How can you use a variety of projects to build your deep learning portfolio?

In the field of deep learning, professionals should aim to be either niche experts or versatile generalists, avoiding intermediate positions. For instance, in sectors like banking, healthcare, or technology, one can specialize in specific deep learning projects, building a niche expertise. On the other hand, consulting offers a pathway for AI professionals seeking a diverse portfolio, enabling them to gain experience across various industries and develop a broad understanding of deep learning applications.

I believe that focusing on a single, well-defined project can lead to greater progress and impact than scattering our efforts across a variety of projects. By concentrating our efforts on one important task, we can allocate sufficient time and energy to fully explore its possibilities and create a truly effective solution.

If you're looking to get into LLMs, I think an easy intro involves sentiment analysis. If you can find even a sample dataset (like from Kaggle) that has lots of open-ended responses, you can run it through OpenAI's API to get not just the sentiment of each response, but top topics as well. It's a well-defined introductory project that is a great intro to the LLMs and also prompt engineering.

Creating a diverse range of projects in many fields is essential to developing an extensive deep learning portfolio. Start with basic tasks like employing neural networks for sentiment analysis or image categorization. Advance to increasingly difficult assignments like reinforcement learning, generative models, or natural language processing. Examine applications in a variety of industries, such as finance, healthcare, or autonomous systems. Display your adaptability by taking on practical problems like as financial fraud detection or medical picture segmentation. To demonstrate your proficiency with a variety of deep learning applications, work on open-source projects, make contributions to sites eg GitHub, and document your results.

Craft Your AI Journey with a Stellar Deep Learning Portfolio! ?? - I'd select a mix of projects showcasing various skills, from essential neural networks to advanced deep learning models. - Emphasize problem-solving by creating unique solutions and experimenting with different models and datasets. - Document each project's process and results, showing your methodology and learning curve. - Share projects on GitHub, LinkedIn, or a personal blog, demonstrating your ability to communicate complex ideas effectively. - Actively seek and incorporate feedback from peers or mentors to enhance your projects and understanding. Pro tip: Consistently update your portfolio with new projects and learnings to reflect your growing expertise.

Here are a few things you can do: 1. Build a strong foundation in basics of deep learning like ANN, CNN, RNN, gradient descent, optimization etc. 2. Work on basic implementation projects. And then move to kaggle competition or other DL hackathon. 3. Participate in research work along with your faculty, or take up some work in deep learning from any institution etc. 4. For interview purposes, have a clear understanding of back propagation, dropout, normalisation etc. Hope it helps.

Best practices and standards are there for a reason. That's right and it applies to all domains. Utilize Git for version control, and enable style checking in your code editor. Utilize dedicated frameworks like 'black' in Python to evaluate and reformat code according to a consistent coding standard. This will take you a long way in building projects that are meaningful and easy to collaborate on.

If you are really looking for a career in AI, then you should follow best practices even when you are implementing a small project. While coding you should try to follow test driven development, use vs-code which provides extensive extension ecosystem, has a active community support, use version control tools like git, provide Readme for your project.

Following best practices in project development is crucial: Code Quality: Write clean, modular, and well-commented code adhering to PEP 8 style guide for Python. Version Control: Utilize Git or GitHub for tracking and managing code changes. Documentation: Create clear, concise README files detailing each project's purpose, requirements, and setup instructions. Interactive Presentation: Employ Jupyter or Colab notebooks to showcase code, data, results, and analysis interactively. Adhering to these standards ensures your projects are professional, maintainable, and accessible to others in the field.

Adhering to best practices goes beyond code quality but more about demonstrating professionalism and the ability to collaborate in the deep learning field. Clean, modular code following PEP 8 or similar standards speaks to one’s meticulousness and respect for the craft. Using version control like Git reflects an understanding of the collaborative and iterative nature of modern coding workflows. Clear READMEs and interactive Jupyter notebooks indicate a commitment to transparency and the ability to communicate complex ideas effectively. These practices are not mere formalities; they are indicative of a deep learning practitioner's readiness to contribute to and lead in the industry.

There's some debate in this article on whether to create diverse projects or focus on one big one. You can do both and that's useful especially in intrapreneurship/consulting roles where you start by pitching and building one small solution, and add more and more extensions as you build trust and credibility. One example of two separate products that can be unified into one large end-to-end product: a keyword forecasting tool to predict when a keyword will start spiking and a generative AI copywriting tool that'll update the company blog with the new keyword-based copy on the optimal day as determined by the forecasting tool. (Many other automation but not algorithmic tools like automated keyword bidding can be integrated into this suite.)

Innovation is required. Deep Learning is not a new field. Much has been explored and pretrained models are available for nearly all standard use-cases. As a Deep Learning expert, it is your job to understand the unique aspects of each project and dataset. This will enable you to come up with a training strategy which takes into account flexibility, compute and complexity. Be agile and try out various models. And once you lock on to a particular architecture: it's time for hyperparameter tuning!

To demonstrate problem-solving and creativity: Original Solutions: Avoid copying code; understand and apply concepts to new problems. Experimentation: Test various parameters, models, and architectures. Compare their performance. Innovation: Strive for unique ideas that improve or add value to the field. Showcasing your ability to innovate and solve problems creatively will highlight your deep understanding and adaptability in AI.

