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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.
Zuber is always looking to explore new technology stacks and frameworks.
He is a C.T.O level experienced developer.His ability to understand complex business logics has helped many clients all over the globe to build scalable businesses.
He loves to talk about new cuisines, new travel destination and books.
Seasoned engineering leader with 23 years of expertise in mobile app development and generative AI. Demonstrated success in designing and developing over 100 apps across platforms such as Android, iOS, and Windows Mobile. Skilled in building, mentoring, and leading high-performing engineering teams. Holder of a filed patent and have successfully published 50+ apps/games on Google Play Store and Apple App Store.
**Key Achievements**
\- **Innovation:** Developed and launched over 100 apps across multiple platforms, with more than 50 apps published on major app stores.
\- **Leadership:** Built and led engineering teams to deliver high-impact projects, resulting in improved efficiency and performance.
\- **Scalability:** Successfully managed services and deployments for products used by ~500 millions of users globally.
**Technical Skills:**
**AI/ML:**
\- Generative AI, Stable Diffusion
\- LLama - Large Language Models
\- Mobile AI/ML (ML Kit)
**Mobile App Development:**
\- Native (Android, iOS)
\- Hybrid (React Native, Flutter)
**Backend Development:**
\- Python, Java, REST API
**Web Development:**
\- HTML, CSS, JavaScript
**Cloud Services:**
\- Amazon Web Services (AWS), Google Cloud Platform (GCP)
**Other Technologies:**
\- IoT, ELK Stack, Generative AI
As a Generative AI Engineer, I am responsible for developing, designing, and maintaining cutting-edge AI-based systems to ensure smooth and engaging user experiences.
My role involves creating and developing generative models that have the ability to generate new content, such as images, text, and audio, based on patterns. I work across client teams to develop and architect Generative AI solutions using machine learning and other AI technologies.
Additionally, I participate in activities which includes refining and optimizing prompts to improve the outcome of Large Language Models (LLMs). I also evaluate and select appropriate AI tools and machine learning models for tasks, as well as build and train working versions of those models using Python and other open-source technologies.
I'm an AI Engineer who loves building AI solutions (custom coded).
I love teaching about AI and its impact.
My experience:
- 7+ years in Software Development
- 3+ years in AI, Machine Learning
- 12+ months in Applied AI with Large Language Models
### Skills:
- RAG
- LLMs
- AI Agents
- AI Chatbots
- Vector Databases
### Technologies:
- **Python**: LangChain, OpenAI API, Flask, HuggingFace, scikit-learn, pandas, numpy
- **Language Models**: GPT-4, Claude 3, Llama 3 (and other open-source), Gemini
- **Vector Databases**: Pinecone, Qdrant
- **JavaScript**: React, Node
**ABOUT ME**
As a seasoned AI/ML engineer with over 9 years of dedicated experience, I specialize in the entire spectrum of AI SaaS product development. My recent focus lies in the advancement of Generative AI and Large Language Models, leveraging from vector databases to optimize content accuracy for superior user engagement.
Over the course of my career, I have engaged with a diverse array of companies, navigating complex challenges and delivering tangible results through the completion of 80֡ innovative projects. My portfolio encompasses a breadth of industries including Retail, Media, Sports, Security, and Health Care, showcasing a consistent ability to translate visionary concepts into impactful, market leading solutions.
**ACHIEVEMENTS**
* Ranked in the Top 1% and handpicked to be featured at Upwork's exclusive enterprise event.
* Played a part in the journey to industry acclaim, earning trust from top brands including P&G, Nvidia, and AT&S. Featured at Adobe, HubSpot, and beyond!
* Achieved top honors with both "Best Student" and "Best Final Year Project" awards from the University of Bradford, UK, for excelling in my degree and capstone project.
