Are you interested in learning the foundations to a successful data science career? Or are you looking to brush up on your maths, or strengthen your understanding by extending that base? This is a selection of maths courses, collections of courses, and specializations which are freely available online, and which can help achieve your data science mathematics goals. They have been separated into the broad topics of mathematical foundations, algebra, calculus, statistics & probability, and those especially relevant to data science & machine learning. Take a look at the list and closer inspect those which may be of interest to you.

Link: Teach a machine to learn to recognize images, Audio & Poses udemy couponed code This course model will teach you how to teach, train and create Machine Learning models faster, easier without writing a single line of code.by Kelvin Fosu What you'll learn Students will be excited about how machine learning works while they work on projects like images, sound and poses Students will learn to host their Machine Learning model for free Program a Machine Learning model without writing a single line of code. Students will learn to generate real TensorFlow format of their models to be used in websites, apps etc Description This course model will teach you how to teach, train and create Machine Learning models faster, easier without writing a single line of code. Just come as you are and leave with some understanding of Machine Learning. No prior knowledge is required.

The 2020 Designcon conference included many talks and exhibits with a storage and memory focus. Both Rambus and Teledyne LeCroy had tutorials on design and connectivity for leading edge electronic components and systems as well as testing memory systems. This piece will look at some material from the tutorials and exhibits that can inform us about disaggregated processing developments, high speed chip to chip interfaces and memory for AI applications. Rambus said that there are multiple drivers to disaggregate semiconductor die, often called die disaggregation or chiplets. Creating specialized chips used with other chips in a package allows the introduction of specialized technologies (such as co-packaged optical systems) as well as better cooling solutions and greater in-package memory.

When you hear the term'Artificial Intelligence,' what comes to your mind? People often say that Artificial Intelligence (AI) is the next big thing, but do you understand what do they mean by it? AI technology has become an essential part of our daily lives. From virtual assistants to chatbots, every application being developed today uses the concept of AI. Due to the advancements happening in organizations with the help of this technology, worldwide, AI has also gained a lot of popularity in recent years, and it will only excel in the coming future.

In it you will find popular algorithms and architectural solutions that are intuitive to any novice developer. The book covers important topics: from learning neural networks, language processing, feedback neural networks, to mathematical prerequisites and the history of artificial intelligence.

How can you share a deep learning model you trained that is giving awesome results with other team members working in a different part of the world or How to save a deep learning model and it's trained weights during or after training or How to resume training of a model from where you left off? Sharing the saved files will allow others to recreate the model to make inferences or to resume further training from you left off. The model can be recreated by loading the model, and it's trained weight from the saved files containing the model architecture and the pre-trained weights. In this article, you will learn how to save a deep learning model developed in Keras to JSON or YAML file format and then reload the model. To save the model, we first create a basic deep learning model.

This course is a practical introduction to natural language processing with TensorFlow 2.0. In this tutorial you will go from having zero knowledge to writing an artificial intelligence that can compose Shakespearean prose. No prior experience with deep learning is required, though it is always helpful to have more background information. We'll use a combination of embedding layers, recurrent neural networks, and fully connected layers to perform the classification. Course Contents (01:16) Getting Started with Word Embeddings (33:25) How to Perform Sentiment Analysis on Movie Reviews (59:32) Let's Write An AI That Writes Shakespeare Course Description The basic idea behind natural language processing is that we start out with words, i.e. strings of characters, that are almost impossible for the computer to meaningfully parse.

Gaby Ecanow loves listening to music, but never considered writing her own until taking 6.S191 (Introduction to Deep Learning). By her second class, the second-year MIT student had composed an original Irish folk song with the help of a recurrent neural network, and was considering how to adapt the model to create her own Louis the Child-inspired dance beats. "It was cool," she says. "It didn't sound at all like a machine had made it." This year, 6.S191 kicked off as usual, with students spilling into the aisles of Stata Center's Kirsch Auditorium during Independent Activities Period (IAP).

Learn how to test & monitor production machine learning models. You've taken your model from a Jupyter notebook and rewritten it in your production system. Are you sure there weren't any mistakes when you moved from the research environment to the production system? How can you control the risk before your deployment? ML-specific unit, integration and differential tests can help you to minimize the risk.