Deep Learning Specialization

What you will learn from this course

• Build & train deep neural networks, identify key architecture parameters, & implement efficient neural networks & deep learning to your applications
• Train test sets & analyze variance for DL applications, use standard techniques & optimization algorithms, & build neural networks in TensorFlow
• Build a CNN & apply it to detection & recognition tasks, use neural style transfer to generate art, & apply algorithms to image, video, & 2D/3D data
• Build & train RNNs, work with NLP & Word Embeddings, & use HuggingFace tokenizers & transformer models to perform tasks like NER & Question Answering
• Learn to set up a machine learning problem with a neural network mindset.
• Learn to use vectorization to speed up your models.
• Learn to build a neural network with one hidden layer, using forward propagation and backpropagation.
• Learn to implement the foundational layers of CNNs (pooling, convolutions) and to stack them properly in a deep network to solve multi-class image classification problems.
• Learn about the practical tricks and methods used in deep CNNs straight from the research papers.
• Learn how to apply your knowledge of CNNs to one of the toughest but hottest field of computer vision: Object detection.
• Learn about recurrent neural networks

By the end of the course, you’ll be able to

• Build and train deep neural networks, implement vectorized neural networks, identify key parameters in architecture, and apply deep learning to your applications
• Use the best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard neural network techniques, apply optimization algorithms, and implement a neural network in TensorFlow
• Diagnose and use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end learning, transfer learning, and multi-task learning
• Build a CNN, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data
• Build and train RNNs, GRUs, and LSTMs, apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform NER and Question Answering

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