**EXPERTISE:**
* **Scripting & Languages:** Python, C, C++, Matlab, R, Bash
* **Tools:** OpenCV, OpenVino, Numpy, Pandas, Spacy, Gensim, Transformers, NLTK, CoreNLP, Other
* **Deep Learning frameworks:** Torch / PyTorch, Tensorflow, CUDA/CuDNN GPU Accelerations
* **LLMs Platforms & Frameworks:** LangChain, OpenAI, Anthropic, Cohere and more
* **Vector Databases:** Pinecone, Chroma, Milvus, Superbase, PgVector
* **Restful APIs:** with Flask, Fast, Nginx, Gunicorn, Locust
* **Cloud Platforms:** Google Cloud (Compute, GCS), Paperspace, AWS (EC2, S3), Azure
* **Deploy models at scale:** optimize your cloud cost with Replicate, Salad, Modal.com
* **Hardware:** CPU, GPU/Multi-GPU, TPU, Raspberry PI 4, Coral Dev Board, Nvidia Jetsen TX2 & others
* **Edge Accelerators:** Coral Accelerator, Intel Movidius Neural Stick
* **OS:** Linux, Windows, MacOS; Containers: Kubernetes, Docker
**CERTIFICATIONS**
* Generative AI with Large Language Models - DeepLearning.AI & AWS
* Machine Learning – Stanford University
* Data Science – R Programming
See the power of our Ai large language models tutors through glowing user reviews that showcase their successful Ai large language models learning journeys. Don't miss out on top-notch Ai large language models training.
“I recently passed my Google interview and I'm now in team matching! I'm still in shock, to be honest, since I heard back last week. This wouldn't have been possible without Brian - thank you!!
I worked with Brian for nearly 1.5 months, and we dedicated several hours to interview prep 4-6 days a week, for anywhere between 3-6 hours a day. I know it may seem excessive, but it was absolutely necessary.
In the past, I had done poorly in interviews and often wouldn't even make it past the initial online coding assessments. I failed many times and only made it to two final round interviews after completing dozens of initial coding assessments. Brian worked with me on a long-term basis, and after a few weeks, I could see my confidence improve. I became faster with coding, finished the Blind 75, and nearly finished Neetcode 150.
Brian was tough on me, but it was necessary. He taught me the importance of constantly communicating with interviewers while working through solutions. He also gave me a solid framework to help break down challenging questions in a way that made them easier to solve. I heard back within 3 days of my interview that they were moving me to "Hire" for L3.
This wouldn't have been possible without Brian. He's an amazing coach and tutor. Thanks!“
Rowan O / Nov 2024
Brian Young
Ai large language models tutor
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Jason Davey
Ai large language models tutor
“Ben immediately saw major flaws in my code-design and prevented me from shooting myself in my foot. In addition, he gave me examples to help get my code in a much better paradigm. I eagerly look forward to our next sesssion :)“
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Ai large language models tutor
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Warith Omoyele
Ai large language models tutor
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Frequently asked questions
How to learn Ai large language models?
Learning Ai 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 Ai 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 Ai 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 Ai 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 Ai 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 Ai large language models skills to real-world projects. This could be anything from developing a simple app to contributing to open source projects. Using Ai 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 Ai 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 Ai large language models?
The time it takes to learn Ai 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 Ai 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 Ai large language models’s more advanced features and best practices.
Advanced level: Achieving proficiency or an advanced level of skill in Ai large language models generally requires at least 2 years of consistent practice and learning. This includes mastering sophisticated aspects of Ai large language models, contributing to major projects, and possibly specializing in specific areas within Ai large language models.
Continuous learning: Technology evolves rapidly, and ongoing learning is essential to maintain and improve your skills in Ai large language models. Engaging with new developments, tools, and methodologies in Ai 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 Ai large language models tutor on Codementor?
The cost of finding a Ai 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 Ai 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 Ai 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 Ai large language models with a dedicated tutor?
Learning Ai 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 Ai large language models, but exceling in a way that directly aligns with your objectives.
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By leveraging these benefits, you can significantly improve your competency in Ai large language models in a structured, supportive, and effective environment.
How does personalized Ai large language models mentoring differ from traditional classroom learning?
Personalized Ai 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 Ai large language models.