Skills Gained
- TensorFlow Fundamentals
- Deep Learning Model Development
- Neural Network Architecture
- Model Training and Optimization
- Model Deployment
- Computer Vision with TensorFlow (Basics)
- Natural Language Processing
Course Highlights
Deep Learning with TensorFlow
Mastered the fundamentals of deep learning and how to implement various neural network architectures using TensorFlow's powerful framework.
Practical Implementation
Gained hands-on experience in building, training, and deploying machine learning models for real-world applications.
Advanced Topics
Explored advanced concepts including transfer learning, model optimization, and working with different types of data (images, text, and sequences).
Learning Outcomes
- Understanding of TensorFlow's architecture and components
- Ability to design and train deep neural networks
- Experience with computer vision and NLP applications
- Knowledge of model optimization techniques
- Skills in deploying TensorFlow models
- Understanding of transfer learning and fine-tuning
Course Content
Key Topics Covered:
- Introduction to TensorFlow and Deep Learning
- Neural Networks and Activation Functions
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs) and LSTM
- Natural Language Processing with TensorFlow
- Transfer Learning and Fine-tuning
- Model Optimization and Deployment
- TensorFlow Extended (TFX) for Production