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0.0 → Introduction to AI and its Evolution
0.1 → Machine Learning vs Deep Learning
0.2 → Tokens VS Parameters in Models
0.3 → What can AI realistically achieve today?
0.4 → 25 Papers That Completely Transformed the Computer World
1.3 → Artificial Neural Network (ANN)
2.1 → Convolutional Neural Networks (CNNs)
2.2 → Recurrent Neural Networks (RNNs)
2.3 → Long Short-Term Memory (LSTM) & (GRU)
2.4 → Encoder-Decoder Architectures and Attention Models
4.1 → Variational Auto-Encoders (VAEs)
4.2 → Generative Adversarial Networks (GANs)
4.3 → Transformers & Language Models
5.1 → Large Language Models (LLMs)
5.5 → How difficult it is to build LLMs?
6.1 → What is Retrieval-Augmented Generation?
6.2 → How does RAG differ from traditional generative models?
6.3 → Steps and Tools to build RAG System
6.5 → Architecture of LangChain
6.6 → How to develop applications using LangChain?
7.1 → What to know on Cloud for LLMs?
7.5 → Operationalizing ML models and challenges
8.2 → How to Build Your Private Language Model?
8.4 → [Case Study] - AI Implementation
9.1 → AI Strategy and Leadership
9.2 → AI Ethics and Societal Implications
9.3 → Framework for AI Product Development
9.8 → [Case Study] - AI Graveyard Companies → Failed
10.3 → AI Certifications or No Certifications
10.4 → 1000+ AI Product Ideas across 21 Industries
11.3 → Career Transition to AI
11.4 → How to get a Job in AI or how to get promoted?
11.5 → How to start your freelance work?
11.6 → Data Engineer VS AI Product Manager, Which profile to choose?
Interaction Event and Community Building
[Checklist] 9 Steps to AI Fundamentals Success
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