Master and Build Large Language Models

Name: Master and Build Large Language Models
Price: 9 USD
Availability: InStock

17h 15m 55s

English

Paid

September 2, 2025

Course description

The best way to understand how Large Language Models (LLM) work is to build your own. And that is exactly what you will do in this course. In this exciting video course, AI expert Sebastian Raschka will guide you step by step through all the stages of creating an LLM - in practice and with explanations in liveVideo format. You will implement a project from his bestseller Build a Large Language Model (From Scratch) alongside the author.

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#	Title	Duration
1	1.1. Python Environment Setup Video	21:10
2	1.2. Foundations to Build a Large Language Model (From Scratch)	06:28
3	2.1. Prerequisites to Chapter 2 (1	01:07:40
4	2.2. Tokenizing text	14:10
5	2.3. Converting tokens into token IDs	09:59
6	2.4. Adding special context tokens	06:36
7	2.5. Byte pair encoding	13:40
8	2.6. Data sampling with a sliding window	23:16
9	2.7. Creating token embeddings	08:37
10	2.8. Encoding word positions	12:23
11	3.1. Prerequisites to Chapter 3 (1	01:14:17
12	3.2. A simple self-attention mechanism without trainable weights \| Part 1	41:10
13	3.3. A simple self-attention mechanism without trainable weights \| Part 2	11:43
14	3.4. Computing the attention weights step by step	20:00
15	3.5. Implementing a compact self-attention Python class	08:31
16	3.6. Applying a causal attention mask	11:37
17	3.7. Masking additional attention weights with dropout	05:38
18	3.8. Implementing a compact causal self-attention class	08:53
19	3.9. Stacking multiple single-head attention layers	12:05
20	3.10. Implementing multi-head attention with weight splits	16:47
21	4.1. Prerequisites to Chapter 4 (1	01:11:23
22	4.2. Coding an LLM architecture	14:00
23	4.3. Normalizing activations with layer normalization	22:14
24	4.4. Implementing a feed forward network with GELU activations	16:19
25	4.5. Adding shortcut connections	10:52
26	4.6. Connecting attention and linear layers in a transformer block	12:14
27	4.7. Coding the GPT model	12:45
28	4.8. Generating text	17:47
29	5.1. Prerequisites to Chapter 5	23:58
30	5.2. Using GPT to generate text	17:32
31	5.3. Calculating the text generation loss: cross entropy and perplexity	27:14
32	5.4. Calculating the training and validation set losses	24:52
33	5.5. Training an LLM	27:04
34	5.6. Decoding strategies to control randomness	03:37
35	5.7. Temperature scaling	13:43
36	5.8. Top-k sampling	08:20
37	5.9. Modifying the text generation function	10:51
38	5.10. Loading and saving model weights in PyTorch	04:24
39	5.11. Loading pretrained weights from OpenAI	20:04
40	6.1. Prerequisites to Chapter 6	39:21
41	6.2. Preparing the dataset	26:58
42	6.3. Creating data loaders	16:08
43	6.4. Initializing a model with pretrained weights	10:11
44	6.5. Adding a classification head	15:38
45	6.6. Calculating the classification loss and accuracy	22:32
46	6.7. Fine-tuning the model on supervised data	33:36
47	6.8. Using the LLM as a spam classifier	11:07
48	7.1. Preparing a dataset for supervised instruction fine-tuning	15:48
49	7.2. Organizing data into training batches	23:45
50	7.3. Creating data loaders for an instruction dataset	07:31
51	7.4. Loading a pretrained LLM	07:48
52	7.5. Fine-tuning the LLM on instruction data	20:02
53	7.6. Extracting and saving responses	09:40
54	7.7. Evaluating the fine-tuned LLM	21:57