Deep Learning is the go-to technique for many applications, from natural language processing to biomedical. Deep learning can handle many different types of data such as images, texts, voice/sound, graphs and so on. This course will cover the basics of DL including how to build and train multilayer perceptron, convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders (AE) and generative adversarial networks (GANs). The course includes several hands-on projects, including cancer detection with CNNs, RNNs on disaster tweets, and generating dog images with GANs.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Machine Learning: Theory and Hands-on Practice with Python

Über diesen Kurs
Calculus, Linear algebra, Python, NumPy, Pandas, Matplotlib, and Scikit-learn. Familiarity with classic Supervised and Unsupervised Learning.
Was Sie lernen werden
Apply different optimization methods while training and explain different behavior.
Use cloud tools and deep learning libraries to implement CNN architecture and train for image classification tasks.
Apply deep learning package to sequential data, build models, train, and tune.
Kompetenzen, die Sie erwerben
- Deep Learning
- Artificial Neural Network
- Convolutional Neural Network
- Unsupervised Deep Learning
- Recurrent Neural Network
Calculus, Linear algebra, Python, NumPy, Pandas, Matplotlib, and Scikit-learn. Familiarity with classic Supervised and Unsupervised Learning.
Beginnen Sie damit, auf Ihren Master-Abschluss hinzuarbeiten.
Lehrplan - Was Sie in diesem Kurs lernen werden
Deep Learning Introduction, Multilayer Perceptron
Training Neural Networks
Deep Learning on Images
Deep Learning on Sequential Data
Über den Spezialisierung Machine Learning: Theory and Hands-on Practice with Python

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