Everyone's talking about the fast.ai Massive Open Online Course (MOOC) so I decided to have a go at their 2019 deep learning course Practical Deep Learning for Coders, v3. I've always known some deep learning concepts/ideas (I've been in this field for about a year now, dealing mostly with computer vision), but never really understood some intuitions or explanations. I also understand that Jeremy Howard, Rachel Thomas and Sylvain Gugger (follow them on Twitter!) are influential people in the deep learning sphere (Jeremy has a lot of experience with Kaggle competitions), so I hope to gain new insights and intuitions, and some tips and tricks for model training from them. I have so much to learn from these folks.
Have you ever wondered whom to thank for some of the modern conveniences you might have started taking for granted, like Siri, Cortana or Alexa (assuming you agree these are conveniences)? The people at the Association for Computing Machinery (ACM) decided to thank Geoffrey Hinton, Yoshua Bengio and Yann LeCun in April of this year by honoring them with the Turing Award for their contributions to deep learning and neural networks. These contributions are put to use every time you log into your smartphone using fingerprint or facial recognition or when you use Google Photos or a voice assistant, and likely every time you use Amazon, Netflix, Facebook or Instagram. The advances in automatic language translation and autonomous cars in recent years arguably wouldn't have progressed as rapidly had it not been for the contributions of these three researchers. All of that is still an understatement of their contributions to artificial intelligence (AI).
This video is part of an online course, End-to-End Machine Learning with Tensorflow from Google Cloud. About this course: In the first course of this specialization, we will recap what was covered in the Machine Learning with TensorFlow on Google Cloud Platform Specialization. One of the best ways to review something is to work with the concepts and technologies that you have learned.
Automation testing in Selenium using Python language is probably the easiest way of getting into automation testing. Python is an easy to understand language. If you are looking to get into Selenium, this video will be a good start for you. We provide IT certifications training for professionals. We specialize in the following areas: a) Automation Testing (Selenium, DevOps) b) Business Analyst Certifications (Beginner and Senior levels) c) Robotic Process Automation (RPA) d) Tableau 10 Training Website: http://techcanvass.com
Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images.
Last month's announcement by Amazon that it plans to spend $700 million (£569 million) over six years to retrain a third of its US workforce was eye-catching for many reasons. One was the price tag: even for the world's second most valuable company, spending three-quarters of a billion dollars over half a decade to retrain 100,000 workers is a huge undertaking. Also noteworthy was the firm's reasoning. Amazon explicitly attributed its move to the rise of automation, machine learning and other technology: the so-called fourth industrial revolution. There was a sense that the pioneer of online retailing, famed for its use of automation, was merely an early accepter of an inescapable truth that all employers will soon have to face: that the skills of their existing workforces will no longer have any market value as their old roles are taken by machines and new roles are created. The company reportedly has 20,000 current vacancies.
While there are many online courses to learn Python for Machine learning and Data science, books are still the best way to for in-depth learning and significantly improving your knowledge. Python is a universal language that is used by both data engineers and data scientists and probably the most popular programming language as well. All the Data Scientists I have spoken and many in my friend circle just loves Python, mainly because it can automate all the tedious operational work that data engineers need to do. To make the deal even sweeter, Python also has the algorithms, analytics, and data visualization libraries like Metaplotlib, which is essential data scientists. In both roles, the need to manage, automate, and analyze data is made easier by only a few lines of code.
As language learning makes a sea change to online, users will come to expect personalized learning experiences. China's AI-powered online education market alone reached $568 million in 2017 and is expected to surpass $26 billion in 2022. At Sana Labs, we build AI technologies to power these learning experiences through easy to integrate APIs. This means that machine learning models for personalization as well as pronunciation, grammar, and overall fluency feedback can be production ready in days, not months. In this article, I'll highlight why deep learning will power this shift.