Transformer models are the de-facto standard in modern NLP. They have proven themselves as the most expressive, powerful models for language by a large margin, beating all major language-based benchmarks time and time again. In this course, we cover everything you need to get started with building cutting-edge performance NLP applications using transformer models like Google AI's BERT, or Facebook AI's DPR. Throughout each of these use-cases we work through a variety of examples to ensure that what, how, and why transformers are so important. Alongside these sections we also work through two full-size NLP projects, one for sentiment analysis of financial Reddit data, and another covering a fully-fledged open domain question-answering application.

Encouraging women in AI has never been more urgent. A study by the World Economic Forum noted a gender disparity of 78 percent male versus 22 percent female in AI and data science. It reflects a highly nuanced issue that goes beyond any single workplace and if not addressed will have highly negative implications for society. We have seen a lot of work to encourage girls and women to become interested in STEM and address gaps in digital skills at an earlier age than in the past. Yet now, there appears to be less effort to support women as they transition from higher education into a sustainable career in tech.

If you want to learn all the latest 2019 concepts in applying Deep Learning to Computer Vision, look no further - this is the course for you! You'll get hands the following Deep Learning frameworks in Python:

The three little bears strived to get it just right, and AI model builders strive to do the same thing when it comes to specifying their model. Underspecification is when you build a model that performs well on your data, but so do other models, which could lead to your model decaying over time. The discussion of underspecification kicked off last fall when Google researchers published a paper on the subject, "Underspecification Presents Challenges for Credibility in Modern Machine Learning." "ML models often exhibit unexpectedly poor behavior when they are deployed in real-world domains. We identify underspecification as a key reason for these failures," stated the paper, put together by a group of scientists led by author Alexander D'Amour, a research scientist with Google Brain of Cambridge, Mass.

The above given GitHub repository provides an organized list of machine learning libraries, frameworks and tools. Further elaborating, Computer vision libraries include scikit-image, scikit-opt, face_recognition, neural dream and many more, NLP libraries include CLTK and NLTK which helps us to build models that are able to understand human language data, Machine learning libraries include scikit learn, pattern and prophet which was developed by Facebook and is one of the best models for time series data prediction, Data Visualization and analysis libraries include pandas, numpy and many more which are really helpful in modelling and transforming our datasets and finally neural network libraries include neural_talk, nn_builder which can build neural networks in one line. This repository contains instances of the most used and widely used machine learning codes and algorithms implemented using Python explained along with the mathematics and logic working behind them. Also, each algorithm is explained through Jupyter notebook'sinteractive environment. The codes are not only run on a training set for data analysis but also the mathematics is explained which makes it one of the best resources to strengthen one's basics.

This is a rough guide on starting one's journey in Machine Learning. This entails that, if I were to start my Machine Learning journey I would follow this guide. But, different people cope with new information with different methods so one can hollistically choose only necessary steps that they intent to learn based on their experience. The course links that I have attached might be outdated and there might be other updated courses avaialble as well. Please use this guide with a pinch of salt, look at the flow of the guide, and do your own research to learn the concepts catered to your understanding techniques. There are many ongoing competitions also but start basic completed competitions first.

With artificial intelligence course, you will understand the purpose, codes, and the overall platform of artificial intelligence. It will help you in mastering the essential concept related to python programming and machine learning as well. You will be an expert in writing your python script while performing the high-level mathematical and technical computerized work. Artificial intelligence course program is great enough in terms of giving the training on the skills required for becoming a successful artificial intelligence engineer. The exclusive artificial intelligence online course considers mastering deep learning programming language as well as machine learning.

Apply advanced machine learning models to perform sentiment analysis and classify customer reviews such as Amazon Alexa products reviews Understand the theory and intuition behind several machine learning algorithms such as K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression Implement classification algorithms in Scikit-Learn for K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression Build an e-mail spam classifier using Naive Bayes classification Technique Apply machine learning models to Healthcare applications such as Cancer and Kyphosis diseases classification Develop Models to predict customer behavior towards targeted Facebook Ads Classify data using K-Nearest Neighbors, Support Vector Machines (SVM), Decision Trees, Random Forest, Naive Bayes, and Logistic Regression Build an in-store feature to predict customer's size using their features Develop a fraud detection classifier using Machine Learning Techniques Master Python Seaborn library for statistical plots Understand the difference between Machine Learning, Deep Learning and Artificial Intelligence Perform feature engineering and clean your training and testing data to remove outliers Master Python and Scikit-Learn for Data Science and Machine Learning Learn to use Python Matplotlib library for data Plotting Build an in-store feature to predict customer's size using their features Are you ready to master Machine Learning techniques and Kick-off your career as a Data Scientist?! You came to the right place! Machine Learning skill is one of the top skills to acquire in 2019 with an average salary of over $114,000 in the United States according to PayScale! The total number of ML jobs over the past two years has grown around 600 percent and expected to grow even more by 2020. In this course, we are going to provide students with knowledge of key aspects of state-of-the-art classification techniques.

Deep Learning and Machine Learning are one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects. Machine/Deep Learning techniques are widely used in several sectors nowadays such as banking, healthcare, transportation and technology. Machine learning is the study of algorithms that teach computers to learn from experience. Through experience (i.e.: more training data), computers can continuously improve their performance. Deep Learning is a subset of Machine learning that utilizes multi-layer Artificial Neural Networks. Deep Learning is inspired by the human brain and mimics the operation of biological neurons. A hierarchical, deep artificial neural network is formed by connecting multiple artificial neurons in a layered fashion. The more hidden layers added to the network, the more

Neural Ordinary Differential Equations (Neural ODEs) construct the continuous dynamics of hidden units using ordinary differential equations specified by a neural network, demonstrating promising results on many tasks. However, Neural ODEs still do not perform well on image recognition tasks. The possible reason is that the one-hot encoding vector commonly used in Neural ODEs can not provide enough supervised information. We propose a new training based on knowledge distillation to construct more powerful and robust Neural ODEs fitting image recognition tasks. Specially, we model the training of Neural ODEs into a teacher-student learning process, in which we propose ResNets as the teacher model to provide richer supervised information. The experimental results show that the new training manner can improve the classification accuracy of Neural ODEs by 24% on CIFAR10 and 5% on SVHN. In addition, we also quantitatively discuss the effect of both knowledge distillation and time horizon in Neural ODEs on robustness against adversarial examples. The experimental analysis concludes that introducing the knowledge distillation and increasing the time horizon can improve the robustness of Neural ODEs against adversarial examples.