Introduction to Deep Learning

Go to Course: https://www.coursera.org/learn/introduction-to-deep-learning-boulder

Introduction

**Course Review: Introduction to Deep Learning on Coursera** If you're interested in harnessing the power of artificial intelligence and deep learning for real-world applications, "Introduction to Deep Learning" on Coursera is a course worth considering. Whether you're aiming to advance your career in data science, machine learning, or simply looking to dive into the mesmerizing world of neural networks, this course lays a solid foundation for understanding the fundamentals of deep learning. ### Course Overview Deep Learning has revolutionized various fields, including natural language processing, computer vision, and biomedical applications, making it an invaluable skill set for professionals today. This course covers critical concepts, such as building and training various neural network architectures: multilayer perceptrons (MLPs), convolutional neural networks (CNNs), recurrent neural networks (RNNs), autoencoders, and generative adversarial networks (GANs). With a focus on hands-on projects, participants will work through real-world applications and datasets, enhancing their understanding through practical experience. ### Syllabus Breakdown The curriculum is thoughtfully structured over multiple modules: 1. **Deep Learning Introduction and Multilayer Perceptron**: The course kicks off with a comprehensive introduction, exploring the wide-ranging applications of deep learning. You will learn about perceptrons, delve into the concept of multilayer perceptrons, and grasp the significance of the backpropagation algorithm—a cornerstone of training neural networks. This module sets the pace for what's to come and includes quizzes, a practical programming assignment, and peer reviews. 2. **Training Neural Networks**: Building on the foundation, this module covers essential optimization methods, particularly Stochastic Gradient Descent (SGD) and various advanced techniques. You'll learn how to fine-tune parameters to achieve better model performance and tackle issues like overfitting with regularization techniques. By the end of this week, you'll have equipped yourself with the Keras library, ready to implement deep learning algorithms. 3. **Deep Learning on Images**: As images form a crucial part of many datasets, this module focuses on convolutional neural networks. You’ll get hands-on experience with a Kaggle mini-project that challenges you to classify cancer image data—an impactful way to solidify your learning while contributing to healthcare technology. 4. **Deep Learning on Sequential Data**: This module explores recurrent neural networks, designed for sequential data processing, such as time series and text. By engaging in an NLP Kaggle challenge, you’ll get practical exposure to how RNNs handle tasks like sentiment analysis. 5. **Unsupervised Approaches in Deep Learning**: The final module guides you through unsupervised learning techniques, including autoencoders and GANs. You will appreciate the utility of these models in scenarios where labeled data is scarce, especially in fields like healthcare. ### Course Format and Assessments The course balances theoretical concepts with practical applications. Expect quizzes, Jupyter notebook assignments, and Kaggle mini-projects throughout, fostering an engaging learning environment. The emphasis on peer reviews also encourages collaboration and helps sharpen your feedback skills. ### Recommendations This course is perfectly suited for beginners and practitioners alike. If you have a basic understanding of programming (preferably Python) and a foundation in statistics or linear algebra, you're well-prepared to tackle the content. Moreover, the hands-on projects and challenges provided by Kaggle give you the chance to build a portfolio showcasing your skills, which is increasingly important in the job market today. ### Conclusion In conclusion, "Introduction to Deep Learning" on Coursera is a compelling course that equips you with the necessary tools to embark on a journey into the world of deep learning. The structure of the modules, the blend of theory and practice, and the relevance of the projects make this an excellent starting point in your AI journey. Whether your goal is to enhance your career prospects or to explore the intricacies of deep learning, this course should be on your list. I highly recommend enrolling in this course if you are eager to build a robust understanding of deep learning and gain hands-on experience with industry-relevant projects!

Syllabus

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.

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. This assignment is your last Jupyter lab notebook for the course. For the next three weeks, you will build hands-on experience and complete weekly mini-projects that incorporate Kaggle challenges.

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.

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. We don't have a quiz this week, but we have a Kaggle challenge mini-project on NLP with Disaster Tweets. The project is a Getting Started competition designed for learners building their machine learning background. The challenge is very doable in a week, but make sure to start early to run experiments and iterate a bit.

This module will focus on neural network models trained via unsupervised Learning. We will cover autoencoders and GAN as examples. We will consider the famous AI researcher Yann LeCun's cake analogy for Reinforcement Learning, Supervised Learning, and Unsupervised Learning. Supervised Deep Learning has had tremendous success, mainly due to the availability of massive datasets like ImageNet. However, it is expensive and challenging to obtain labeled data for areas like biomedical images. There is great motivation to continue developing unsupervised Deep Learning approaches to harness abundant unlabeled data sources. This week is the last week of new course material. There is no quiz or Jupyter notebook lab. Generative adversarial networks (GANs) learn to generate new data with the same statistics as the training set. This week, you will wrap up one final Kaggle mini-project. This time, you will experiment with creating a network to generate images of puppies.