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Tanisha Bhayani
TensorFlow tutor
US$50.00 /15 min6 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.
- TensorFlow
- Python 3
- AI
- Machine learning
- Machine Learning Deployment
- Web Development
- Keras
- SQL
- Python
- Deep Learning
- TensorFlow.js
- Angular
- React
- Mobile App Development
- Web Scraping
- Scrapy
- TypeScript
- Responsive Web Development
- MongoDB
- PostgreSQL
- MySQL
- Computer Vision Engineering
- PyTorch
- Deep Learning Pipeline
- Deep Reinforcement Learning
- Scipy
- Matplotlib
- Pandas
- Python 2
- Cloud Functions
- AWS SageMaker
- Azure
- Google Cloud Platform
- GPU
- AWS
- Kubernetes
- Docker
- Machine Learning System Design
- Machine Learning Frameworks
- Machine Learning Research
- Machine Learning Models
- Open Source Software
- AI/ML
- AI Chatbot
- OpenAI APIs
- OpenCV
- OpenAI
- GPT-4
- GPT-3
- GPT
- Research
- LLM
- Large Language Models
- Data Visualization
- Data Engineering
- Data Science
- Artificial Neural Networks
- NumPy
- Flask
Gopal Chitalia
TensorFlow tutor
US$20.00 /15 minI am [Gopal Chitalia](https://scholar.google.com/citations?user=QncMQEIAAAAJ&hl=en), a graduate student at Purdue University and working with Prof. [Jan Anders Mansson](https://www.purdue.edu/engineering/mdlab/mansson/) on fault detection in motors using deep learning in a joint project with Wistron. Additionally, I'm working with Prof. [Junjie Qin](https://engineering.purdue.edu/people/junjie.qin.1) on transfer learning application using LLMs for optimizing power networks. I have had the privilege of working at [Center for Building Science Lab](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fcbs.iiit.ac.in%2F&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=UCjKsb98EbNq8jGvbf%2F5TEETyHMI1gnMx20lLBeQmog%3D&reserved=0) under the supervision of Prof. [Vishal Garg](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscholar.google.co.in%2Fcitations%3Fuser%3DvyH26MIAAAAJ%26hl%3Den&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=pDBa4RZtT0eg4rN%2FdvVtv3q%2F6goK1EEVT6%2F6UeGPcw4%3D&reserved=0). I have previously been a Research Assistant/Visiting ML Scholar at the [Smart Grid Research Unit](https://nam04.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.sgru.eng.chula.ac.th%2F&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=UAp49aqIF2oxM3WIO5IS68gTyMLeYsKR5ENaosJMhvk%3D&reserved=0) (SGRU) under [Manisa Pipattanasomporn](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fscholar.google.com.sg%2Fcitations%3Fuser%3D4W2KIQkAAAAJ%26hl%3Den&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=Pey86yYp3KpRPj9d9a97nzxLxQGMkQrN%2BFASqUqyheQ%3D&reserved=0) (Adjunct Faculty, Virginia Tech). In addition to my academic pursuits, I have also contributed to the industry in the domains of Energy Efficiency, IoT, and Machine Learning. I have worked at [ClevAir](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fclevair.io%2Fen%2F&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=nonCkudNp3cFUL2CNs6rMHm0HyQXSnh9lhoJ76s5xus%3D&reserved=0) (Norway) as a **Data Scientist** and Growthworks.ai (CA, USA) as an **ML Scientist/Energy Demand Expert**. My research work on predicting short/long-term energy prediction in office/residential buildings using machine learning is published in [Applied Energy](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fabs%2Fpii%2FS0306261920309223%3Fvia%253Dihub&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=V%2Bq5MVSeJ7deNpE%2F%2FEP1PYrsrEVWEH%2FwNK1yAG4CPlM%3D&reserved=0). The results improve the state-of-the-art results by 20-40%. I am also an active reviewer at Applied Energy. Furthermore, I have collaborated on a building-level dataset paper which has been published in [Nature Scientific Data](https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.nature.com%2Fsdata%2F&data=05%7C01%7Cgchitali%40purdue.edu%7C9ca93da4ba36440447b708db86ccdfe0%7C4130bd397c53419cb1e58758d6d63f21%7C0%7C0%7C638251986720958299%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=rtv6fVxtvuYsk5NAwwR8ujauC1AfX9cs32w4evIj%2BZU%3D&reserved=0). Previously, I have also worked with Prof. [Jyotirmay Mathur](https://scholar.google.co.in/citations?user=AmGPPL4AAAAJ&hl=en) at the [Centre for Energy & Environment](http://www.mnit.ac.in/dept_cree/index.php), MNIT Jaipur on Predicting time ahead heating/cooling energy demand HVAC systems. Moreover, as an independent research student, I also collaborated with Prof. [Praveen Paruchuri](https://scholar.google.com/citations?user=ILUqgKEAAAAJ&hl=en) of [Machine Learning Lab](https://mll.iiit.ac.in/), IIIT-H for Reinforcement Learning (RL) applications for controlling HVAC systems.
