Deep Learning is the driving force behind AI advancements, enabling machines to learn from vast amounts of data. This course will provide an in-depth understanding of neural networks, deep learning architectures, and their applications. Learn to build and optimize deep learning models using TensorFlow and PyTorch.
This course covers the foundations of deep learning, from basic neural networks to advanced architectures such as CNNs, RNNs, and Transformers. You will work on real-world applications like image recognition, speech processing, and predictive analytics. With hands-on coding sessions and projects, you will gain the expertise needed to create deep learning-powered solutions.
- Fundamentals of neural networks and activation functions.
- Deep learning frameworks: TensorFlow and PyTorch.
- Convolutional Neural Networks (CNNs) for image processing.
- Recurrent Neural Networks (RNNs) for sequential data.
- Transformer-based models for deep learning applications.
- Model optimization, hyperparameter tuning, and deployments.
Earn an industry-recognized Techbriq certification in Deep Learning, validating your expertise in building and deploying deep neural networks for AI applications.
- Neural networks and backpropagation
- Activation functions and loss functions
- Optimization techniques (SGD, Adam, RMSProp)
- Basics of CNN architecture
- Image classification with CNNs
- Transfer learning for deep learning models
- Recurrent Neural Networks (RNNs) and LSTMs
- Attention mechanisms and transformers
- Generative AI using deep learning
- Image recognition and object detection
- Time-series forecasting using RNNs
- Building a deep learning-powered recommendation system
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