This first course in the two-part program, Creative Applications of Deep Learning with TensorFlow, introduces you to deep learning: the state-of-the-art approach to building artificial intelligence algorithms. We cover the basic components of deep learning, what it means, how it works, and develop code necessary to build various algorithms such as deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks.

A major focus of this course will be to not only understand how to build the necessary components of these algorithms, but also how to apply them for exploring creative applications. We'll see how to train a computer to recognize objects in an image and use this knowledge to drive new and interesting behaviors, from understanding the similarities and differences in large datasets and using them to self-organize, to understanding how to infinitely generate entirely new content or match the aesthetics or contents of another image.

Deep learning offers enormous potential for creative applications and in this course we interrogate what's possible. Through practical applications and guided homework assignments, you'll be expected to create datasets, develop and train neural networks, explore your own media collections using existing state-of-the-art deep nets, synthesize new content from generative algorithms, and understand deep learning's potential for creating entirely new aesthetics and new ways of interacting with large amounts of data.

Artificial Intelligence is everywhere. Organizations are increasingly adopting AI as a way to enable data-driven decision making, and as a great source of automated predictions that will potentially generate interesting savings or new sources of revenue. Even our personal devices such as smartphones or voice assistants are already leveraging AI technologies.

However, the level of AI maturity within the companies varies a lot, as well as the needs for AI-savvy professionals. Reality is that not everyone needs to be an AI expert or a data scientist. Companies need other kinds of profiles for which at least AI knowledge is required, such as product managers or top executives managing innovation initiatives.

This course is designed to give you an introduction to the amazing world of Artificial Intelligence. It offers a very pragmatic overview of AI fundamentals, accessible to both technical and non-technical audiences. This course provides an entrance to the amazing Linux Foundation AI & Data ecosystem, which will be very useful for people looking for relevant open source tools or areas to get involved to continue developing new data and AI skills.

Data and AI Fundamentals is geared towards professionals and students looking for new AI skills, including company executives, hiring managers, product managers, and developers. This course would also be beneficial for industry professionals coming from diverse industries such as finance, supply chain, manufacturing, and other verticals.

It examines the different kinds of AI technologies (e.g., machine learning, NLP). It discusses how to enumerate typical AI use cases for a variety of industries and identifies potential AI career opportunities. Learn to navigate the rich set of Linux Foundation AI & Data open source projects and tools throughout the course.

This course prepares students with the ability to identify the different options available from the family of AI technologies. Upon completing this course, you will be able to choose suitable AI techniques depending on the business needs and leverage existing AI projects and tools from the LF AI & Data ecosystem.

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.

You've definitely heard of AI and Deep Learning. But when you ask yourself, what is my position with respect to this new industrial revolution, that might lead you to another fundamental question: am I a consumer or a creator? For most people nowadays, the answer would be, a consumer.

But what if you could also become a creator?

What if there was a way for you to easily break into the World of Artificial Intelligence and build amazing applications which leverage the latest technology to make the World a better place?

Sounds too good to be true, doesn't it?

But there actually is a way..

Computer Vision is by far the easiest way of becoming a creator.

And it's not only the easiest way, it's also the branch of AI where there is the most to create.

Why? You'll ask.

That's because Computer Vision is applied everywhere. From health to retail to entertainment - the list goes on. Computer Vision is already a $18 Billion market and is growing exponentially.

Just think of tumor detection in patient MRI brain scans. How many more lives are saved every day simply because a computer can analyze 10,000x more images than a human?

And what if you find an industry where Computer Vision is not yet applied? Then all the better! That means there's a business opportunity which you can take advantage of.

So now that raises the question: how do you break into the World of Computer Vision?

Up until now, computer vision has for the most part been a maze. A growing maze.

As the number of codes, libraries and tools in CV grows, it becomes harder and harder to not get lost.

On top of that, not only do you need to know how to use it - you also need to know how it works to maximise the advantage of using Computer Vision.

