Introductory hands-on courses such as our smartphone-based coding courses, SuaCode require a lot of support for students to accomplish learning goals. Online environments make it even more difficult to get assistance especially more recently because of COVID-19. Given the multilingual context of our students (learners across 38 African countries), in this work, we developed an AI Teaching Assistant (Kwame) that provides answers to students' coding questions from our SuaCode courses in English and French. Kwame is a Sentence-BERT(SBERT)-based question-answering (QA) system that we trained and evaluated using question-answer pairs created from our course's quizzes and students' questions in past cohorts. It finds the paragraph most semantically similar to the question via cosine similarity. We compared the system with TF-IDF and Universal Sentence Encoder. Our results showed that SBERT performed the worst for the duration of 6 secs per question but the best for accuracy and fine-tuning on our course data improved the result.

Learning has been essential to our existence. And, continuous learning helps an individual avoid stagnation in any profession and ensures that one keeps on moving forward towards reaching his goal and potential. The same also goes for machine models that are backed by AI's machine learning algorithm. Continual learning, also called lifelong learning or online machine learning, is a fundamental idea in machine learning in which models continuously learn and evolve based on the input of increasing amounts of data while retaining previously learned knowledge. In practice, this refers to supporting a model's ability to autonomously learn and adapt in production as new data comes in.

ImageDataGenerator is Keras's go-to class for pipelining image data for deep learning. It allows easy access to your local file-system and multiple different methods for loading in data from different structures. It also has some pretty powerful data pre-processing and augmentation capabilities. For the purposes of this tutorial, we won't be doing much data augmentation, we will primarily be focusing on the different methods for reading data in using ImageDataGenerator. If you already have your own image data simply need a quick tutorial on a single method, review the'Methods and use-cases' section, and continue down to the appropriate tutorial.

It's been nearly 4 years since Tensorflow was released, and the library has evolved to its official second version. Tensorflow is Google's library for deep learning and artificial intelligence. Tensorflow is the world's most popular library for deep learning, and it's built by Google, whose parent Alphabet recently became the most cash-rich company in the world (just a few days before I wrote this). It is the library of choice for many companies doing AI and machine learning. In other words, if you want to do deep learning, you gotta know Tensorflow.

We also especially encourage students from underrepresented minorities to participate. Hands-on programming labs are a core part of our curriculum, so having some programming knowledge (specifically Python) will help participants get more out of the workshop. However, programming knowledge is not required; the workshop will include a track for participants who are completely new to programming. Experience with typical undergraduate math (calculus, linear algebra) and statistics (intro probability) is also helpful, but not required. The workshop will be run on Eastern Time, though students from outside this timezone are welcome to apply.

Robots trained with reinforcement learning (RL) have the potential to be used across a huge variety of challenging real world problems. To apply RL to a new problem, you typically set up the environment, define a reward function, and train the robot to solve the task by allowing it to explore the new environment from scratch. While this may eventually work, these "online" RL methods are data hungry and repeating this data inefficient process for every new problem makes it difficult to apply online RL to real world robotics problems. What if instead of repeating the data collection and learning process from scratch every time, we were able to reuse data across multiple problems or experiments? By doing so, we could greatly reduce the burden of data collection with every new problem that is encountered.

Google Data Studio (GDS) is a free dashboard and reporting tool (which lives in the cloud). It allows you to create dynamic, collaborative reports, and visualization dashboards. Paid Business Intelligence and Data Analytics Tools Like Tableau Are have either plateaued or will plateau soon. Many of these are either too expensive for small or teams or have a steep learning curve for beginners. This course helps you start with GDS and become proficient in producing powerful visualizations and reports.4.5/5

Computer vision has many uses. It can recognise faces, it can be used in quality control and security and it can also recognise very successfully different object on the image. Today we will look at the last example. We will build a supervised machine learning model to recognise cats and dogs on the image using Neural Networks. You will learn how to create and configure a Convolutional Neural Network (CNN).

Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve.

Proof assistants offer tactics to apply proof by induction, but these tactics rely on inputs given by human engineers. We address this problem with SeLFiE, a domain-specific language to encode experienced users' expertise on how to apply the induct tactic in Isabelle/HOL: when we apply an induction heuristic written in SeLFiE to an inductive problem and arguments to the induct tactic, the SeLFiE interpreter examines both the syntactic structure of the problem and semantics of the relevant constants to judge whether the arguments to the induct tactic are plausible for that problem according to the heuristic.