AI your way to a better job

Learn to code AI with Deeplearning.ai

Jane Elizabeth
deeplearning.ai
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Just off a stint at Baidu’s AI Group, Andrew Ng is here to bring artificial intelligence to the masses. Deeplearning.ai and Coursera are teaming up to bring a new sequence of online classes to help you learn to code AI.

 

Andrew Ng has been busy since he left Baidu’s AI Group earlier this year.  Now, the first of three promised projects from the Stanford Professor has finally been brought into the light, and it’s a doozy. Deeplearning.ai is teaming up with Coursera to bring a new, innovative artificial intelligence class to the masses.

Deeplearning.ai

Deep learning is one of the most highly sought after skills in tech. But you know that already. If you’re looking to start a career in tech or branch out, this might be a good idea. The Deep Learning specialization has five courses that will help devs understand the foundations of deep learning, how to build neural networks, and how to run successful machine learning projects.

The course is in Python and TensorFlow, parts of which will be taught in the course. The course description page lists all of the skills students will learn, including Convolutional networks, RNNs, LSTM, Adam, Dropout, BatchNorm, Xavier/He initialization, and more. There’s practical hands-on quizzes and applications of the deep learning theory, which can only help you in the job market.

Andrew Ng is optimistic about AI. “I hope we can build an AI-powered society that gives everyone affordable healthcare, provides every child a personalized education, makes inexpensive self-driving cars available to all, and provides meaningful work for every man and woman,” said Ng. “An AI-powered society that improves every person’s life.” Deeplearning.ai is geared towards bringing that future into light.

While fascinating, there is a small caveat: Coursera is not a free MOOC. Developers interested in taking this course will have to shell out a cool $49 a month for access to this course. (€43 for our European friends.) Since most of us have lives and work, it’s unlikely that we’ll finish the whole specialization in under a month. Especially since course 4 and 5 are still in the works. So, caveat emptor.

Got any free alternatives?

I’m so glad you asked. Not everyone has the spare monies to pay for this kind of specialization. So, for those of us with bills to pay, here are some open source alternatives.

Machine learning

DEEPNLP Deep Learning for Natural Language Processing University of Oxford

This is an applied course focusing on recent advances in analyzing and generating speech and text using recurrent neural networks. We introduce the mathematical definitions of the relevant machine learning models and derive their associated optimization algorithms. The course covers a range of applications of neural networks in NLP including analyzing latent dimensions in text, transcribing speech to text, translating between languages, and answering questions. This course is organised by Phil Blunsom and delivered in partnership with the DeepMind Natural Language Research Group.

COMS 4771 Machine Learning Columbia University

Course taught by Tony Jebara introduces topics in Machine Learning for both generative and discriminative estimation. Material will include least squares methods, Gaussian distributions, linear classification, linear regression, maximum likelihood, exponential family distributions, Bayesian networks, Bayesian inference, mixture models, the EM algorithm, graphical models, hidden Markov models, support vector machines, and kernel methods.

SEE MORE: Top 5 machine learning libraries for Java

CS20si Tensorflow for Deep Learning Research Stanford University

This course will cover the fundamentals and contemporary usage of the Tensorflow library for deep learning research. We aim to help students understand the graphical computational model of Tensorflow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. Through the course, students will use Tensorflow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks with LSTM to solve tasks such as word embeddings, translation, optical character recognition. Students will also learn best practices to structure a model and manage research experiments.

CS 156 Learning from Data Caltech

This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. ML has become one of the hottest fields of study today, taken up by undergraduate and graduate students from 15 different majors at Caltech. This course balances theory and practice, and covers the mathematical as well as the heuristic aspects.

SEE MORE: Top 5 open-source tools for machine learning

CS 287 Advanced Robotics UC Berkeley

The course introduces the math and algorithms underneath state-of-the-art robotic systems. The majority of these techniques are heavily based on probabilistic reasoning and optimization—two areas with wide applicability in modern Artificial Intelligence. An intended side-effect of the course is to generally strengthen your expertise in these two areas.

DS-GA 1008 Deep Learning New York University

This increasingly popular course is taught through the Data Science Center at NYU. Originally introduced by Yann Lecun, it is now led by Zaid Harchaoui, although Prof. Lecun is rumored to still stop by from time to time. It covers the theory, technique, and tricks that are used to achieve very high accuracy for machine learning tasks in computer vision and natural language processing. The assignments are in Lua and hosted on Kaggle.

Artificial Intelligence

CS 188 Introduction to Artificial Intelligence UC Berkeley

This course will introduce the basic ideas and techniques underlying the design of intelligent computer systems. A specific emphasis will be on the statistical and decision-theoretic modeling paradigm. By the end of this course, you will have built autonomous agents that efficiently make decisions in fully informed, partially observable and adversarial settings. Your agents will draw inferences in uncertain environments and optimize actions for arbitrary reward structures. Your machine learning algorithms will classify handwritten digits and photographs. The techniques you learn in this course apply to a wide variety of artificial intelligence problems and will serve as the foundation for further study in any application area you choose to pursue.

SEE MORE: Freelancers should dust off their AI and ML skills for fun and profit

CS 4700 Foundations of Artificial Intelligence Cornell University

This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, and object and face detection and recognition. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation, and mobile computer vision. This is a project-based course, in which you will implement several computer vision algorithms throughout the semester.

6.868J The Society of Mind MIT

This course is an introduction, by Prof. Marvin Minsky, to the theory that tries to explain how minds are made from collections of simpler processes. It treats such aspects of thinking as vision, language, learning, reasoning, memory, consciousness, ideals, emotions, and personality. It incorporates ideas from psychology, artificial intelligence, and computer science to resolve theoretical issues such as wholes vs. parts, structural vs. functional descriptions, declarative vs. procedural representations, symbolic vs. connectionist models, and logical vs. common-sense theories of learning.

 

Author
Jane Elizabeth
Jane Elizabeth is an assistant editor for JAXenter.com

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