Personal ML/DL assistant
This Chainlit app was created following instructions from this repository!
Welcome to ML/DL Technical and Code Helper: Your Personal AI Assistant
Are you struggling with Machine Learning and Deep Learning concepts? Do you need code snippets to solve complex problems? ML/DL Code Helper is here to assist you!
What can ML/DL Technical and Code Helper do for you?
- Answer Technical Questions: From foundational theories to advanced algorithms, get clear explanations tailored to your level of expertise.
- Provide Code Examples: Need to implement an algorithm or fix a bug? Get code samples in Python, R, and more.
- Offer Learning Resources: Whether you're a beginner or advanced user, discover curated resources to further your understanding.
- Guide Through Projects: Get guidance on ML/DL projects, including design, data preprocessing, model selection, and evaluation.
Ready to dive in? Here are some commands to get you started:
explain <neural networks>: A user looking for an understanding of neural networks might use this command. The chatbot could respond with an overview of neural networks, discussing how they are computational models inspired by the human brain that are used to recognize patterns and solve complex problems in machine learning.code <convolutional neural network>: If a user wants to see how to implement a convolutional neural network in Python using a library like TensorFlow or PyTorch, this command could trigger the chatbot to provide a basic example of the code necessary to create a CNN for image classification.<resource gradient descent>: When a user needs educational resources to learn about gradient descent, this command might lead the chatbot to offer links to tutorials, video lectures, or articles that explain gradient descent, which is an optimization algorithm used to minimize a function by iteratively moving in the direction of steepest descent.<project sentiment analysis>: If a user is starting a project on sentiment analysis, this command could prompt the chatbot to discuss the steps involved in creating a sentiment analysis model, such as data collection, preprocessing, model selection, training, and evaluation, and potentially offer advice on best practices or methodologies to consider.
Let's start learning and coding together!