Artificial Intelligence

2024-02-01 16:32:13 StackOverflow is a dinosaur that's going extinct.

I mostly use Copilot, ChatGPT, & Perplexity. I haven't found a single reason to visit StackOverflow anymore.

StackOverflow can't compete, & I don't see how they can stay relevant any longer.

I use Copilot for inline suggestions. As I type, Copilot takes care of the little things. It saves me from dozens of searches every day.

ChatGPT is the workhorse. I use it to solve more complex tasks with my code. Here are some examples:

• Explain what this code does
• Simplify it
• Rewrite it in a more efficient way
• Rewrite it in a more readable way
• Replace the use of a library with another
• Write documentation for it
• Describe potential edge cases
• Write unit tests for those edge cases

I use Perplexity to ask questions. Google is broken. If you don't believe me, try Perplexity for a day.

(Google is still king as a navigation tool, I never type a complete URL in my browser)

Modern AI-powered tools are replacing boomer tech.

StackOverflow is dead to me. Open / Comment

2024-01-29 15:09:18 Artificial Intelligence Bootcamp II

Announcing Cohort II

+90 hrs sessions.
+26 Weeks.
+19 Tools & Technology.
+9 Case studies.
+9 Projects.
+9 Skills.
+7 Domains.
+13 Homework Assignments.

Duration: 6 months (Starting from 3rd Feb)

Learn Artificial intelligence and Data Science with FAANG working professionals and IIT, BITS, COEP Alumni.

Starting from the basics.
Get Certificate.
Live Sessions. (Zoom)
Weekend sessions.

To attend the 1st Session: https://chat.whatsapp.com/BibIwuuUEWrGEWdZHYluNe

For registrations & FAQs: https://aiindia.ai/ai-bootcamp/ Open / Comment

2024-01-19 17:32:47 Excited to share with you some FREE but very useful NEW AI Courses!

These are great for anyone who wants to dig deeper into the topic this year.

𝟭. Prompt Engineering Basics: https://explore.skillbuilder.aws/learn/course/external/view/elearning/17763/foundations-of-prompt-engineering

𝟮. ChatGPT Prompts Mastery: https://www.deeplearning.ai/short-courses/chatgpt-prompt-engineering-for-developers/

𝟯. Intro to Generative AI: https://www.cloudskillsboost.google/course_templates/536

𝟰. AI Introduction by Harvard: https://pll.harvard.edu/course/cs50s-introduction-artificial-intelligence-python/2023-05

𝟱. Microsoft GenAI Basics: https://www.linkedin.com/learning/what-is-generative-ai/generative-ai-is-a-tool-in-service-of-humanity

𝟲. Prompt Engineering Pro: https://learnprompting.org

𝟳. Google's Ethical AI: https://www.cloudskillsboost.google/course_templates/554

𝟴. Harvard Machine Learning: https://pll.harvard.edu/course/data-science-machine-learning

𝟵. LangChain App Developer: https://www.deeplearning.ai/short-courses/langchain-for-llm-application-development/

𝟭𝟬. Bing Chat Applications: https://www.linkedin.com/learning/streamlining-your-work-with-copilot-formerly-bing-chat-bing-chat-enterprise/put-your-fingers-to-work-chatting-as-a-productivity-tool

𝟭𝟭. Generative AI by Microsoft: https://learn.microsoft.com/en-us/training/paths/introduction-generative-ai/

𝟭𝟮. Amazon's AI Strategy: https://explore.skillbuilder.aws/learn/public/learning_plan/view/1909/generative-ai-learning-plan-for-decision-makers

𝟭𝟯. GenAI for Everyone: https://www.deeplearning.ai/courses/generative-ai-for-everyone/

𝟭𝟰. AWS GenAI Foundation: https://www.coursera.org/learn/generative-ai-with-llms

𝗕𝗼𝗻𝘂𝘀:

• OpenCV Bootcamp: https://opencv.org/university/free-opencv-course/

• Tensorflow Bootcamp: https://opencv.org/university/free-tensorflow-keras-course/ Open / Comment

2024-01-17 16:34:01 This is a class from Harvard University:

"Introduction to Data Science with Python."

You can take this class for free. If you want the certificate, you can pay $299 for it. You should be familiar with Python to take this course.

