AI and dev ideas, brewed in forty seconds.

Evening · 20:00 IST

AI from scratch

100 lessons in 8 modules, in order. From what AI is to how today's models are built, one idea per reel.

1. Lesson 1: What AI actually is, coming soon
2. Lesson 2: Rules vs learning, coming soon
3. Lesson 3: Narrow AI vs general AI, coming soon
4. Lesson 4: The Turing test (1950), coming soon
5. Lesson 5: Dartmouth 1956: AI gets a name, coming soon
6. Lesson 6: Expert systems and the AI winters, coming soon
7. Lesson 7: Why AI took off after 2010, coming soon
8. Lesson 8: AI, ML, deep learning, LLMs, coming soon
9. Lesson 9: Data, features and labels, coming soon
10. Lesson 10: Training vs inference, coming soon
11. Lesson 11: Supervised learning, coming soon
12. Lesson 12: Unsupervised learning, coming soon
13. Lesson 13: Reinforcement learning, coming soon
14. Lesson 14: Linear regression, coming soon
15. Lesson 15: Loss functions, coming soon
16. Lesson 16: Gradient descent, coming soon
17. Lesson 17: Learning rate, coming soon
18. Lesson 18: Classification and logistic regression, coming soon
19. Lesson 19: Overfitting vs underfitting, coming soon
20. Lesson 20: Train, validation and test splits, coming soon
21. Lesson 21: Accuracy, precision and recall, coming soon
22. Lesson 22: Decision trees and random forests, coming soon
23. Lesson 23: The artificial neuron, coming soon
24. Lesson 24: Layers and deep networks, coming soon
25. Lesson 25: Activation functions, coming soon
26. Lesson 26: Forward pass, coming soon
27. Lesson 27: Backpropagation, coming soon
28. Lesson 28: Epochs and batches, coming soon
29. Lesson 29: Why GPUs matter, coming soon
30. Lesson 30: ImageNet and AlexNet (2012), coming soon
31. Lesson 31: Convolutional networks for images, coming soon
32. Lesson 32: Recurrent networks for sequences, coming soon
33. Lesson 33: Vanishing gradients and LSTMs, coming soon
34. Lesson 34: Dropout and regularisation, coming soon
35. Lesson 35: Transfer learning, coming soon
36. Lesson 36: GANs: two networks competing, coming soon
37. Lesson 37: How computers read text, coming soon
38. Lesson 38: Tokens and tokenization, coming soon
39. Lesson 39: Bag of words and TF-IDF, coming soon
40. Lesson 40: Word embeddings, coming soon
41. Lesson 41: word2vec: king - man + woman, coming soon
42. Lesson 42: Cosine similarity, coming soon
43. Lesson 43: Sentence embeddings, coming soon
44. Lesson 44: Semantic search, coming soon
45. Lesson 45: Vector databases, coming soon
46. Lesson 46: Seq2seq and machine translation, coming soon
47. Lesson 47: The bottleneck problem, coming soon
48. Lesson 48: Attention, the first version, coming soon
49. Lesson 49: Attention Is All You Need (2017), coming soon
50. Lesson 50: Self-attention, intuitively, coming soon
51. Lesson 51: Queries, keys and values, coming soon
52. Lesson 52: Multi-head attention, coming soon
53. Lesson 53: Positional encoding, coming soon
54. Lesson 54: Encoder vs decoder models, coming soon
55. Lesson 55: Why transformers scale, coming soon
56. Lesson 56: Context windows, coming soon
57. Lesson 57: Next-token prediction, coming soon
58. Lesson 58: Temperature and sampling, coming soon
59. Lesson 59: Pre-training, coming soon
60. Lesson 60: Scaling laws, coming soon
61. Lesson 61: From GPT-1 to GPT-3, coming soon
62. Lesson 62: Emergent abilities, coming soon
63. Lesson 63: Instruction tuning, coming soon
64. Lesson 64: RLHF, coming soon
65. Lesson 65: ChatGPT, November 2022, coming soon
66. Lesson 66: Hallucinations, coming soon
67. Lesson 67: Parameters vs tokens, coming soon
68. Lesson 68: Open vs closed models, coming soon
69. Lesson 69: Quantization, coming soon
70. Lesson 70: Running models locally, coming soon
71. Lesson 71: Multimodal models, coming soon
72. Lesson 72: Reasoning models, coming soon
73. Lesson 73: Benchmarks and their limits, coming soon
74. Lesson 74: API pricing and tokens, coming soon
75. Lesson 75: Prompt engineering basics, coming soon
76. Lesson 76: System prompts, coming soon
77. Lesson 77: Few-shot examples, coming soon
78. Lesson 78: Chain-of-thought prompting, coming soon
79. Lesson 79: Structured outputs, coming soon
80. Lesson 80: Tool calling, coming soon
81. Lesson 81: RAG explained, coming soon
82. Lesson 82: Chunking documents, coming soon
83. Lesson 83: Reranking, coming soon
84. Lesson 84: Evals, coming soon
85. Lesson 85: Fine-tuning vs RAG vs prompting, coming soon
86. Lesson 86: LoRA fine-tuning, coming soon
87. Lesson 87: Prompt injection, coming soon
88. Lesson 88: Prompt caching and latency, coming soon
89. Lesson 89: What an AI agent is, coming soon
90. Lesson 90: The agent loop, coming soon
91. Lesson 91: Memory for agents, coming soon
92. Lesson 92: MCP: Model Context Protocol, coming soon
93. Lesson 93: Multi-agent systems, coming soon
94. Lesson 94: AI coding assistants, coming soon
95. Lesson 95: Agentic coding workflows, coming soon
96. Lesson 96: Computer-use agents, coming soon
97. Lesson 97: Guardrails and human-in-the-loop, coming soon
98. Lesson 98: Deploying AI features, coming soon
99. Lesson 99: Responsible AI for developers, coming soon
100. Lesson 100: What comes next, coming soon

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