NVIDIA Free Online Courses on AI

NVIDIA has released a series of free online courses covering various aspects of AI, from foundational concepts to advanced applications. Below are the key courses along with essential details and practical implementations.

1. Generative AI Explained

🔗 Course Link

What You’ll Learn:

• Definition and workings of Generative AI
• Applications across industries
• Challenges and opportunities

You Should Know:

• Try generating text with OpenAI’s GPT-3:

  import openai
  response = openai.Completion.create(engine="text-davinci-003", prompt="Explain Generative AI in simple terms.")
  print(response.choices[0].text)
  

• Use Stable Diffusion for image generation:

  git clone https://github.com/CompVis/stable-diffusion
  python scripts/txt2img.py --prompt "A futuristic AI city"
  

1. AI for All: From Basics to GenAI Practice
   🔗 Course Link

What You’ll Learn:

• AI’s impact on healthcare, autonomous vehicles, and more
• Transition from ML to Generative AI
• Creating music, images, and videos with AI

You Should Know:

• Run a simple AI classification model using Scikit-learn:

  from sklearn.datasets import load_iris
  from sklearn.ensemble import RandomForestClassifier
  iris = load_iris()
  model = RandomForestClassifier().fit(iris.data, iris.target)
  print(model.predict([[5.1, 3.5, 1.4, 0.2]]))
  

3. Getting Started with AI on Jetson Nano

🔗 Course Link

What You’ll Learn:

• Setting up Jetson Nano and camera
• Data collection and annotation
• Neural network training

You Should Know:

• Install JetPack SDK:

  sudo apt-get install nvidia-jetpack
  

• Capture images using the camera:

  nvarguscamerasrc ! nvoverlaysink
  

4. Building A Brain in 10 Minutes

🔗 Course Link

What You’ll Learn:

• Neural network learning mechanisms
• Mathematical foundations of neurons

You Should Know:

• Implement a basic neural network in TensorFlow:

  import tensorflow as tf
  model = tf.keras.Sequential([
  tf.keras.layers.Dense(10, activation='relu', input_shape=(4,)),
  tf.keras.layers.Dense(3, activation='softmax')
  ])
  model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')
  

5. Building Video AI Applications on Jetson Nano

🔗 Course Link

What You’ll Learn:

• DeepStream pipelines for video processing
• Multi-stream handling
• YOLO and other inference engines

You Should Know:

• Run YOLOv5 on Jetson Nano:

  docker run -it --rm --runtime nvidia -v $(pwd):/workspace ultralytics/yolov5
  python detect.py --source 0  Webcam inference
  

6. Building RAG Agents with LLMs

🔗 Course Link

What You’ll Learn:

• Scalable deployment strategies
• LangChain for dialog management
• State-of-the-art model experimentation

You Should Know:

• Deploy a RAG model with LangChain:

  from langchain import OpenAI, VectorDBQA
  retriever = VectorDBQA.from_chain_type(llm=OpenAI(), chain_type="stuff")
  print(retriever.run("What is NVIDIA’s latest AI chip?"))
  

1. Accelerate Data Science Workflows with Zero Code Changes
   🔗 Course Link

What You’ll Learn:

• Unified CPU/GPU workflows
• GPU-accelerated data processing
• Faster ML training

You Should Know:

• Use RAPIDS for GPU-accelerated Pandas:

  import cudf
  df = cudf.read_csv("data.csv")
  print(df.groupby("category").mean())
  

9. to AI in the Data Center

🔗 Course Link

What You’ll Learn:

• AI, ML, and GPU architecture basics
• Deep learning frameworks
• Multi-system AI cluster deployment

You Should Know:

• Monitor GPU usage in Linux:

  nvidia-smi
  

• Run distributed TensorFlow training:

  strategy = tf.distribute.MirroredStrategy()
  with strategy.scope():
  model = tf.keras.Sequential([...])
  

What Undercode Say

These NVIDIA courses provide hands-on AI learning with real-world applications. Key takeaways:
– Generative AI is reshaping industries—experiment with diffusion models and LLMs.
– Jetson Nano enables edge AI—master camera integration and model deployment.
– GPU acceleration (RAPIDS, CUDA) optimizes data science workflows.
– RAG and LangChain enhance LLM capabilities for dynamic applications.

Expected Output:

A structured learning path for AI enthusiasts, covering theory, code, and deployment strategies.

References:

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