Machine Learning

What is GenAI? Generative AI (GenAI) empowers end-users to generate content, such as images and text, quickly and easily. Entrepreneurs are taking advantage of this technology to create a growing number of startups that utilize GenAI models for various aspects of content creation. In the coming year, we can expect to see a proliferation of new products that build on GenAI models like titans GPT-3 and Stable Diffusion. The GenAI renaissance is just beginning and the recent boom in niche end-user applications for this technology is just the tip of the iceberg. These models will serve as the foundation for many future applications ushering in a new GenAI-economy replete with add-ons to existing software and entirely new offerings for end-users. With GenAI, the possibilities for content creation are endless and entrepreneurs are poised to capitalize on this powerful technology to revolutionize the way we create and consume media. ...

Currently, Python is the dominant programming language of data science and machine learning and is popular for more general scripting. It’s pretty awesome compared to its predecessors like C/C++, FORTRAN due to its ease of use, flexibility and readability. Python also has an active and robust library culture after over 30 years of existence. However, Python has some weaknesses that newer languages like Julia, Go and Rust readily address. Pythons Challenge Areas ...

For upskilling on AI/ML, I prefer taking a top-down approach i.e. starting with high level concepts then proceeding to more foundational topics (read: delve more into the theory) . I liked taking the breadth-first approach (rather than a depth-first approach) to initially understand AI/ML. Once I had a solid foundation, I easily pivoted to learning a specific topic, like masked regional CNNs, for building expertise through real-world experience. I took multiple courses and read several authors at the same time. You want to find the right sources of information for you and there are so many options out there, so explore. Sometimes I need to read or watch something a few times from different folks before it just clicks. This ultimately accelerated my learning of concepts. Completion of courses should not be the ultimate goal as some courses cover trivial topics and others go too deep into other topics. ...

Hey Folks! I finally got around to watching a bunch of the talks and found several of the talks useful for improving my Python coding skills in general and/or in the context of doing Data Science. Here are some interesting talks from PyCon 2020: Beautiful Python Refactoring video. The talk was simple but powerful in demonstrating the process of refactoring in Python. Docker and Python: making them play nicely and securely for Data Science and ML. Talk discussed why use Docker in general and for machine learning, and how to manage security and performance while using Docker. It’s a great introduction to Docker - motivating its use case as a Data Scientist. Some tips that she shared include getting standard project templates from cookie cutter data science. Another was to use repo2docker to build a docker based on a local or remote repo. It was also very engaging. Small Big Data: Using Numpy and Pandas When Your Data Doesn’t Fit In Memory. This talk expanded my knowledge of Numpy and Pandas for handling small big data, which happens more than you think. Pro-tip: Watch at ~1.5 speed to save time!

We know that you’ve probably heard of object oriented programming (OOP), but outside of designing games, when is best to use it for data science? We haven’t used OOP much until recently when we refactored a data science project code base. In this article, we give a brief refresher for OOP and discuss our top reasons for using OOP with a few examples. When we code, we generally skew towards more of a functional style of programming, but sometimes we get frustrated with code that is too long. This means that we focus on building our code using functional modules of code and functions. However, at some point, our functions become too large to keep track of and we consider refactoring into classes. ...

Great Email Newsletters on AI/ML All newsletters are released weekly. Import AI - AI newsletter that summarizes recent news articles and research; I enjoy how honest and succinct this newsletter is; also like that the implications of new algorithms are always discussed by Jack, who is an advocate for improved ML model explicability and data privacy. AI News Weekly - curated list of AI-related articles; presents a brief summary of each article describing the latest news, applied use cases and ethics; has ads AI Applied use Cases Top News - curated list of the latest AI news articles; subscribers can customize their newsletters according to topics of interest; weekly The Batch - newsletter from deeplearning.ai more academic with discussion of recent AI research papers; great breakdown of topics e.g. what’s new, why it matters and the implications TOPBOTS - list of various AI articles recently released on TOPBOT.com; articles cover a wide array of topics e.g. NLP, ML in marketing and interpretability; articles tend to be more technical PyImageSearch - articles on using Python to tackle real-world computer vision and deep learning problems; very practical, well-done computer vision blog. I highly recommend Adrian’s free email courses. ODSC - newsletter with articles spanning various topics geared to beginners, experts and everyone in between. They also offer webinars (free and paid) as well as online meetups. Podcasts Data Skeptic has short episodes that cover various ML concepts TWIML AI - Formally This Week in ML & AI is hosted by Sam Charrington who asks really strong questions to the latest and greatest AI/ML researchers. Practical AI - aims to make AI accessible to everyone Find Practical Information on AI/ML ...

I generally reach for Python when building data science pipelines, however I discovered R before I decided to invest in learning Python. R has saved me lots of time when it came to quickly and easily preparing nice-looking plots for research. It begs the question of where is R better than Python for certain purposes? We will discuss the benefits and downsides of Python and R so that we can reach for the appropriate tool when needed, instead of treating all problems like nails when a screwdriver is required. ...

Recommendations systems are good for matching users to their favorite products and are incredibly popular. In fact you have likely used a recommendation system at least once in your life. For example, Amazon uses recommendation systems to suggest new exciting products to purchase based on users’ previous purchase patterns and those similar users. Netflix also utilizes recommendation systems to suggest new TV Shows and movies. Before we get into recommendation systems, it is important to briefly cover two general-purpose approaches for identifying target customer groups and making product recommendations. These two approaches are called Clustering and Association Rules. ...

Feed forward networks cannot learn from the past, but Recurrent Neural Networks (RNNs) can learn by accepting data in a sequence. Examples of applications for RNNs include the text autocomplete feature on your phone and performing language translations. Recurrent Neurons (RNs) act as the building blocks of RNNs. The difference between RNs and feed forward neurons is that RNs accept input x, at time t, as well as a hidden state or output from time t-1 from another RN. The output of a RN is a vector, unlike for a feed forward neuron. RNNs are trained (i.e. their weights are calculated) using backpropagation via Gradient Descent Optimization in time. Output from a single RNN layer at time instance,t is an input to the next layer. Each layer in the RNN represents an instance in time. ...

On Aug 21st, 2020, fastai released a new version of their Practical Deep Learning for Coders -Part 1 course. This course is a must-take for new and intermediate deep learning practitioners. It is well done and teaches you intuition without drowning you in theory. The only prerequisites are some high-school math, and a year of coding experience (preferably in Python). This course is free and can be done without any installation, by taking advantage of the Colab and/or Gradient platforms, which provide free, GPU-powered Python notebooks. Go here to learn more about the course. ...