These are the lecture notes for FAU's YouTube Lecture "Deep Learning". This is a full transcript of the lecture video & matching slides. We hope, you enjoy this as much as the videos. Of course, this transcript was created with deep learning techniques largely automatically and only minor manual modifications were performed. If you spot mistakes, please let us know!

BESTSELLER Created by Ankit Mistry, Vijay Gadhave, Data Science & Machine Learning Academy English English [Auto] PREVIEW THIS COURSE - GET COUPON CODE Description Recent reviews: "Very practical and interesting, Loved the course material, organization and presentation. Thank you so much" "This is the best course to learn NLP from the basic. According to statista dot com which field of AI is predicted to reach $43 billion by 2025? If answer is'Natural Language Processing', You are at right place. How Android speech recognition recognize your voice with such high accuracy.

For all the advances enabled by artificial intelligence, from speech recognition to self-driving cars, AI systems consume a lot of power and can generate high volumes of climate-changing carbon emissions. A study last year found that training an off-the-shelf AI language-processing system produced 1,400 pounds of emissions--about the amount produced by flying one person roundtrip between New York and San Francisco. The full suite of experiments needed to build and train that AI language system from scratch can generate even more: up to 78,000 pounds, depending on the source of power. But there are ways to make machine learning cleaner and greener, a movement that has been called "Green AI." Some algorithms are less power-hungry than others, for example, and many training sessions can be moved to remote locations that get most of their power from renewable sources.

Free Coupon Discount Preview this course Introduction to AI, Machine Learning and Python basics, Learn to understand Artificial Intelligence and Machine Learning algorithms, and learn the basics of Python Programming

There's a couple of reasons why you might want to feature selection. One is to avoid overfitting and get a better model. In practice, I have rarely seen that happen. It's not usually what I would try to do to increase performance. If I'm only interested in performance I probably would not try to do automatic feature selection unless I think only a very small subset of my feature is actually important.

Supervised, semi-supervised or unsupervised deep learning is part of a broader family of machine learning methods, that teach you the basics of neural networks. Learn from the Top 10 Deep Learning Courses curated exclusively by Analytics Insight and build your deep learning models with Python and NumPy. Taught by one of the best Data Science experts of 2020 Andrew Ng, this course teaches you how to build a successful machine learning project. You will understand the complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance. Over 20 videos spread across the entire module will explain you error analysis and different kind of the learning techniques.

In our last post on deploying a machine learning pipeline in the cloud, we demonstrated how to develop a machine learning pipeline in PyCaret, containerize it with Docker and serve it as a web application using Google Kubernetes Engine. If you haven't heard about PyCaret before, please read this announcement to learn more. In this tutorial, we will use the same machine learning pipeline and Flask app that we built and deployed previously. This time we will demonstrate how to containerize and deploy a machine learning pipeline serverless using AWS Fargate. This tutorial will cover the entire workflow starting from building a docker image locally, uploading it onto Amazon Elastic Container Registry, creating a cluster and then defining and executing task using AWS-managed infrastructure i.e.

You will learn both Python and R Programming with Data Science in this course. Python: You will first learn how to Install Anaconda and Jupyter on your desktop/laptop Python: You will understand and learn the basics of For Loops and Advanced For Loops. You will have clarity on Python generators and will master the flow of your code using "If Else" Python: You will understand Why foundations Modify Lists and Dictionaries and Functions. Learn how to analyze, retrieve and clean data with Python Python: Learn Concatenation (Combining Tables) with Python and Pandas and Manipulating Time and Date Data with Python Datetime Python: You will learn to Use Pandas with Large Data Sets, Time Series Analysis and Effective Data Visualization in Python R: You will learn the most important tools in R that will allow you to do data science R: You will have the tools to tackle a wide variety of data science challenges, using the best parts of R. R: You will learn how to Tidy the data. Tidying your data means storing it in a consistent form that matches the semantics of the dataset with the way it is stored.

Dr. Andrew Ng is a globally recognized leader in artificial intelligence. He was until recently chief scientist at Baidu, where he led the company's approximately 1,300-person AI group and was responsible for driving its global AI strategy and infrastructure. He was also the founding lead of the Google Brain team. In addition, Dr. Ng is co-chairman and cofounder of Coursera, the world's leading MOOC (massive open online course) platform, and an adjunct professor of computer science at Stanford University. He has authored or coauthored over 100 research papers in machine learning, robotics, and related fields.

As we near the end of our summer posts, we've started to think more broadly about statistics as well as data science courses. Today's post considers a broad question relevant for many courses: how can we teach statistical thinking without having to resort to introducing a profusion of tests? Jonas Kristoffer Lindeløv proposed an elegant approach using the idea that common statistical tests are linear models. Cheatsheets in R and Python describe how standard statistical tests (e.g., the one-sample t-test) can be undertaken using the lm() and glm() functions. The results are fully equivalent.