Master Generative AI with 10+ Real-world Projects in 2025!
Understand fine-tuning vs full training vs training from scratch in ML, with use cases, advantages, and practical code examples.
Compare PyTorch vs TensorFlow: two leading ML frameworks. Discover their features, advantages, syntax differences, and best use cases
Machine learning's full potential with hyperparameter optimization. Manual tuning, grid search, random search, Bayesian optimization & more.
Automate email sending using Python's smtplib: single/multiple recipients, attachments. Learn to streamline communication tasks efficiently.
Explore the significance of labeled data, particularly machine learning, its creation, applications, advantages, and limitations.
Kolmogorov-Arnold Networks (KAN) offer a promising alternative to Multilayer Perceptrons (MLP) it uses learnable activation functions & more.
Artificial Intelligence enhances smart cities by optimizing traffic, waste, energy, safety, healthcare, and more, making urban life efficient.
Advanced prompt engineering techniques with LangChain. Enhance language models with dynamic prompting, context-aware interactions & more.
Explore the difference between ANN vs CNN vs RNN neural networks. Learn how to choose the right network for your specific AI application.
Deploying LLMs in generative AI applications: challenges, strategies, and best practices for Moving Generative AI Applications to Production.
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