What you will learn in IBM Deep Learning with PyTorch, Keras and Tensorflow Professional Certificate Course
- Master neural network fundamentals (CNNs, RNNs, transformers)
- Implement models in PyTorch, Keras, and TensorFlow
- Solve computer vision and NLP problems
- Optimize models with hyperparameter tuning
- Deploy models using TensorFlow Serving and TorchScript
- Apply transfer learning with pretrained models
Program Overview
Deep Learning Fundamentals
⏱️ 4 weeks
- Neural network mathematics
- Activation functions and backpropagation
- Framework comparison (PyTorch vs TensorFlow)
- Basic image classification
Computer Vision
⏱️ 5 weeks
- CNN architectures (ResNet, VGG)
- Object detection (YOLO)
- Image segmentation (U-Net)
- Data augmentation techniques
Natural Language Processing
⏱️5 weeks
- Word embeddings (Word2Vec, GloVe)
- RNNs and LSTMs
- Transformer architectures
- BERT fine-tuning
Production Deployment
⏱️4 weeks
- Model quantization
- ONNX format conversion
- TensorFlow Serving
- Performance optimization
Get certificate
Job Outlook
- High-Demand Roles:
- Deep Learning Engineer (120K220K)
- AI Researcher (140K250K+)
- Computer Vision Specialist (130K210K)
- NLP Engineer (125K200K)
- Industry Trends:
- 40% annual growth in deep learning jobs
- PyTorch dominates research (70% papers)
- TensorFlow leads production deployments (60% enterprises)
Specification: IBM Deep Learning with PyTorch, Keras and Tensorflow Professional Certificate
|
IBM Deep Learning with PyTorch, Keras and Tensorflow Professional Certificate