To this problem we want to bring...

Computer Vision A-Z.

With this brand new course you will not only learn how the most popular computer vision methods work, but you will also learn to apply them in practice!

Can't wait to see you inside the class,

Kirill & Hadelin

AI is used in many businesses to optimise processes and improve working life.

On this three-week course, you’ll learn about the past, present, and future of artificial intelligence and explore its potential in the workplace.

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With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions.

The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. Visit the FAQs below for important information regarding 1) Date of original release and Termination or expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.

After finishing this course you will know:

How to train models that achieve state-of-the-art results in:
Computer vision, including image classification (e.g., classifying pet photos by breed)
Natural language processing (NLP), including document classification (e.g., movie review sentiment analysis) and phrase similarity
Tabular data with categorical data, continuous data, and mixed data
Collaborative filtering (e.g., movie recommendation)
How to turn your models into web applications, and deploy them
Why and how deep learning models work, and how to use that knowledge to improve the accuracy, speed, and reliability of your models
The latest deep learning techniques that really matter in practice
How to implement stochastic gradient descent and a complete training loop from scratch

Here are some of the techniques covered (don’t worry if none of these words mean anything to you yet–you’ll learn them all soon):

Random forests and gradient boosting
Affine functions and nonlinearities
Parameters and activations
Transfer learning
Stochastic gradient descent (SGD)
Data augmentation
Weight decay
Image classification
Entity and word embeddings
And much more

Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. This area is also concerned with issues both theoretical and practical.

In this course, we will present algorithms and approaches in such a way that grounds them in larger systems as you learn about a variety of topics, including:

statistical supervised and unsupervised learning methods
randomized search algorithms
Bayesian learning methods
reinforcement learning

The course also covers theoretical concepts such as inductive bias, the PAC and Mistake‐bound learning frameworks, minimum description length principle, and Ockham's Razor. In order to ground these methods the course includes some programming and involvement in a number of projects.

By the end of this course, you should have a strong understanding of machine learning so that you can pursue any further and more advanced learning.

This is a three-credit course.

Students should be familiar with college-level mathematical concepts (calculus, analytic geometry, linear algebra, and probability) and computer science concepts (algorithms, O notation, data structures). In addition to this, students should have working knowledge of computer programming; the course will focus on using Python for its programming assignments.

Our Global BBA & a Bachelor in Artificial Intelligence for Business offers a double degree in just four years with 1-2 years of study abroad. Students can choose to study at one of our EADA-SKEMA Global BBA campuses (Brazil, China, France, South Africa, U.S.) or our 47 exchange partner institutions.

Participants receive a general business education, expert AI training, and specialised knowledge in one of 11 areas that prepares them, like so many others in our alumni community, to launch an international career from the more than 2,000 job offers we receive annually. What makes this double degree so unique is the solid cross-disciplinary managerial skills and technological know-how that our design provides, and which allow graduates to progress quickly towards senior management positions.

All the graduates of this programme, whether in a more managerial or technical role, will be ready to make the important choices that will shape the future of society, including those that give a long-term sustainable advantage to the organisations they choose to work for.

Artificial intelligence (AI) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. To stay competitive, organizations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data driven actionable intelligence for their businesses. This 6-course Professional Certificate is designed to equip you with the tools you need to succeed in your career as an AI or ML engineer.

You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers.

Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders.

In addition to earning a Professional Certificate from Coursera, you will also receive a digital badge from IBM recognizing your proficiency in AI engineering.

Проект прикладного обучения

Throughout the program, you will build a portfolio of projects demonstrating your mastery of course topics. The hands-on projects will give you a practical working knowledge of Machine Learning libraries and Deep Learning frameworks such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow. You will also complete an in-depth Capstone Project, where you’ll apply your AI and Neural Network skills to a real-world challenge and demonstrate your ability to communicate project outcomes.