{"id": "urn:uuid:ca9fad8e-7615-44f8-8816-0d8a15540b0b", "type": ["VerifiableCredential", "OpenBadgeCredential"], "proof": {"type": "Ed25519Signature2020", "created": "2026-08-13T20:31:38Z", "proofValue": "zTq9WrQfkg7JfJzAqzsUwg5kp9C3FeeZUqHkTjoKawZFvSCPioAwoJoUCmg6fa31LfnzRpSbJWKUqPCKr7brtM54", "proofPurpose": "assertionMethod", "verificationMethod": "did:key:z6MkfwJEckNVbaLwbCETHMYkpfb7g2Nnvqn78ASyrYJfqvow#z6MkfwJEckNVbaLwbCETHMYkpfb7g2Nnvqn78ASyrYJfqvow"}, "issuer": {"id": "did:key:z6MkfwJEckNVbaLwbCETHMYkpfb7g2Nnvqn78ASyrYJfqvow", "name": "MIT Learn", "type": ["Profile"], "image": {"id": "https://learn.mit.edu/images/mit-red.png", "type": "Image", "caption": "MIT Learn logo"}}, "@context": ["https://www.w3.org/ns/credentials/v2", "https://purl.imsglobal.org/spec/ob/v3p0/context-3.0.3.json", "https://w3id.org/security/suites/ed25519-2020/v1"], "validFrom": "2026-08-13T20:31:38Z", "credentialSubject": {"type": ["AchievementSubject"], "identifier": [{"salt": "not-used", "type": "IdentityObject", "hashed": false, "identityHash": "Nikolaos Papakonstantinou", "identityType": "name"}], "achievement": {"id": "https://learn.mit.edu/courses/course-v1:UAI_SOURCE+UAI.6", "name": "Hands-On Deep Learning", "type": ["Achievement"], "criteria": {"narrative": "1. Understand the fundamentals of AI and deep learning\r\n   - Distinguish between AI, machine learning, and deep learning\r\n   - Recognize key application domains and problem types\r\n2. Explain the architecture and functioning of neural networks\r\n    - Describe the role of layers, weights, biases, and activation functions\r\n    - Understand forward and backward propagation\r\n3. Apply optimization techniques to train neural networks effectively\r\n    - Implement gradient descent, mini-batch gradient descent, and Adam optimization\r\n    - Tune learning rates and use early stopping to avoid overfitting\r\n4. Utilize deep learning frameworks\r\n    - Build and train models using Keras with a TensorFlow or PyTorch backend\r\n    - Leverage predefined layers, functional API, and preprocessing tools\r\n5. Handle real-world data in model training\r\n    - Prepare and preprocess tabular datasets for neural network input\r\n    - Select suitable loss functions and output layers for different tasks\r\n6. Evaluate and improve model performance\r\n    - Use validation sets for hyperparameter tuning\r\n    - Apply regularization techniques to generalize better to unseen data"}, "description": "Nikolaos Papakonstantinou has successfully completed all modules and earned a Course Certificate in Hands-On Deep Learning.", "achievementType": "Course"}, "activityEndDate": "2026-08-13T20:31:38Z", "activityStartDate": "2026-06-15T18:52:33Z"}}