9 Deep

[Niobe Hennigan]

TheDeep Learning textbook is a resource intended to help studentsand practitioners enter the field of machine learning in generaland deep learning in particular.The online version of the book is now complete and will remainavailable online for free.

If you notice any typos (besides the known issues listed below) or have suggestions for exercises to add to thewebsite, do not hesitate to contact the authors directly by e-mailat: feed...@deeplearningbook.org

Founded in 1917, Deep Springs College is a unique institution of higher learning. The educational program is built upon three pillars: academics, student self-government, and manual labor. The school is located forty miles from Bishop, California on an isolated cattle ranch in Deep Springs Valley.

Between twelve and fifteen students are admitted each year. Each receives a full scholarship; the college covers the costs of tuition, room, and board for every student offered admission. In exchange, Deep Springs students are expected to dedicate themselves to lives of service to humanity. Alumni have gone on to exemplify this ideal in a variety of fields, including politics, science, journalism, academics, agriculture, medicine, law, business, and design.

Together with the Telluride Association, founded in 1911, these compose The League of Nunnian Schools. The League aims to support existing and developing Nunnian projects in America and around the world. If you have questions, requests, or would like to otherwise contact the League, please write to com...@deepsprings.edu.

Students at Deep Springs participate in running the college itself in ways that are not possible at larger institutions. The Student Body exists as a discrete, self-governing entity within the College, tasked with governing itself and working with the staff and faculty in the day-to-day operation of the college.

The Student Body convenes at least once a week to deliberate and take action on issues concerning community life, labor, academic planning, disciplinary matters, election of student office holders, and more. Students are elected to positions of leadership within the Student Body and the larger community. Elected positions include Student Body President; Labor Commissioner, who assigns students labor positions and is in charge of making sure the labor program runs smoothly; and Student Trustee, one of two students who sits on the Board of Trustees of the College as full voting members.

Since Deep Springs is only a two-year program, students do not often see the full outcomes of their decisions. But just as they are the recipients of decisions made by students before them, so they, in turn, are obligated to try to make the best choices possible for students who follow. This chain of responsibility, from one generation of students to the next, continually reinforces and reinvigorates the gravity students at Deep Springs take in governing their college.

The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology.

In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.

AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

In the fourth course of the Deep Learning Specialization, you will understand how computer vision has evolved and become familiar with its exciting applications such as autonomous driving, face recognition, reading radiology images, and more.

In the fifth course of the Deep Learning Specialization, you will become familiar with sequence models and their exciting applications such as speech recognition, music synthesis, chatbots, machine translation, natural language processing (NLP), and more.

Deep Learning is a subset of machine learning where artificial neural networks, algorithms based on the structure and functioning of the human brain, learn from large amounts of data to create patterns for decision-making. Neural networks with various (deep) layers enable learning through performing tasks repeatedly and tweaking them a little to improve the outcome.

The Deep Learning Specialization is a foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. In this Specialization, you will build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. Get ready to master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more. AI is transforming many industries. The Deep Learning Specialization provides a pathway for you to take the definitive step in the world of AI by helping you gain the knowledge and skills to level up your career. Along the way, you will also get career advice from deep learning experts from industry and academia.

1. Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to your applications.2. Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow3. Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning4. Build a Convolutional Neural Network, 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 data5. Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering

Younes Bensouda MourriOpens in a new tab completed his Bachelor's in Applied Mathematics and Computer Science and Master's in Statistics from Stanford University. Younes helped create 3 AI courses at Stanford - Applied Machine Learning, Deep Learning, and Teaching AI - and taught two of them for a few years.

Visit coursera.org/businessOpens in a new tab for more information, to pick up a plan, and to contact Coursera. For each plan, you decide the number of courses every member can enroll in and the collection of courses they can choose from.

Those planning to attend a degree program can utilize ACE️ recommendationsOpens in a new tab, the industry standard for translating workplace learning to college credit. Learners can earn a recommendation of 10 college credits for completing the Deep Learning Specialization. This aims to help open up additional pathways to learners who are interested in higher education, and prepare them for entry-level jobs.

To share proof of completion with schools, certificate graduates will receive an email prompting them to claim their CredlyOpens in a new tab badge, which contains the ACE️ credit recommendation. Once claimed, they will receive a competency-based transcript that signifies the credit recommendation, which can be shared directly with a school from the Credly platform. Please note that the decision to accept specific credit recommendations is up to each institution and is not guaranteed.

To share proof of completion with schools, certificate graduates will receive an email prompting them to claim their Credly badge, which contains the ACE️ credit recommendation. Once claimed, they will receive a competency-based transcript that signifies the credit recommendation, which can be shared directly with a school from the Credly platform. Please note that the decision to accept specific credit recommendations is up to each institution and is not guaranteed.

The Deep Learning Specialization is eligible for college credit at participating colleges and universities nationwide. The decision to accept specific credit recommendations is up to each institution and not guaranteed. Read more about ACE Credit College & University Partnerships hereOpens in a new tab.

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. If you only want to read and view the course content, you can audit the course for free. If you cannot afford the fee, you can apply for financial aidOpens in a new tab.

Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, to simulate the complex decision-making power of the human brain. Some form of deep learning powers most of the artificial intelligence (AI) applications in our lives today.

While supervised learning models require structured, labeled input data to make accurate outputs, deep learning models can use unsupervised learning. With unsupervised learning, deep learning models can extract the characteristics, features and relationships they need to make accurate outputs from raw, unstructured data. Additionally, these models can even evaluate and refine their outputs for increased precision.

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