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
von
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.
von

University of Colorado Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
Beginnen Sie damit, auf Ihren Master-Abschluss hinzuarbeiten.
Lehrplan - Was Sie in diesem Kurs lernen werden
Deep Learning Introduction, Multilayer Perceptron
We are starting off the course with a busy week. This week's module has two parts. In the first part, after a quick introduction to Deep Learning's exciting applications in self-driving cars, medical imaging, and robotics, we will learn about artificial neurons called perceptrons. Interestingly, neural networks are loosely modeled on the human brain with perceptrons mimicking neurons. After we learn to train a simple perceptron (and become aware of its limitations), we will move on to more complex multilayer perceptrons. The second part of the module introduces the backpropagation algorithm, which trains a neural network through the chain rule. We will finish by learning how deep learning libraries like Tensorflow create computation graphs for gradient computation. This week, you will have two short quizzes, a Jupyter lab programming assignment, and an accompanying Peer Review assignment. This material, notably the backpropagation algorithm, is so foundational to Deep Learning that it is essential to take the time necessary to work through and understand it.
Training Neural Networks
Last week, we built our Deep Learning foundation, learning about perceptrons and the backprop algorithm. This week, we are learning about optimization methods. We will start with Stochastic Gradient Descent (SGD). SGD has several design parameters that we can tweak, including learning rate, momentum, and decay. Then we will turn our attention to advanced gradient descent methods like learning rate scheduling and Nesterov momentum. Besides vanilla gradient descent, other optimization algorithms include AdaGrad, AdaDelta, RMSprop, and Adam. We will cover general tips to reduce overfitting while training neural networks, including regularization methods like dropout and batch normalization. This week, you will build your DL toolkit, gaining experience with the Python library Keras. Assessments for the week include a quiz and a Jupyter lab notebook with an accompanying Peer Review.
Deep Learning on Images
This module will teach a type of neural network called convolutional neural networks, suitable for image analysis tasks. We will learn about definitions, design parameters, operations, hyperparameter tuning, and applications. There is no Jupyter lab notebook this week. You will have a brief quiz and participate in a clinically relevant Kaggle challenge mini-project. It is critical to evaluate whether cancer has spread to the sentinel lymph node for staging breast cancer. You will build a CNN model to classify whether digital pathology images show that cancer has spread to the lymph nodes. This project utilizes the PCam dataset, which has an approachable size, with the authors noting that "Models can easily be trained on a single GPU in a couple of hours, and achieve competitive scores." As you prepare for the week, look over the rubric and develop a plan for how you will complete it. It will be necessary for a project like this to work on a timeframe that allows you to run experiments. The expectation is not that you will cram the equivalent of a final project into a single week or that you need to have a top leaderboard score to receive a good grade for this project. Hopefully, you will have time to achieve some exciting results to show off in your portfolio.
Deep Learning on Sequential Data
This module will teach you another neural network called recurrent neural networks (RNNs) to handle sequential data. So far, we have covered feed-forward neural networks, including Multi-layer Perceptrons and CNNs. However, in biological systems, information can flow backward and forwards. RNNs do a backward pass closer to biological systems. Using RNNs has excellent benefits, especially for text data, since RNN architectures reduce the number of parameters. We will learn about the vanishing and exploding gradient problems that can arise when working with vanilla RNNs and remedies for those problems, including GRU and LSTM cells.
Über den Spezialisierung Machine Learning: Theory and Hands-on Practice with Python
In the Machine Learning specialization, we will cover Supervised Learning, Unsupervised Learning, and the basics of Deep Learning. You will apply ML algorithms to real-world data, learn when to use which model and why, and improve the performance of your models. Starting with supervised learning, we will cover linear and logistic regression, KNN, Decision trees, ensembling methods such as Random Forest and Boosting, and kernel methods such as SVM. Then we turn our attention to unsupervised methods, including dimensionality reduction techniques (e.g., PCA), clustering, and recommender systems. We finish with an introduction to deep learning basics, including choosing model architectures, building/training neural networks with libraries like Keras, and hands-on examples of CNNs and RNNs.

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