Week 9 of the AI Mastery Bootcamp focuses on Neural Networks and Deep Learning Fundamentals, providing participants with a comprehensive introduction to the concepts that power modern artificial intelligence systems. This week’s lessons guide learners through the foundational principles of deep learning, starting from understanding artificial neural networks (ANNs) to building and training models using industry-standard frameworks like TensorFlow and PyTorch. By the end of the week, participants will have the skills to implement a fully functional neural network capable of solving real-world tasks such as image classification and data prediction. The week begins with an overview of deep learning, emphasizing how it differs from traditional machine learning. Learners explore artificial neural networks, understanding their structure, including layers, neurons, weights, and biases. Real-world applications in areas like computer vision, natural language processing, and healthcare are discussed to contextualize the theoretical knowledge. Participants set up their development environments and familiarize themselves with popular datasets such as MNIST and CIFAR-10, laying the groundwork for practical implementation. As the week progresses, participants delve into the mechanics of how information flows through a neural network using forward propagation. They learn about essential activation functions such as sigmoid, tanh, ReLU, and softmax, understanding when and where to use each for optimal performance. The training process is further explored with the introduction of loss functions, including Mean Squared Error and Cross-Entropy, which are crucial for evaluating model predictions. Learners implement these functions manually and visualize how changes in loss values affect model accuracy. Another critical component covered this week is backpropagation, paired with gradient descent optimization techniques. Participants explore different gradient descent methods, including stochastic, mini-batch, and full-batch variants. They also learn about advanced optimizers such as Adam, RMSprop, and Adagrad, emphasizing the importance of learning rate selection. Implementing these methods helps participants experience how model weights are updated during training to minimize prediction errors.

Goal: a simple web app where you can type a prompt, tweak generation settings (temp/top-p/top-k), and get model outputs. Optional upgrades: prompt templates, chat history, basic safety, and a tiny fine-tune.

Unlock your future in Generative AI with the NCA-GENL: NVIDIA-Certified Generative AI LLMs Specialization. This comprehensive course is designed to help you master the foundations of large language models (LLMs), prompt engineering, model alignment, and the powerful NVIDIA AI ecosystem—all while preparing you to pass the NCA-GENL certification exam with confidence. Whether you're an aspiring AI engineer, data scientist, product manager, or a tech-savvy learner eager to break into the world of transformer-based models, this course will guide you step-by-step. You'll learn the core principles of machine learning, neural networks, and self-attention mechanisms that power modern LLMs like GPT, BERT, and T5. We'll dive deep into fine-tuning strategies, including LoRA and PEFT, and help you master zero-shot, few-shot, and chain-of-thought prompting techniques to enhance model performance. Hands-on labs and real-world examples will walk you through using NVIDIA tools such as NeMo, Triton Inference Server, TensorRT, cuDF, and Base Command—tools that are essential for deploying and optimizing LLMs at scale. By the end of this course, you’ll not only be equipped with the technical knowledge to pass the NVIDIA-Certified Associate: Generative AI and LLMs (NCA-GENL) exam—you’ll also gain practical, job-ready skills to thrive in the fast-growing world of AI and LLM deployment. If you're looking for a clear path into AI certification, a career in LLM applications, or hands-on experience with NVIDIA generative AI tools, this course is your launchpad.

Artificial Intelligence has evolved rapidly over the past few years. What started as simple automation and predictive models has now entered a new phase — the age of AI agents. An AI agent is more than a chatbot or a smart assistant. It is a system designed to act independently, make decisions, use tools, and continuously learn from its environment. Unlike traditional AI systems that respond only when prompted, AI agents can: - Understand goals - Break them into steps - Execute tasks across tools - Adapt based on outcomes - Operate with minimal human input In short, AI agents don’t just assist — they operate. In 2025, AI agents began appearing across industries. Businesses used them for customer service, research, workflow automation, analytics, marketing, and even software development. Instead of hiring multiple tools or teams, organizations started deploying intelligent agents capable of handling end-to-end tasks. This shift also led to the rise of multi-agent systems, where multiple AI agents collaborate with each other — much like a human team. One agent gathers data, another analyzes it, another generates output, and another monitors quality. However, this power also comes with responsibility. As AI agents become more autonomous, questions around governance, transparency, control, and safety become critical. That’s why 2026 will focus heavily on: - Agent orchestration - AI governance frameworks - Responsible deployment - Human-in-the-loop systems The future of work will not be humans versus AI. It will be humans working alongside intelligent agents. Those who understand how to design, manage, and leverage AI agents will have a massive advantage — whether they’re developers, product leaders, entrepreneurs, or executives. At School of AI, we’re building a community and curriculum around exactly this future — helping people move from AI curiosity to AI mastery. Because AI isn’t coming someday, it’s already here.

Creating a machine learning model and uploading it to Hugging Face involves several key steps. First, you need to install the necessary libraries, which include transformers, datasets, and huggingface_hub. These tools are essential for model training, dataset management, and interacting with the Hugging Face platform. Once your environment is set up, you can either train a new model or fine-tune a pre-trained model. For instance, using a popular transformer model like BERT, you can tokenize your dataset, which prepares it for the model by truncating or padding text sequences to match the model's expected input size. Hugging Face’s datasets library simplifies the loading and tokenization of datasets, such as IMDB for sentiment analysis. After preparing your dataset and defining your training arguments, the next step is to initialize a pre-trained model, configure training parameters, and begin fine-tuning. During training, the model learns from your dataset, adjusting its weights accordingly. After training is complete, you save both the model and tokenizer locally, ensuring that everything is ready for upload to Hugging Face. Uploading your model involves creating a new repository on Hugging Face using the huggingface_hub library. You authenticate using your Hugging Face credentials and create a repository under your username. Once the repository is created, you clone it locally and push the saved model files using Git commands or the repository's push_to_hub() method. After a successful push, your model becomes available on the Hugging Face platform, where it can be shared, accessed, or further fine-tuned by other users. Hugging Face’s interface allows for easy version control, collaboration, and deployment of your machine learning models, making it a central hub for model sharing and experimentation.