power of our Deep Learning Pipeline tutors through glowing user reviews that showcase their successful Deep Learning Pipeline learning journeys. Don't miss out on top-notch Deep Learning Pipeline training.
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Derek Smith / Nov 2024
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Pam / Nov 2024
K M Rakibul Islam
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“Anup is amazing! His business acumen and personality are both top-notch. I'm incredibly appreciative of the time he spent with me, appending queries and teaching me how to use Power Query. He's also super responsive and always on time, which I really appreciate.
As someone with dyslexia, I sometimes struggle with directional instructions, but Anup was very patient and understanding. I'm so grateful I found him and will definitely reach out for more training in the future.“
genevieve Perry / Nov 2024
Anup Mistry
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“Yuriy was super helpful in getting me up to speed on navigating a complex frontend open source codebase to which I wanted to contribute. He has a ton of experience and insight, and was able to drill down into the details quickly and get a rapid understanding of how things worked. Highly recommended!“
Matt / Nov 2024
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Frequently asked questions
How to learn Deep Learning Pipeline?
Learning Deep Learning Pipeline effectively takes a structured approach, whether you're starting as a beginner or aiming to improve your existing skills. Here are key steps to guide you through the learning process:
Understand the basics: Start with the fundamentals of Deep Learning Pipeline. You can find free courses and tutorials online that cater specifically to beginners. These resources make it easy for you to grasp the core concepts and basic syntax of Deep Learning Pipeline, laying a solid foundation for further growth.
Practice regularly: Hands-on practice is crucial. Work on small projects or coding exercises that challenge you to apply what you've learned. This practical experience strengthens your knowledge and builds your coding skills.
Seek expert guidance: Connect with experienced Deep Learning Pipeline tutors on Codementor for one-on-one mentorship. Our mentors offer personalized support, helping you troubleshoot problems, review your code, and navigate more complex topics as your skills develop.
Join online communities: Engage with other learners and professionals in Deep Learning Pipeline through forums and online communities. This engagement offers support, new learning resources, and insights into industry practices.
Build real-world projects: Apply your Deep Learning Pipeline skills to real-world projects. This could be anything from developing a simple app to contributing to open source projects. Using Deep Learning Pipeline in practical applications not only boosts your learning but also builds your portfolio, which is crucial for career advancement.
Stay updated: Since Deep Learning Pipeline is continually evolving, staying informed about the latest developments and advanced features is essential. Follow relevant blogs, subscribe to newsletters, and participate in workshops to keep your skills up-to-date and relevant.
How long does it take to learn Deep Learning Pipeline?
The time it takes to learn Deep Learning Pipeline depends greatly on several factors, including your prior experience, the complexity of the language or tech stack, and how much time you dedicate to learning. Here’s a general framework to help you set realistic expectations:
Beginner level: If you are starting from scratch, getting comfortable with the basics of Deep Learning Pipeline typically takes about 3 to 6 months. During this period, you'll learn the fundamental concepts and begin applying them in simple projects.
Intermediate level: Advancing to an intermediate level can take an additional 6 to 12 months. At this stage, you should be working on more complex projects and deepening your understanding of Deep Learning Pipeline’s more advanced features and best practices.
Advanced level: Achieving proficiency or an advanced level of skill in Deep Learning Pipeline generally requires at least 2 years of consistent practice and learning. This includes mastering sophisticated aspects of Deep Learning Pipeline, contributing to major projects, and possibly specializing in specific areas within Deep Learning Pipeline.
Continuous learning: Technology evolves rapidly, and ongoing learning is essential to maintain and improve your skills in Deep Learning Pipeline. Engaging with new developments, tools, and methodologies in Deep Learning Pipeline is a continuous process throughout your career.
Setting personal learning goals and maintaining a regular learning schedule are crucial. Consider leveraging resources like Codementor to access personalized mentorship and expert guidance, which can accelerate your learning process and help you tackle specific challenges more efficiently.
How much does it cost to find a Deep Learning Pipeline tutor on Codementor?
The cost of finding a Deep Learning Pipeline tutor on Codementor depends on several factors, including the tutor's experience level, the complexity of the topic, and the length of the mentoring session. Here is a breakdown to help you understand the pricing structure:
Tutor experience: Tutors with extensive experience or high demand skills in Deep Learning Pipeline typically charge higher rates. Conversely, emerging professionals might offer more affordable pricing.
Pro plans: Codementor also offers subscription plans that provide full access to all mentors and include features like automated mentor matching, which can be a cost-effective option for regular, ongoing support.
Project-based pricing: If you have a specific project, mentors may offer a flat rate for the complete task instead of an hourly charge. This range can vary widely depending on the project's scope and complexity.
To find the best rate, browse through our Deep Learning Pipeline tutors’ profiles on Codementor, where you can view their rates and read reviews from other learners. This will help you choose a tutor who fits your budget and learning needs.
What are the benefits of learning Deep Learning Pipeline with a dedicated tutor?
Learning Deep Learning Pipeline with a dedicated tutor from Codementor offers several significant benefits that can accelerate your understanding and proficiency:
Personalized learning: A dedicated tutor adapts the learning experience to your specific needs, skills, and goals. This personalization ensures that you are not just learning Deep Learning Pipeline, but exceling in a way that directly aligns with your objectives.
Immediate feedback and assistance: Unlike self-paced online courses, a dedicated tutor provides instant feedback on your code, concepts, and practices. This immediate response helps eliminate misunderstandings and sharpens your skills in real-time, making the learning process more efficient.
Motivation and accountability: Regular sessions with a tutor keep you motivated and accountable. Learning Deep Learning Pipeline can be challenging, and having a dedicated mentor ensures you stay on track and continue making progress towards your learning goals.
Access to expert insights: Dedicated tutors often bring years of experience and industry knowledge. They can provide insights into best practices, current trends, and professional advice that are invaluable for both learning and career development.
Career guidance: Tutors can also offer guidance on how to apply Deep Learning Pipeline in professional settings, assist in building a relevant portfolio, and advise on career opportunities, which is particularly beneficial if you plan to transition into a new role or industry.
By leveraging these benefits, you can significantly improve your competency in Deep Learning Pipeline in a structured, supportive, and effective environment.
How does personalized Deep Learning Pipeline mentoring differ from traditional classroom learning?
Personalized Deep Learning Pipeline mentoring through Codementor offers a unique and effective learning approach compared to traditional classroom learning, particularly in these key aspects:
Customized content: Personalized mentoring adapts the learning material and pace specifically to your needs and skill level. This means the sessions can focus on areas where you need the most help or interest, unlike classroom settings which follow a fixed curriculum for all students.
One-on-one attention: With personalized mentoring, you receive the undivided attention of the tutor. This allows for immediate feedback and detailed explanations, ensuring that no questions are left unanswered, and concepts are fully understood.
Flexible scheduling: Personalized mentoring is arranged around your schedule, providing the flexibility to learn at times that are most convenient for you. This is often not possible in traditional classroom settings, which operate on a fixed schedule.
Pace of learning: In personalized mentoring, the pace can be adjusted according to how quickly or slowly you grasp new concepts. This custom pacing can significantly enhance the learning experience, as opposed to a classroom environment where the pace is set and may not align with every student’s learning speed.
Practical, hands-on learning: Mentors can provide more practical, hands-on learning experiences tailored to real-world applications. This direct application of skills is often more limited in classroom settings due to the general nature of the curriculum and the number of students involved.
Personalized mentoring thus provides a more tailored, flexible, and intensive learning experience, making it ideal for those who seek a focused and practical approach to mastering Deep Learning Pipeline.