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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.
Greetings! I'm an **iOS developer** with a passion for crafting apps that blend simplicity with **cutting-edge ML technology**.
With **over a decade of experience**, I specialize in **Swift**, **SwiftUI**, and **UIKit**, creating scalable and modular solutions.
My recent focus is on automating tasks using **LLMs** and **Stable Diffusion**, enhancing dev experience and marketing through **next.js** and **API-served ML models**.
Expect meticulous planning and robust **advice on architecture** and **tech choices** when working with me.
I've transitioned from traditional roles to focus on creating tools that offer freedom and balance, aligning with my values of health, family, and adventure.
Let's create something revolutionary together.
I've poured my passion into these big projects:
- [Whisperboard](https://apps.apple.com/us/app/whisperboard/id1661442906) open-source voice transcription app
- [GetLost](https://apps.apple.com/us/app/getlost-explore-consciously/id1584133759) social network for travelers
- nu.nl - Biggest dutch news provider
- [Uber](https://uber.com) worked wallet experience in iOS client
`Python (Django)`, `PHP (Laravel, Wordpress), ReactJs (Javascript)`, `Flutter (Dart) and` `3X Certified Salesforce Consultant` `(Dart)` with 5+ years dedicated expertise in end-to-end application development. Proficient in harnessing Python frameworks including Django, Flask, Pandas, Tensorflow, and Microservices. Skilled in AWS, GCP, Heroku Cloud platforms, combined with frontend expertise in React.js. Well-versed with databases like MySQL, PostgreSQL, MySQL, and MongoDB. Utilizes robust communication and collaboration capabilities to ensure projects resonate with client goals efficiently and sustainably.
`**Key Strengths & Qualifications:**`
* **Full Stack Expertise:** Proficient in frontend and backend development, with a focus on creating seamless user experience and scalable server-side applications.
* **Backend Mastery:** Extensive experience in Python language programming, enabling the development of efficient and maintainable backend systems, Restful APIs, and microservices using Django, Flask, TensorFlow, LLAMA and Numpy.
* **Frontend Proficiency:** Skilled in modern frontend technologies, including JavaScript ES6, React, Angular and Vue, allowing for the creation of responsive and user-friendly interfaces.
* **Database Management:** Experienced in designing and optimizing databases on PostgreSQL & Oracle, to ensure data integrity and high-performance data retrieval.
* **Cloud Expertise:** Proficient in deploying applications on major cloud platforms such as AWS, Azure, and GCP, with knowledge of containerization (Docker, Kubernetes).
* **Agile Leadership and Version Control:** Good understanding of agile software development principles & techniques and proficiency in version control system like Git.
* **Security and Scalability:** Committed to building secure and scalable applications, implementing best practices for load balancing, fault tolerance, and system performance.
* **Code Quality:** Passionate about writing clean, maintainable code and conducting thorough code reviews to uphold quality standards.
* **Problem solving and Troubleshooting:** Strong analytical and problem-solving skills to identify and resolve technical issues efficiently.
* **Communication and Collaboration:** Effective communication and collaboration skills to work with cross-functional teams and stakeholders.
* **Continuous Learning and Adaptability:** Willingness to stay updated with the latest industry trends & technologies and comprehensive ability to quickly adapt to new challenges and technologies.
Embedded Systems Engineer and Certified Tensorflow Developer with a strong technical/coding background, including C, C++, Java, Python and Assembly. Additionally skilled back-end development based on Spring/Django Framework, and Machine Learning with Tensorflow and Pytorch.
I'm a dedicated full-time mentor, consultant and Lead software developer with a track record of over 1000+ sessions since 2016.
Having 8 years of programming experience in Python, java, GoLang, AWS, MongoDb, ElasticSearch, telethon and many more technologies.
I'm passionate about problem-solving and navigating intricate code bases.
I love working with:
⭐ Python
⭐ Java
⭐ Spring
⭐ NodeJS
⭐ AWS
⭐ SQL
⭐ MongoDB
⭐ ElasticSearch
⭐ React
⭐ GoLang
WHAT SEPARATES ME FROM OTHERS?
* Strong and clear communication
* Availability at all times
Achievements:
* 1000+ Sessions
I'm a senior programmer who has written lots of code in lots of programming languages and platforms and designed the technical aspects of things all the way from microcontroller based robots to a service oriented architecture handling web scale requests. I enjoy mentoring and look forward to working with you!
Note: If you're asking for help related to graded assignments, please note that I won't do the complete assignments for you. I'll be happy to help you learn and give pointers so you can do them yourself.
See the power of our Large Language Models tutors through glowing user reviews that showcase their successful Large Language Models learning journeys. Don't miss out on top-notch Large Language Models training.
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Amaechi johnkingsley
Large Language Models tutor
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Olamide Soyoye
Large Language Models tutor
“As always, my session with Isaac was excellent. He is so knowledgeable and straight to the point. We are coming to the end of our development of my first app, with his skills and experience, the app is looking great. I will definitely be continuing future work with him, developing my Power Platform skills.“
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Isaac Eduardo Krauss Moreno
Large Language Models tutor
“Amazing job. Had a complex installation to complete on a massive Angular application with many npm and other technical stuff to navigate and Opeoluwa was able to figure everything out and get my application running. I learned a lot and will continue to work with him.“
Jai Waterman / Nov 2024
Opeoluwa Lanre
Large Language Models tutor
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Frequently asked questions
How to learn Large Language Models?
Learning Large Language Models 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 Large Language Models. 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 Large Language Models, 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 Large Language Models 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 Large Language Models through forums and online communities. This engagement offers support, new learning resources, and insights into industry practices.
Build real-world projects: Apply your Large Language Models skills to real-world projects. This could be anything from developing a simple app to contributing to open source projects. Using Large Language Models in practical applications not only boosts your learning but also builds your portfolio, which is crucial for career advancement.
Stay updated: Since Large Language Models 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 Large Language Models?
The time it takes to learn Large Language Models 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 Large Language Models 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 Large Language Models’s more advanced features and best practices.
Advanced level: Achieving proficiency or an advanced level of skill in Large Language Models generally requires at least 2 years of consistent practice and learning. This includes mastering sophisticated aspects of Large Language Models, contributing to major projects, and possibly specializing in specific areas within Large Language Models.
Continuous learning: Technology evolves rapidly, and ongoing learning is essential to maintain and improve your skills in Large Language Models. Engaging with new developments, tools, and methodologies in Large Language Models 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 Large Language Models tutor on Codementor?
The cost of finding a Large Language Models 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 Large Language Models 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 Large Language Models 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 Large Language Models with a dedicated tutor?
Learning Large Language Models 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 Large Language Models, 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 Large Language Models 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 Large Language Models 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 Large Language Models in a structured, supportive, and effective environment.
How does personalized Large Language Models mentoring differ from traditional classroom learning?
Personalized Large Language Models 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 Large Language Models.