Deep Learning Specialization

Course 1: Neural Networks and Deep Learning (4 weeks)

• Week 1: Introduction to deep learning, logistic regression as neural network
• Week 2: Shallow neural networks, activation functions
• Week 3: Deep neural networks, forward and backward propagation
• Week 4: Deep neural network application

Course 2: Improving Deep Neural Networks (3 weeks)

• Week 1: Regularization, dropout, batch normalization, gradient checking
• Week 2: Optimization algorithms: mini-batch, momentum, RMSprop, Adam
• Week 3: Hyperparameter tuning, batch normalization, TensorFlow framework

Course 3: Structuring Machine Learning Projects (2 weeks)

• Week 1: ML strategy, orthogonalization, train/dev/test sets
• Week 2: Error analysis, mismatched training/test sets, transfer learning

Course 4: Convolutional Neural Networks (4 weeks)

• Week 1: Foundations of CNNs, edge detection, padding, pooling
• Week 2: Classic networks: LeNet, AlexNet, VGG, ResNet
• Week 3: Object detection: YOLO, R-CNN
• Week 4: Face recognition and neural style transfer

Course 5: Sequence Models (3 weeks)

• Week 1: Recurrent Neural Networks (RNNs), LSTM, GRU
• Week 2: Natural Language Processing and word embeddings
• Week 3: Sequence-to-sequence models, attention mechanism, transformers

Taught by Andrew Ng, world-renowned AI expert and educator Comprehensive coverage of deep learning from foundations to advanced topics Hands-on programming assignments using TensorFlow and Keras Build real projects: image classification, object detection, NLP models Industry-recognized certificate from DeepLearning.AI Free to audit all course materials

Requires solid understanding of machine learning basics (take ML Specialization first) Math-intensive with linear algebra and calculus Time-intensive: 16 weeks at 10-15 hours/week Certificate costs $49/month