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Artificial Intelligence (AI) └── Machine Learning (ML) └── Deep Learning (DL) β”œβ”€β”€ Neural Network Architectures β”‚ β”œβ”€β”€ Feedforward Neural Networks (FNN) β”‚ β”œβ”€β”€ Convolutional Neural Networks (CNN) β†’ mainly vision β”‚ β”œβ”€β”€ Recurrent Neural Networks (RNN) β”‚ β”‚ β”œβ”€β”€ LSTM β”‚ β”‚ └── GRU β”‚ β”œβ”€β”€ Autoencoders β”‚ β”œβ”€β”€ Generative Adversarial Networks (GANs) β”‚ └── Transformers β”‚ β”œβ”€β”€ Encoder-only (e.g., BERT-style) β”‚ β”œβ”€β”€ Decoder-only (e.g., GPT-style) β”‚ └── Encoder–Decoder (e.g., T5-style) β”‚ └── Large Language Models (LLMs) β”œβ”€β”€ Built on Transformer architectures β”œβ”€β”€ Pretraining (self-supervised learning) β”œβ”€β”€ Fine-tuning β”‚ β”œβ”€β”€ Supervised Fine-Tuning (SFT) β”‚ β”œβ”€β”€ Instruction Tuning β”‚ └── RLHF (Reinforcement Learning from Human Feedback) β”œβ”€β”€ Inference-time methods β”‚ β”œβ”€β”€ Prompt Engineering β”‚ β”œβ”€β”€ Retrieval-Augmented Generation (RAG) β”‚ └── Tool / Function Calling β”œβ”€β”€ Model Types β”‚ β”œβ”€β”€ Base models β”‚ β”œβ”€β”€ Instruction-tuned models β”‚ └── Multimodal models └── Scaling Dimensions β”œβ”€β”€ Parameters β”œβ”€β”€ Training data └── Compute
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Topic to kickstart the Journey to understand the Deep Learning. Refer the above MindMap.
Topic to kickstart the Journey to understand the Deep Learning. Refer the above MindMap.
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Join @pythonjoyy
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ai_agent_roadmap.html
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*One field. Five career paths. Infinite opportunities.* Credit: geeksforgeeks
*One field. Five career paths. Infinite opportunities.* Credit: geeksforgeeks
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https://medium.com/@prakash.read/artificial-intelligence-explained-for-beginners-319f0053c4fa
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Anthropic dropped the best free masterclass on prompt engineering
Anthropic dropped the best free masterclass on prompt engineering
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