Free Coupon Discount - TensorFlow 2.0 Practical Advanced, Master Tensorflow 2.0, Google's most powerful Machine Learning Library, with 5 advanced practical projects Created by Dr. Ryan Ahmed, Ph.D., MBA, Kirill Eremenko, Hadelin de Ponteves, SuperDataScience Team, Mitchell Bouchard Students also bought Recommender Systems and Deep Learning in Python Machine Learning and AI: Support Vector Machines in Python Natural Language Processing with Deep Learning in Python Artificial Intelligence: Reinforcement Learning in Python Data Science: Deep Learning in Python Preview this Udemy Course GET COUPON CODE Description Google has recently released TensorFlow 2.0 which is Google's most powerful open source platform to build and deploy AI models in practice. Tensorflow 2.0 release is a huge win for AI developers and enthusiast since it enabled the development of super advanced AI techniques in a much easier and faster way. The purpose of this course is to provide students with practical knowledge of building, training, testing and deploying Advanced Artificial Neural Networks and Deep Learning models using TensorFlow 2.0 and Google Colab. This course will cover advanced, state-of-the–art AI models implementation in TensorFlow 2.0 such as DeepDream, AutoEncoders, Generative Adversarial Networks (GANs), Transfer Learning using TensorFlow Hub, Long Short Term Memory (LSTM) Recurrent Neural Networks and many more. The applications of these advanced AI models are endless including new realistic human photographs generation, text translation, image de-noising, image compression, text-to-image translation, image segmentation, and image captioning.

The Banff International Research Station will host the "Interpretability in Artificial Intelligence" workshop in Banff from May 1 - 6, 2022. State-of-the-art deep learning networks are achieving strong predictive power, but the gain in accuracy often comes at the price of transparency, meaning that the prediction of the model is not interpretable. These so-called black-box models raise critical challenges in high-stake domains, such as healthcare, crime recidivism, or finance, where a wrong decision can have very harmful consequences. For instance, a tool to risk-stratify patients trained on a very unbalanced datasets can assign all new cases to the most prevalent risk-category, failing hence to identify clinical factors that might separate high- and low-risk patients. In other cases, ethnic, economic, or social factors, which might by chance correlate with a patient group, might be wrongly used by the model to classify new patients.

In the era of Big Data, machine learning and data analytics are vital to the success of any organisation. From simple sales forecasts to the AI behind self-driving cars, data are helping to drive continuous improvement. The techniques are powerful, but need to be used with a full understanding of the subject. It is vital to understand best practice, and how an analytics project fits with the business objectives. This is a technical course, but it also has a very applied focus.

This course is designed to teach the student the concepts of supervised and unsupervised machine learning by experimenting on the toy datasets that are installed in Python's machine learning library, sklearn. The student will learn the basics of coding in the Python programming language and then will learn the basics of machine learning by studying a very small dataset and the code that has been used to make predictions on it. When the student has learned the basics of programming in Python and making predictions on a very small movie recommendation dataset, he will go on to study the eight toy datasets that are installed in sklearn, which is Python's machine learning library. The student will study the code of the above dataset and will learn the basics of supvervised machine learning, which involves making predictions on labeled datasets to answer either regression or classification problems. The students will also go over the code on an unsupervised learning technique, clustering.

I have worked with IBM, Cisco, EMC-RSA and others, and I have been an academics for a couple of years. I worked in four continents and travelled extensively. I have a PhD in Engineering, an MSc in AI and an MBA, i am also a Certified Blockchain Expert.

Students in the MIT course 6.036 (Introduction to Machine Learning) study the principles behind powerful models that help physicians diagnose disease or aid recruiters in screening job candidates. Now, thanks to the Social and Ethical Responsibilities of Computing (SERC) framework, these students will also stop to ponder the implicationsof these artificial intelligence tools, which sometimes come with their share of unintended consequences. Last winter, a team of SERC Scholars worked with instructor Leslie Kaelbling, the Panasonic Professor of Computer Science and Engineering, and the 6.036 teaching assistants to infuse weekly labs with material covering ethical computing, data and model bias, and fairness in machine learning. The process was initiated in the fall of 2019 by Jacob Andreas, the X Consortium Assistant Professor in the Department of Electrical Engineering and Computer Science. SERC Scholars collaborate in multidisciplinary teams to help postdocs and faculty develop new course material. Because 6.036 is such a large course, more than 500 students who were enrolled in the 2021 spring term grappled with these ethical dimensions alongside their efforts to learn new computing techniques.

Lucas and his team are experienced Machine Learning engineers and trainers with over 10 years of industry experience in building machine learning and other engineering projects. Lucas enjoys learning and is interested in all things machine learning, data science and cryptocurrency related. He's a keen kaggler and when he's not writing courses or working he's probably to be found hacking an arduino.

Have you ever wondered how the Stock Market, Forex, Cryptocurrency and Online Trading works? Have you ever wanted to become a rich trader having your computers work and make money for you while you're away for a trip in the Maldives? Ever wanted to land a decent job in a brokerage, bank, or any other prestigious financial institution?We have compiled this course for you in order to seize your moment and land your dream job in financial sector. This course covers the advances in the techniques developed for algorithmic trading and financial analysis based on the recent breakthroughs in machine learning. We leverage the classic techniques widely used and applied by financial data scientists to equip you with the necessary concepts and modern tools to reach a common ground with financial professionals and conquer your next interview.By the end of the course, you will gain a solid understanding of financial terminology and methodology and a hands-on experience in designing and building financial machine learning models.

In the world of data mining and analyzing data for business growth, data science is a hot topic of discussion among professionals and organizations. Data analytics courses are in huge demand among the courses for data professionals. Students and working professionals are highly interested to have a strong understanding of different aspects and elements of data science. Students can access multiple virtual data science courses on multiple educational platforms having collaborations with reputed educational institutes. Courses on data science are providing a sufficient and deep understanding of all key concepts and hands-on experience with real-life projects to candidates.

Consider the squeak of a mouse and the low rumble of a lion's roar. In the animal kingdom, bigger animals usually produce lower pitch sounds as a result of their larger larynges and longer vocal tracts. But harbour seals seem to break that rule: they can learn how to change their calls. That means they can deliberately move between lower or higher pitch sounds and make themselves sound bigger than they really are. "The information that is in their calls is not necessarily honest," says Koen de Reus at the Max Planck Institute for Psycholinguistics in Nijmegen, Netherlands.