Deep Learning Specialization
Delve into the world of artificial neural networks, deep architectures, and state-of-the-art AI techniques. This course equips you with the skills to design, train, evaluate, and deploy deep learning models in real-world environments.
Key Topics Covered:
- Introduction to Artificial Neural Networks
- Backpropagation and Optimization Techniques
- Convolutional Neural Networks (CNNs) for Image Processing
- Recurrent Neural Networks (RNNs), LSTMs and GRUs
- Transformers and Attention Mechanisms
- Autoencoders and Generative Adversarial Networks (GANs)
- Deep Reinforcement Learning Basics
Tools & Frameworks:
- Python, NumPy, Pandas
- TensorFlow, Keras, PyTorch
- Scikit-learn for comparison and preprocessing
- OpenCV, NLTK, HuggingFace Transformers
Hands-On Projects:
- Image classifier using CNN on CIFAR-10 dataset
- Text generation with RNNs using Shakespeare corpus
- Digit recognition app using MNIST and TensorFlow
- GAN-based image generation model
- Deploying a model using TensorFlow Lite
What You'll Gain:
- Mastery of modern deep learning architectures
- Skills to build AI-powered applications
- Understanding of the math behind deep learning
- Hands-on experience with real datasets and models
Career Pathways:
- AI Engineer
- Deep Learning Researcher
- Computer Vision Specialist
- Natural Language Processing Engineer