Marketing strategy and AI-Free Webinar from Google

In this session you will learn:

How is AI changing the marketing landscape and what are the opportunities for marketers

AI-powered marketing tools you can try

Supercharging your digital marketing strategy

https://bit.ly/googleaimarketing

#ai #freecourse #marketing
@kdnuggets @datasciencechats @coursenuggets

https://www.kdnuggets.com/collection-of-free-courses-to-learn-data-science-data-engineering-machine-learning-mlops-and-llmops
#freecourses
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https://www.youtube.com/watch?v=wjZofJX0v4M
@kdnuggets @datasciencechats

If you are a fresher important to realise how important data structures and algorithms are in your preparation to get into software journey. Here is a well documented roadmap that you could use with any programming language. If you are looking to expand into datascience then via python 🐍
#beginner #datastructure #python #fresher

Feel free to discuss on practising this in our exclusive python chats channel
https://www.tg-me.com/joinchat-BNEH6lWLXAYvblVPWbb_ag

@kdnuggets @datasciencechats

Did you know we have a linkedin community where useful datascience related posts are shared frequently?

If you havent joined yet please join
https://www.linkedin.com/groups/13661437/

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AI Python for beginners Free course for limited period by Andrew Ng
#ai #python
Join @coursenuggets for more
@kdnuggets @datasciencechats

https://www.deeplearning.ai/short-courses/ai-python-for-beginners/

The AI for Impact Hackathon, presented by Google Cloud and powered by Hack2skill, is a unique opportunity to leverage the transformative power of AI to address pressing social challenges across the APAC region.
You can participate in the hackathon for free with below link.

Deadline: 17th Nov

Register : https://bit.ly/4hEcMQI

Our channel is now on whatsapp too.

Feel free to join. We will be sharing some exclusive content posts on whatsapp and linkedin. So dont miss out.
Whatsapp:🔗
https://whatsapp.com/channel/0029Vaw8loEIXnlogBO5ma1H
Linkedin:🔗
https://www.linkedin.com/groups/13622969

Who all are participating here? If you havent there is still time to participate and get trained for free. Highly recommend if you want to learn genai in short span of time. Are you doing? Comment below.

Those who missed to register the course. You can find all the content of course:
@kdnuggets @coursenuggets
https://www.youtube.com/playlist?list=PLqFaTIg4myu-b1PlxitQdY0UYIbys-2es

Differences between RAG, Agents and Agentic RAG
@kdnuggets @datasciencechats

Subscribe to WhatsApp channel for the post
https://whatsapp.com/channel/0029Vaw8loEIXnlogBO5ma1H/105

Google announced Gemini 2.0, a more advanced AI model capable of native image and audio output and tool use. This new model powers several projects, including Project Astra (a universal AI assistant) and Project Mariner (browser-based task completion). Gemini 2.0 Flash, an experimental version, is available to developers, with wider release planned. Google emphasizes responsible AI development, prioritizing safety and security in its applications. The announcement highlights Gemini 2.0's integration into Google products and its potential to revolutionize user experience
https://blog.google/technology/google-deepmind/google-gemini-ai-update-december-2024/
#gemini
@kdnuggets @datasciencechats

https://blog.google/products/google-cloud/ai-trends-business-2025/
@kdnuggets @datasciencechats
#AINews #Google

Did you know we have some unique learning posts on our WhatsApp channel that doesnt get shared here? Follow our whatsapp channel to keep up with those updates. (Your contact numbers stay anonymous on whatsapp channels)
https://bit.ly/dsnwhatsapp

Building LLMs - Stanford Course
#ai #generativeai #llm

https://www.youtube.com/watch?v=9vM4p9NN0Ts

@kdnuggets @datasciencechats

00:10 Building Large Language Models overview
02:21 Focus on data evaluation and systems in industry over architecture
06:25 Auto regressive language models predict the next word in a sentence.
08:26 Tokenizing text is crucial for language models
12:38 Training a large language model involves using a large corpus of text.
14:49 Tokenization process considerations
18:40 Tokenization improvement in GPT 4 for code understanding
20:31 Perplexity measures model hesitation between tokens
24:18 Comparing outputs and model prompting
26:15 Evaluation of language models can yield different results
30:15 Challenges in training large language models
32:06 Challenges in building large language models
35:57 Collecting real-world data is crucial for large language models
37:53 Challenges in building large language models
41:38 Scaling laws predict performance improvement with more data and larger models
43:33 Relationship between data, parameters, and compute
47:21 Importance of scaling laws in model performance
49:12 Quality of data matters more than architecture and losses in scaling laws
52:54 Inference for large language models is very expensive
54:54 Training large language models is costly
59:12 Post training aligns language models for AI assistant use
1:01:05 Supervised fine-tuning for large language models
1:04:50 Leveraging large language models for data generation and synthesis
1:06:49 Balancing data generation and human input for effective learning
1:10:23 Limitations of human abilities in generating large language models
1:12:12 Training language models to maximize human preference instead of cloning human behaviors.
1:16:06 Training reward model using softmax logits for human preferences.
1:18:02 Modeling optimization and challenges in large language models (LLMs)
1:21:49 Reinforcement learning models and potential benefits
1:23:44 Challenges with using humans for data annotation
1:27:21 LLMs are cost-effective and have better agreement with humans than humans themselves
1:29:12 Perplexity is not calibrated for large language models
1:33:00 Variance in performance of GPT-4 based on prompt specificity
1:34:51 Pre-training data plays a vital role in model initialization
1:38:32 Utilize GPUs efficiently with matrix multiplication
1:40:21 Utilizing 16 bits for faster training in deep learning
1:44:08 Building Large Language Models from scratch

https://cloud.google.com/blog/products/application-development/firebase-studio-lets-you-build-full-stack-ai-apps-with-gemini
#ai

@kdnuggets @datasciencechats