Kazi Sohan
TensorFlow tutor
US$20.00 /15 minI love to teach what I know. I think I can explain things easily with examples. I have been working as a Software Engineer for the last 5 years and have worked with some of the world's most reputed companies like **Open AI, Scale AI, Doloras Lab**, etc as a freelancer.
Tushar Gupta
TensorFlow tutor
US$15.00 /15 min
I have good knowledge of predictive analysis, machine learning methodologies, modeling and cluster analysis of large datasets. I am proficient in utilizing SQL in RDBMS concepts. Experience in project documentation (functional and technical), developers and assisting team with project reports and status reports. Highly motivated learner with great team working skills Strong interpersonal, leadership and customer service skills. I have published paper at BMVC 2017: https://arxiv.org/abs/1710.05158 Deep Learning Blogger: https://medium.com/@tushar20 Technical Skills: Tools: Lucene, Latex, Eclipse Languages: Java, C, C++, R, Python Machine Learning Libraries: TensorFlow, Scikit-Learn, Theano, Keras Relational Database Management Systems: MySQL Web framework: HTML, CSS, JavaScript
Grant Williams
TensorFlow tutor
US$11.00 /15 minI am in the 5th year of my PhD at Georgia Institute of Technology (with a Masters in CS). I've published in top-tier computer science/cybersecurity conferences (including USENIX, ACM CCS, and ACM IMC). My research/expertise is primarily in Network Security, Network Security, and ML Applications to Security. I’m interested in applying my extensive Cybersecurity and ML knowledge to freelance projects, and am enthusiastic about applying my programming expertise to the problems clients are facing!
Martijn Courteaux
TensorFlow tutor
US$20.00 /15 min🧔️🎙🎤 I use a high-quality microphone, with fast-internet connection. 🌍🏎️ 👨🏫 Ex-University teacher: taught math, systems and signals, multimedia techniques, with programming in Python and C++. Always tries to teach something new instead of showing how it's done. Understanding of the student is key. 🧑🎓 Quit pursuing a PhD (after 5 years) at Ghent University, where I taught some courses (Systems and Signals, and Multimedia Techniques). Lots of work done on machine learning (Python/TensorFlow), and C++. Personal interest is on software performance and code quality. 🖥️ Currently also working on an application to replace for Lightroom, called SilverNode. Worked on a video game in C++ with SDL2 and OpenGL as a hobby project.
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See the power of our TensorFlow tutors through glowing user reviews that showcase their successful TensorFlow learning journeys. Don't miss out on top-notch TensorFlow training.
“Very knowledgeable, definitely someone to go to for learning purposes. Great guy, definitely will be my go-to mentor for anything machine learning, deep learning and python!“
John Cunningham / Nov 2024
Daniel Al Mouiee
TensorFlow tutor
“Brian was able to debug our code well. He was very helpful even before the session started and great to work with!“
Neeraja Kulkarni / Sep 2024
Brian Young
TensorFlow tutor
“Helped me understand that Reinforcement Learning is a technique of last resort when there is no exploitable or evident structure to the problem.“
Michael Osofsky / Aug 2023
Chris Potempa
TensorFlow tutor
“Sandeep has helped in code debugging and for sure I will come back to him. He is fast and quick to understand the problem. I appreciate your help.“
Abdo / Jul 2023
Sandeep Solanki
TensorFlow tutor
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Arrange regular session times with TensorFlow tutors for one-on-one instruction.
Frequently asked questions
How to learn TensorFlow?
Learning TensorFlow 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 TensorFlow. 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 TensorFlow, 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 TensorFlow 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 TensorFlow through forums and online communities. This engagement offers support, new learning resources, and insights into industry practices.
- Build real-world projects: Apply your TensorFlow skills to real-world projects. This could be anything from developing a simple app to contributing to open source projects. Using TensorFlow in practical applications not only boosts your learning but also builds your portfolio, which is crucial for career advancement.
- Stay updated: Since TensorFlow 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 TensorFlow?
The time it takes to learn TensorFlow 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 TensorFlow 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 TensorFlow’s more advanced features and best practices.
- Advanced level: Achieving proficiency or an advanced level of skill in TensorFlow generally requires at least 2 years of consistent practice and learning. This includes mastering sophisticated aspects of TensorFlow, contributing to major projects, and possibly specializing in specific areas within TensorFlow.
- Continuous learning: Technology evolves rapidly, and ongoing learning is essential to maintain and improve your skills in TensorFlow. Engaging with new developments, tools, and methodologies in TensorFlow 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 TensorFlow tutor on Codementor?
The cost of finding a TensorFlow 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 TensorFlow 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 TensorFlow 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 TensorFlow with a dedicated tutor?
Learning TensorFlow 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 TensorFlow, 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 TensorFlow 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 TensorFlow 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 TensorFlow in a structured, supportive, and effective environment.
How does personalized TensorFlow mentoring differ from traditional classroom learning?
Personalized TensorFlow 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 TensorFlow.
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