In my experience, showcasing problem-solving and creativity is not just about solving problems but about embracing challenges as opportunities for growth. While working on projects, I've learned the importance of delving into the core concepts rather than relying on ready-made solutions. Experimentation with different approaches and a willingness to explore uncharted territories foster both problem-solving skills and creativity. Building a deep learning portfolio isn't just about demonstrating technical competence; it's about illustrating a mindset of continuous curiosity, adaptability, and a drive to push the boundaries of what's possible in artificial intelligence.

It is so much required for implementation of any AI project. When you are making a project, its important to understand it thoroughly so that you can use your skills to debug as well as make your project more innovative. Try to use available resources and come up with an improvement to the existing ones.

Feedback is key. Recently I developed a fine-tuned ROBERTA for Ayurvedic QnA. It was a full package: manually curated dataset, custom embeddings and vocabulary. I uploaded it to HuggingFace and within days I had 10s of downloads! Moral of the story is that it is important to share your work. Great if you can present it as part of a research paper. If not, put it on GitHub or HuggingFace and let the community try it out!

If you have made a project which can beat state of the art or has an innovation, you should share it across AI community. You can share it on github so that others can provide feedback as well as use it in their projects if needed. You can present in conferences as well.

Participation in AI-related forums or discussions on Reddit or Discord can yield diverse perspectives that may challenge and refine your approaches. Involuntary exposure to critiques is as valuable as the recognition, fostering a collaborative ethos and driving continuous improvement. This practice is a testament to a practitioner's commitment to learning and contributing to the collective knowledge of the AI community.

Sharing projects and seeking feedback is vital: Platforms: Use GitHub, Kaggle, Medium, LinkedIn, YouTube, or a personal blog to showcase your work. Online Communities: Join AI and deep learning forums like Reddit, Stack Overflow, Quora, or Discord. Engagement: Participate in discussions, challenges, and competitions. This approach enhances your communication skills, provides valuable insights, and connects you with the AI community for continuous learning and improvement.

Sharing your work with others and asking for feedback—such as showing a drawing to friends—are crucial for building a deep learning portfolio.

I encourage you to build these: - A portfolio (to showcase skills, projects, and your resume) - A blog (to share learnings and teach others how to learn AI/ML too) - A robust LinkedIn profile (It's a must in this day and age) Now update all these by learning what's trending and adding them to your portfolio. Don't worry about speed, take your time to learn them, have fun, develop good fundamentals and demonstrate them in your portfolio.

As I always mention, AI is evolving very fast. It might be possible that a new algorithm will replace the existing ones next year or may be early. So, it is very important to keep yourself updated using blogs, companies websites etc

Continuously advance your deep learning journey by fostering curiosity and embracing a perpetual learning mindset. Stay abreast of emerging frameworks, tools, algorithms, and AI applications, integrating newfound knowledge into your projects. Regularly track the latest trends and research developments, leveraging them to enhance existing projects or embark on new endeavors. Ensure the relevancy of your portfolio by routinely reviewing and updating its content eliminating outdated or irrelevant projects while incorporating new and improved ones.

Continuously updating your skills and portfolio is essential: Stay Curious: Always explore new frameworks, tools, algorithms, and AI applications. Follow Trends: Keep abreast of the latest developments and research in AI and deep learning. Update Portfolio: Regularly revise your portfolio, removing outdated projects and adding new or improved ones. This approach ensures you remain at the forefront of the field, showcasing your growth and adaptability in deep learning.

Like cultivating a garden, creating a deep learning portfolio requires constant learning and updating. Enhance previous projects and add new ones to demonstrate your skill development. My experience has shown that updating and learning new things on a regular basis keeps your portfolio current and highlights your growing area of expertise.

To build a diverse deep learning portfolio, adopt a structured approach focusing on real-world problems, as advised by neptune.ai and towardsai.net. Essential steps include defining clear objectives, meticulous data collection and cleaning, and iterative model training and refinement. Techniques like regularisation, pruning, and hyperparameter fine-tuning are crucial to control overfitting. Use appropriate tools, such as sentence-transformers for clustering and Streamlit for front-end development. Document your projects well on platforms like GitHub for accessibility. This method ensures a portfolio that showcases a broad range of deep learning capabilities effectively.

Take a project of your interest, get your hands dirty by building the 'Baseline' version of it first. By now, we will get to know the scope of this project and figure out ways to improve. Documenting our projects from the moment we start it, will prove extremely helpful while we are making progress. Version control systems like git is useful in this case to keep track of changes. Once the baseline is set, half the project is considered done. Once we see progress, it gives us hope to continue and complete it.

One of the specific challenges with deep learning portfolios though is to find datasets, debugging those complex models, and ensuring code quality which makes a portfolio of this kind much more difficult to make.

Diverse Problem Solving - Tackle a range of problems from different domains. For instance, I once worked on a facial recognition project and later on a natural language processing task. This variety not only showcased my versatility in deep learning but also helped me understand its applications in diverse fields. Progressive Complexity - Start with simple projects and gradually increase complexity. My journey began with basic image classification tasks, progressing to more complex sequence modelling. This approach demonstrates a clear growth trajectory in your skills and a deepening understanding of deep learning principles.

Creating diverse deep learning portfolio extends beyond technical skill. Integrate projects that intersect with science, technology, and societal challenges. Develop applications in healthcare, leveraging AI for medical image analysis or predicting disease outcomes. Explore technology-driven solutions for environmental monitoring or climate modeling, showcasing the intersection of deep learning and scientific inquiry. Address social issues by creating projects that aid in sentiment analysis for social media, language translation, or accessibility for differently-abled individuals. By blending technology with scientific and social contexts, your portfolio becomes a testament to the multifaceted impact of deep learning across various domains