Learning Deep Learning: Unit 2

About this course

This course covers advanced deep learning topics, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and modern language models. You will learn techniques for image classification, time series prediction, and natural language processing. The course includes building and optimizing CNNs for image recognition, using architectures such as AlexNet, VGGNet, GoogLeNet, and ResNet, and working with pre-trained models. You will also work with RNNs and LSTMs for tasks like forecasting and text autocompletion. The curriculum covers neural language models, word embeddings (such as Word2vec and wordpieces), encoder-decoder architectures, attention mechanisms, and Transformers for machine translation. Hands-on projects using TensorFlow and PyTorch will help you develop practical skills for solving real-world problems in computer vision and language processing.

What you will learn

• Build and optimize convolutional neural networks for advanced image classification tasks using TensorFlow and PyTorch.
• Apply recurrent neural networks and LSTMs to sequential data problems, including time series forecasting and text autocompletion.
• Develop neural language models and implement word embeddings for robust natural language processing.
• Design and implement encoder-decoder architectures and Transformer models for machine translation and sequence-to-sequence tasks.