We present LLMSTEP, a tool for integrating a language model into the Lean proof assistant. LLMSTEP is a Lean 4 tactic that sends a user's proof state to a server hosting a language model. The language model generates suggestions, which are checked in Lean and displayed to a user in their development environment. We provide a baseline language model, along with code for fine-tuning and evaluation to support further development. We provide server implementations that run on CPU, a CUDA GPU, or a Google Colab notebook, as a step towards fast, effective language model suggestions for any user.

From sifting through Twitter data to making your own Minecraft modifications, Python is one of the most versatile programming languages at a coder's disposal. The open-source, object-oriented language is also quickly becoming one of the most-used languages in data science. According to the Association for Computing Machinery, Python is now the most popular introductory language at universities in the United States. To help readers practice the Python fundamentals, datascience@berkeley gathered six coding problems, including some from the W200: Introduction to Data Science Programming course. Consider the following questions to make sure you have the proper prior knowledge and coding environment to continue.

This course focuses on using state-of-the-art Natural Language processing techniques to solve the problem of question generation in edtech. If we pick up any middle school textbook, at the end of every chapter we see assessment questions like MCQs, True/False questions, Fill-in-the-blanks, Match the following, etc. In this course, we will see how we can take any text content and generate these assessment questions using NLP techniques. This course will be a very practical use case of NLP where we put basic algorithms like word vectors (word2vec, Glove, etc) to recent advancements like BERT, openAI GPT-2, and T5 transformers to real-world use. We will use NLP libraries like Spacy, NLTK, AllenNLP, HuggingFace transformers, etc.

Google Colaboratory, often referred to as colab, is a product created by Google to allow anyone to create and run python code in the browser. It has many standard machine and data science libraries built-in including pandas and scikit-learn. You can also install practically any other python library for use in each notebook. To access colab you need to sign up for a Google account and this then gives you free access to the notebook environment and computing resources that include GPU's. Let's walk through a quick demo.

This course focuses on using state-of-the-art Natural Language processing techniques to solve the problem of question generation in edtech. If we pick up any middle school textbook, at the end of every chapter we see assessment questions like MCQs, True/False questions, Fill-in-the-blanks, Match the following, etc. In this course, we will see how we can take any text content and generate these assessment questions using NLP techniques. This course will be a very practical use case of NLP where we put basic algorithms like word vectors (word2vec, Glove, etc) to recent advancements like BERT, openAI GPT-2, and T5 transformers to real-world use. We will use NLP libraries like Spacy, NLTK, AllenNLP, HuggingFace transformers, etc.

Gartner estimates that by 2022, 40% of AI/ML models will be trained on synthetic data. Indeed, synthetic data is more and more popular -- I see this every day, working at a synthetic data company. If you feel like it's time for you to pick up some synthetic data skills, please read on. I will tell you about the basics and introduce you to a cool open-source tool you will find handy going forward. Synthetic data is created with a synthetic data generator.

Colaboratory, or Colab for short, is a Google Research product, which allows developers to write and execute Python code through their browser. Google Colab is an excellent tool for deep learning tasks. It is a hosted Jupyter notebook that requires no setup and has an excellent free version, which gives free access to Google computing resources such as GPUs and TPUs. Since Google Colab is built on top of vanilla Jupyter Notebook, which is built on top of Python kernel, let's look at these technologies before diving into why we should and how we can use Google Colab. There are several tools used in Python interactive programming environments.

Step by step instructions to train Yolo-v5 & do Inference(from ultralytics) to count the blood cells and localize them. I vividly remember that I tried to do an object detection model to count the RBC, WBC, and platelets on microscopic blood-smeared images using Yolo v3-v4, but I couldn't get as much as accuracy I wanted and the model never made it to production. Now recently I came across the release of the Yolo-v5 model from Ultralytics, which is built using PyTorch. I was a bit skeptical to start, owing to my previous failures, but after reading the manual in their Github repo, I was very confident this time and I wanted to give it a shot. And it worked like a charm, Yolo-v5 is easy to train and easy to do inference.

Lately, posts and tutorials about new deep learning architectures and training strategies have dominated the community. However, one very interesting research area, namely few-shot learning, is not getting the attention it deserves. If we want widespread adoption of ML we need to find ways to train them efficiently, with little data and code. In this tutorial, we will go through a Google Colab Notebook to train an image classification model using only 5 labeled samples per class. Using only 5 exemplary samples is also called 5-shot learning.

Step by step instructions to train Yolo-v5 & do Inference(from ultralytics) to count the blood cells and localize them. I vividly remember that I tried to do an object detection model to count the RBC, WBC, and platelets on microscopic blood-smeared images using Yolo v3-v4, but I couldn't get as much as accuracy I wanted and the model never made it to the production. Now recently I came across the release of the Yolo-v5 model from Ultralytics, which is built using PyTorch. I was a bit skeptical to start, owing to my previous failures, but after reading the manual in their Github repo, I was very confident this time and I wanted to give it a shot. And it worked like a charm, Yolo-v5 is easy to train and easy to do inference.