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!

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!

People who wish to advance their career by getting into one of technology's trending fields, data science

Natural Language Processing Explained NLP Tutorial For Beginners - will provide you with a detailed description of NLP (Natural Language Processing). You will also learn about the various applications of NLP in the industry. This Edureka video will provide you with a detailed description of NLP (Natural Language Processing). You will also learn about the various applications of NLP in the industry.

Automation Anywhere is one of the most popular vendors which offers user-friendly and powerful RPA capabilities. Edureka is partnering with Automation Anywhere for this webinar on Decoding futuristic career roles in RPA with Automation Anywhere. The guest speaker for this session would be Arjun Meda who is an RPA evangelist and part of the Bot Store developer relations team. The average salary for an Automation Anywhere Engineer is around $113k per annum – Payscale.com The Robotic Process Automation market is estimated to reach USD 2,467.0 million by 2022, at a CAGR of 30.14% between 2017 and 2022 – MarketsandMarkets.com

In our last post, we demonstrated how to develop a machine learning pipeline and deploy it as a web app using PyCaret and Flask framework in Python. 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 deploy a machine learning pipeline as a web app using the Microsoft Azure Web App Service. In order to deploy a machine learning pipeline on Microsoft Azure, we will have to containerize our pipeline in a software called "Docker".

Deploy Models with TensorFlow Serving and Flask TensorFlow Serving makes the process of taking a model into production easier and faster. Create a web application with Flask to work as an interface to a served model. In this 2-hour long project-based course, you will learn how to deploy TensorFlow models using TensorFlow Serving and Docker, and you will create a simple web application with Flask which will serve as an interface to get predictions from the served TensorFlow model. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser.

To provide awareness of the two most integral branches (i.e. To build appropriate neural models from using state-of-the-art python framework. To build neural models from scratch, following step-by-step instructions. To build end – to – end solutions to resolve real-world problems by using appropriate Machine Learning techniques from a pool of techniques available. To use ML evaluation methodologies to compare and contrast supervised and unsupervised ML algorithms using an established machine learning framework.

In this 1.5 hour long project-based course, you will learn to create and train a Convolutional Neural Network (CNN) with an existing CNN model architecture, and its pre-trained weights. In this 1.5 hour long project-based course, you will learn to create and train a Convolutional Neural Network (CNN) with an existing CNN model architecture, and its pre-trained weights. We will use the MobileNet model architecture along with its weights trained on the popular ImageNet dataset. By using a model with pre-trained weights, and then training just the last layers on a new dataset, we can drastically reduce the training time required to fit the model to the new data . The pre-trained model has already learned to recognize thousands on simple and complex image features, and we are using its output as the input to the last layers that we are training.

Udemy Coupon - Machine Learning A-Z: Hands-On Python & R In Data Science, Learn to create Machine Learning Algorithms in Python and R from two Data Science experts. Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.