Learning Python!!👨🏻‍💻

Artificial Intelligence (AI) └── Machine Learning (ML) └── Deep Learning (DL) ├── Neural Network Architectures │ ├── Feedforward Neural Networks (FNN) │ ├── Convolutional Neural Networks (CNN) → mainly vision │ ├── Recurrent Neural Networks (RNN) │ │ ├── LSTM │ │ └── GRU │ ├── Autoencoders │ ├── Generative Adversarial Networks (GANs) │ └── Transformers │ ├── Encoder-only (e.g., BERT-style) │ ├── Decoder-only (e.g., GPT-style) │ └── Encoder–Decoder (e.g., T5-style) │ └── Large Language Models (LLMs) ├── Built on Transformer architectures ├── Pretraining (self-supervised learning) ├── Fine-tuning │ ├── Supervised Fine-Tuning (SFT) │ ├── Instruction Tuning │ └── RLHF (Reinforcement Learning from Human Feedback) ├── Inference-time methods │ ├── Prompt Engineering │ ├── Retrieval-Augmented Generation (RAG) │ └── Tool / Function Calling ├── Model Types │ ├── Base models │ ├── Instruction-tuned models │ └── Multimodal models └── Scaling Dimensions ├── Parameters ├── Training data └── Compute

Topic to kickstart the Journey to understand the Deep Learning. Refer the above MindMap.

*One field. Five career paths. Infinite opportunities.* Credit: geeksforgeeks

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