The course is for beginners. It's for those who want to build a fundamental understanding of machine learning and artificial intelligence.

The course will cover some of these topics:
• Generalization and overfitting
• Model building, regularization, and evaluation
• Linear and logistic regression models
• k-Nearest Neighbor
• Scikit-Learn, NumPy, Pandas, and Matplotlib

This course is perfect if you are a software developer with experience writing Python code and want to start with Machine Learning.
Link: https://pll.harvard.edu/course/introduction-data-science-python Open / Comment

2024-01-11 17:56:23 I teach hard-core Machine Learning Engineering.

Artificial Intelligence changed my life forever. There has never been a better time to build a career that will set you apart for the next 20-30 years.

I teach a program where I show people how to build Machine Learning systems.

My program is not an online course. It's not a group of videos you watch and a PDF you read.

My program is a 90-hour live class with an additional 13 Assignments and 9 projects material. It's tough.

While everyone wants to know what will happen in the next ten years, we won't waste time trying to predict that. Instead, we focus on what never changes.

The program is about the fundamental principles of building machine learning systems. It's about timeless ideas that will help you understand the future, whatever that is.

My guarantee is simple: you'll learn more than you've ever done before.

• Cohort #1 starts this Saturday on 13th January

You can join here: www.Aiindia.ai/bootcamp.

Some of the most frequently asked questions:

How much do I have to pay?

The cost to join is a one-time payment of ₹30k and for students ₹10k There are no recurrent payments. Once you join, you get lifetime access to all materials and a community of engineers who went through the program.

Is every class live, or can I watch them offline?

Classes are live, but you can watch the recordings at your own pace.

What are the prerequisites to join?

Ideally, you don't need any prerequisite to join our program only required is your dedication and commitment. You don't need a machine learning experience to learn.

What are some of the topics you'll cover?

We cover a lot, but here are 10 of the most important topics we'll discuss in class:

1. Framing machine learning problems
2. How to fine tune models and transfer learning.
3. Processing, training, deploying, inference pipelines
4. Offline evaluation and testing in production
5. Performing error analysis. Where to work next
6. Distributed training. Data and model parallelism
7. Pruning, quantization, and knowledge distillation
8. Model deployment. Online and batch inference
9. LLMs basics with training.
10. Python programming from scratch.

Here is the link to join: www.aiindia.ai/bootcamp Open / Comment

2024-01-07 16:34:01 Here are 300 hours of curated courses focused on Machine Learning Engineering.

There are 15 courses.
From beginner to advanced.
From Google.
For free.

Some of the topics they cover:

• Fundamentals of Machine Learning
• Feature Engineering
• Production Machine Learning Systems
• Computer Vision and Natural Language
• Recommendation Systems
• MLOps
• TensorFlow, Google Cloud, VertexAI

The courses are well structured. They aren't just links to YouTube videos. You have to join the course, and they have an interface that takes you through every module.

This is good content. And it's free. https://www.cloudskillsboost.google/paths/17 Open / Comment

2024-01-04 17:31:39 AI wishlist for this new year Open / Comment

2023-12-31 17:16:01 Dear Community,

As the year draws to a close, I would like to express gratitude for being a part of the Ai India community dedicated to expanding AI education.

My prediction for 2024

1. 1B models will outperform 70B models.

2. Models will be deployed on CPUs for almost free. Not API services.

3. Data quality will yield the next 10x boost in performance.

4. A combination of open source models will beat the best private models.

5. Compilers will unlock at least 80% speed up in models (both training and inference)

6. Legislation will side with content creators over model developers.

7. Local ML is going to be huge. It will be in part driven by the adoption of Apple Silicon and other innovative hardware, but also on raw CPU and mobile devices

8. In many cases except for the largest of LLMs, local inference will become a viable alternative to hosted inference.

Join WhatsApp Channel

Happy New Year 2024 Open / Comment

2023-12-29 14:22:46 Open / Comment

2023-12-25 17:32:03 Data scientists spend 80% of their time working on the data.

Books spend 80% of their time talking about algorithms.

Today, there's a large gap between academia and reality. Between what they say is important, and what really is.

Better data is better than better models. Open / Comment