Generative AI Live_Classes – Learn the Magic Behind Machines that Create!

Comprehensive Roadmap For Deep Learning and GEN-AI

Basic Mathematics for Deep Learning
1 Linear Algebra (Matrices, Vectors, Eigenvalues)
2 Probability & Statistics (Bayes Theorem, Gaussian Distribution)
3 Calculus (Derivatives, Partial Derivatives, Chain Rule)

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Fundamentals of Neural Networks & Deep Learning
1 What is a Neural Network?
2 Artificial Neural Networks (ANN/MLP)
3 Backpropagation & Gradient Descent
4 Dropout & Batch Normalization
5 Optimization Techniques (Adam, RMSProp, SGD)

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Natural Language Processing (NLP)
1 NLP Basics & Text Preprocessing
2 Types of Embeddings:
  2.1 Word2Vec (CBOW, Skip-gram)
  2.2 TF-IDF, Bag of Words (BoW), N-grams
  2.3 FastText, Sentence Embeddings
3 Recurrent Neural Networks (RNN) & Their Limitations
4 Long Short-Term Memory (LSTM) & Gated Recurrent Units (GRU)

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Transformer Models & Foundation Models
1 Encoder-Decoder Architecture
2 "Attention Is All You Need" Paper (The Birth of Transformers)
3 Transformer Architecture:
  3.1 Self-Attention & Multi-Head Attention
  3.2 Positional Encoding & Feedforward Layers
4 Foundation Models: Understanding Their Scaling & Performance

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Generative AI Models
1 Transformer-based LLMs (GPT, LLaMA)
  1.1 GPT Series (GPT-3, GPT-4)
  1.2 LLaMA (Meta AI) + Fine-tuning (Alpaca/Vicuna)
  1.3 T5 (Text-to-Text Transfer Transformer) or FLAN-T5
  1.4 InstructGPT (RLHF-based Fine-Tuning)

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Fine-tuning & Customization of LLMs
1 Supervised Fine-tuning vs. In-Context Learning
2 Low-Rank Adaptation (LoRA) & PEFT
3 Reinforcement Learning from Human Feedback (RLHF)
4 Instruction Tuning & Preference Learning

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Retrieval-Augmented Generation (RAG) & AI Agents
1 What is RAG & Why It Matters
2 Vector Databases (FAISS, Pinecone, Weaviate)
3 Hybrid Search (BM25 + Dense Retrieval)
4 Multi-modal RAG (Text + Images + Audio)
5 Autonomous AI Agents (LangChain, AutoGPT, BabyAGI)

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Prompt Engineering & Optimization
1 Zero-shot, Few-shot, Chain-of-Thought (CoT) Prompting
2 Function Calling & Structured Outputs
3 Jailbreaking & Defensive Prompting
4 Context Length & Efficient Tokenization

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Evaluation & Benchmarking of AI Systems
1 Evaluation Methodologies (MMLU, HELM, BIG-Bench)
2 Language Model Metrics (Perplexity, BLEU, ROUGE)
3 Hallucination Detection & Bias Mitigation
4 AI as a Judge & Comparative Evaluation

 

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Research & Staying Updated
1 Reading & Implementing Research Papers 
2 Hands-on with Hugging Face, OpenAI API, LangChain, LlamaIndex
3 Participating in AI Hackathons & Kaggle Competitions

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