Modern AI Learning Roadmap 2025 | Generated by AI
Excellent — let’s make a modern 2025 roadmap that takes you from core ML/DL foundations to frontier AI systems like GPT-4/5, DeepSeek, Qwen, Sora, and multi-modal agents.
This roadmap is organized in 7 progressive layers, each building toward the next. I’ll avoid table formatting for readability.
1. Mathematical and Programming Foundations
Goal: Build intuition and skill to read and implement ML research.
Topics
- Linear algebra (vectors, matrices, eigen decomposition)
- Calculus (partial derivatives, chain rule)
- Probability and statistics (Bayes theorem, distributions)
- Optimization (gradient descent, convex vs non-convex)
- Python, NumPy, PyTorch basics
Recommended Path
- “Mathematics for Machine Learning” (Deisenroth)
- 3Blue1Brown’s Essence of Linear Algebra & Calculus
- Fast.ai Practical Deep Learning for Coders
- Implement logistic regression, softmax regression, and basic backprop from scratch
2. Classical Machine Learning
Goal: Understand the algorithms that preceded deep learning and are still core to data modeling.
Key Concepts
- Supervised vs unsupervised learning
- Decision trees, random forests, SVMs
- K-means, PCA, t-SNE
- Regularization (L1/L2)
- Evaluation metrics (accuracy, precision, recall, AUC)
Practice
- Use scikit-learn for small datasets
- Explore Kaggle competitions to gain intuition
3. Deep Learning Core
Goal: Master neural networks and training mechanics.
Concepts
- Feedforward networks (DNNs)
- Backpropagation, loss functions
- Activation functions (ReLU, GELU)
- BatchNorm, Dropout
- Optimizers (SGD, Adam, RMSProp)
- Overfitting and generalization
Projects
- Build an MLP to classify MNIST and CIFAR-10
- Visualize training curves and experiment with hyperparameters
4. Convolutional & Recurrent Models (CNN, RNN, LSTM, Transformer)
Goal: Understand architectures that power perception and sequence modeling.
Study
- CNNs: convolution, pooling, padding, stride
- RNNs/LSTMs: sequence learning, attention
- Transformers: attention mechanism, positional encoding, encoder-decoder
Projects
- Implement a CNN for image classification (e.g., ResNet)
- Implement a transformer for text (e.g., translation on a small dataset)
- Read “Attention Is All You Need” (2017)
5. Modern NLP and Foundation Models (BERT → GPT → Qwen → DeepSeek)
Goal: Understand how transformers evolved into massive language models.
Learn in Sequence
- BERT (2018): Bidirectional encoder, pretraining (MLM, NSP)
- GPT series (2018–2025): Decoder-only transformers, causal masking, instruction tuning
- Qwen & DeepSeek: Chinese-led open LLM families; architecture scaling, MoE (Mixture of Experts), training on bilingual corpora
- RLHF (Reinforcement Learning from Human Feedback): Core of instruction following
- PEFT, LoRA, quantization: Efficient fine-tuning and deployment
Projects
- Use Hugging Face Transformers
- Fine-tune a small model (e.g., Llama-3-8B, Qwen-2.5)
- Study open training recipes from DeepSeek and Mistral
6. Multimodal and Generative Systems (Sora, Gemini, Claude 3, etc.)
Goal: Move beyond text — integrate vision, audio, and video.
Concepts
- Vision transformers (ViT, CLIP)
- Diffusion models (Stable Diffusion, Imagen)
- Video generation (Sora, Pika, Runway)
- Audio & speech (Whisper, MusicGen)
- Unified multimodal architectures (Gemini 1.5, GPT-4o)
Practice
- Experiment with CLIP + diffusion pipelines
- Study OpenAI’s Sora architecture overview (video diffusion + transformer)
- Implement image captioning or text-to-image demo using pretrained models
7. AI Agents and Systems
Goal: Learn how models are connected into reasoning and tool-using agents.
Key Ideas
- Prompt engineering and reasoning chains
- Memory and planning (ReAct, Tree-of-Thought, AutoGPT, BabyAGI)
- Retrieval-augmented generation (RAG)
- Tool use and APIs (function calling)
- Multi-agent systems and orchestration (LangChain, LlamaIndex, CrewAI)
Projects
- Build a local agent that uses RAG + external APIs
- Create a multi-agent collaboration demo (e.g., planner + executor)
8. Latest Research & Staying Current
Goal: Keep learning at the frontier.
Habits
- Follow Arxiv-sanity, Papers with Code, and Hugging Face Daily Papers
- Watch labs: OpenAI, DeepSeek, Qwen, Anthropic, Google DeepMind, Meta FAIR
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Read papers on topics like:
- Efficient inference (FlashAttention-3, speculative decoding)
- Agent alignment & evaluation
- Multimodal understanding and world models
Final Practical Road
- Learn math + Python deeply.
- Build small models yourself.
- Understand CNN/RNN → Transformer transition.
- Study BERT, GPT, Qwen, DeepSeek architectures.
- Explore multimodal (Sora-like) models.
- Build agents integrating models and tools.
- Stay updated with current research every month.
If you’d like, I can generate a 12-month learning plan or step-by-step progression with project milestones (e.g., Month 1–3: foundations; 4–6: transformers; 7–12: multimodal and agents).
Would you prefer that next?