IBM Deep Learning with PyTorch, Keras and Tensorflow Professional Certificate Course Syllabus
Full curriculum breakdown — modules, lessons, estimated time, and outcomes.
Overview: This comprehensive professional certificate program covers the core concepts and practical applications of deep learning using PyTorch, Keras, and TensorFlow—the three most widely used frameworks in industry and research. Designed by IBM, the course spans approximately 18 weeks of content with hands-on labs and real-world projects. Learners will build, train, and deploy neural networks across computer vision and natural language processing domains, culminating in a production-ready final project. Ideal for those seeking job-ready skills and an industry-recognized credential.
Module 1: Deep Learning Fundamentals
Estimated time: 16 hours
- Neural network mathematics
- Activation functions and backpropagation
- Framework comparison (PyTorch vs TensorFlow)
- Basic image classification with Keras
Module 2: Computer Vision
Estimated time: 20 hours
- CNN architectures (ResNet, VGG)
- Object detection (YOLO)
- Image segmentation (U-Net)
- Data augmentation techniques
Module 3: Natural Language Processing
Estimated time: 20 hours
- Word embeddings (Word2Vec, GloVe)
- RNNs and LSTMs
- Transformer architectures
- BERT fine-tuning
Module 4: Production Deployment
Estimated time: 16 hours
- Model quantization
- ONNX format conversion
- TensorFlow Serving
- Performance optimization
Module 5: Framework Integration and Advanced Topics
Estimated time: 12 hours
- Implementing models in PyTorch, Keras, and TensorFlow
- Hyperparameter tuning
- Transfer learning with pretrained models
- TorchScript for model export
Module 6: Final Project
Estimated time: 20 hours
- Build a deep learning application using one or more frameworks
- Deploy model using TensorFlow Serving or TorchScript
- Submit code, documentation, and performance evaluation
Prerequisites
- Proficiency in Python programming
- Familiarity with basic machine learning concepts
- Understanding of linear algebra and calculus fundamentals
What You'll Be Able to Do After
- Master neural network fundamentals including CNNs, RNNs, and transformers
- Implement deep learning models using PyTorch, Keras, and TensorFlow
- Solve real-world computer vision and NLP problems
- Optimize and deploy models in production environments
- Apply transfer learning and hyperparameter tuning to improve model performance