Udemy Generative AI Skillpath Zero to Hero in Generative AI
Seeders : 5 Leechers : 2
| Torrent Hash : | C605B20AD9A2A0C6BA34D34D3E506518210BF2CC |
| Torrent Added : | at Nov. 13, 2025, 4:23 a.m. in Other |
| Torrent Size : | 3.8 GB |
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Udemy Generative AI Skillpath Zero to Hero in Generative AI
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Udemy Generative AI Skillpath Zero to Hero in Generative AI
Get Bonus Downloads Here.url -
1 - Introduction.mp4 -
1 - Retrieval Augmented Generation Concepts.mp4 -
2 - Step 1 Reading Documents in RAG Workflow.mp4 -
3 - Step 2 Creating Chunks in the RAG Process.mp4 -
4 - Step 3 Generating Embeddings in the RAG Workflow.mp4 -
5 - Step 4 Storing Embeddings in a Vector Database.mp4 -
6 - Building an End-to-End RAG Application.mp4 -
1 - Overview of Tools and Agents in LangChain.mp4 -
2 - Developing Custom Tools with LangChain.mp4 -
3 - LangChain Built-in Tools DuckDuckGo Search and Wikipedia.mp4 -
4 - Working with Agents in LangChain.mp4 -
5 - Building a Memory-Enabled Agent in LangChain.mp4 -
1 - Overview of LangSmith and Its Capabilities.mp4 -
2 - Running and Monitoring Applications Using LangSmith.mp4 -
1 - Introduction to Streamlit.mp4 -
2 - Building a GUI for Your GenAI App Using Streamlit.mp4 -
1 - Strengths and Limitations of Generative AI - foundation of AI.pdf -
1 - Strengths and Limitations of Generative AI.mp4 -
2 - Generative AI The Magic Behind the Mechanism.mp4 -
3 - Understanding How AI Learns.mp4 -
4 - Evolution from Linear Regression to Neural Networks.mp4 -
5 - Understanding Tokens and Embeddings.mp4 -
6 - Inside Transformers — The Core Architecture of LLMs.mp4 -
7 - How Language Models Generate Predictions.mp4 -
8 - Pre-Training vs Fine-Tuning — How Models Evolve.mp4 -
9 - Exploring Open-Source LLMs.mp4 -
1 - What is On-Device AI.mp4 -
2 - Exploring the Qualcomm AI Hub.mp4 -
3 - Setting Up and Logging Into Qualcomm AI Hub.mp4 -
1 - Understanding On-Device Model Deployment Steps.mp4 -
2 - Model Training Phase – Concepts & Workflow.mp4 -
3 - Hands-On Model Training in Practice.mp4 -
1 - Model Compilation – Concepts and Process.mp4 -
2 - Hands-On Model Compilation.mp4 -
3 - Model Profiling – Theory & Performance Insights.mp4 -
4 - Practical Model Profiling Exercise.mp4 -
1 - Running Model Inference on Device.mp4 -
2 - Exporting and Downloading Your Model.mp4 -
3 - Overview to Quantization.mp4 -
4 - Symmetric Quantization Explained.mp4 -
5 - Asymmetric Quantization Explained.mp4 -
6 - Applying Quantization Techniques – Hands-On.mp4 -
1 - About your certificate.html -
1 - Bonus lecture.html -
2 - Bonus lecture.html -
1 - Crafting Effective Prompts Be Detailed and Specific.mp4 -
10 - Thought structures Skeleton-of-Thought Prompting.mp4 -
11 - Thought structures Program-of-Thought Prompting.mp4 -
2 - Best Practices for Prompting.mp4 -
3 - Using Prompt Templates for Consistency.mp4 -
4 - Prompting Framework Chain of Thought.mp4 -
5 - Prompting Framework Step-Back Reasoning.mp4 -
6 - Prompting Framework Role Prompting.mp4 -
7 - Prompting Framework Self-Consistency.mp4 -
8 - Prompting Framework Chain-of-Density.mp4 -
9 - Thought structure Tree-of-Thought Prompting.mp4 -
1 - Understanding Prompt Hyperparameters.mp4 -
2 - Temperature & Top-p Controlling Randomness.mp4 -
3 - Max Tokens & Stop Sequences Managing Output Length.mp4 -
4 - Presence & Frequency Penalties Adding Variety.mp4 -
5 - Tuning Prompt Parameters for Optimal Results - Prompt+parameter+tuning.ipynb -
5 - Tuning Prompt Parameters for Optimal Results.mp4 -
1 - Three Methods to Evaluate Prompt Quality.mp4 -
2 - Conducting Prompt AB Testing.mp4 -
3 - Evaluating Prompts with PromptFoo - Link to download nodejs.url -
3 - Evaluating Prompts with PromptFoo - Prompts+and+test+cases+for+promptfoo.docx -
3 - Evaluating Prompts with PromptFoo.mp4 -
3 -Link to download nodejs.url -
1 - Downloading and Installing Ollama Setup.mp4 -
2 - Configuring Ollama and downloading models.mp4 -
3 - Model customization via Command Line or Terminal.mp4 -
4 - Building, Saving, and Implementing a Custom Ollama Model.mp4 -
1 - Configuring the Python Environment.mp4 -
2 - Working with the Ollama Library in Python.mp4 -
3 - Invoking the Model via the Ollama REST API.mp4 -
1 - Understanding LangChain Objectives and Core Benefits.mp4 -
2 - LangChain Fundamentals Prompt Templates and LLM Models.mp4 -
3 - LangChain Fundamentals Formatting the Output.mp4 -
1 - Using the Pipe Operator in LCEL.mp4 -
2 - Understanding Runnables Theoretical Foundations.mp4 -
3 - Runnable Types Parallel, Passthrough, and Lambda.mp4 -
4 - Example Managing Execution Flow with LCEL.mp4 -
5 - Understanding Dynamic Routing in LangChain.mp4 -
6 - Implementing Dynamic Routing in LCEL.mp4 -
1 - Overview to Memory in LangChain.mp4 -
2 - Understanding Conversation Buffer Memory.mp4 -
3 - Customizing Memory Using Memory Keys and Adding Messages.mp4 -
4 - Implementing Conversation Chains.mp4 -
5 - Working with Conversation Buffer Window Memory.mp4 -
6 - Understanding Conversation Summary Memory.mp4 -
7 - Using Runnables with Message History.mp4 -
Bonus Resources.txt -
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Get Bonus Downloads Here.url -
180 bytes
1 - Introduction.mp4 -
63.9 MB
1 - Retrieval Augmented Generation Concepts.mp4 -
47.0 MB
2 - Step 1 Reading Documents in RAG Workflow.mp4 -
82.3 MB
3 - Step 2 Creating Chunks in the RAG Process.mp4 -
65.2 MB
4 - Step 3 Generating Embeddings in the RAG Workflow.mp4 -
40.0 MB
5 - Step 4 Storing Embeddings in a Vector Database.mp4 -
60.8 MB
6 - Building an End-to-End RAG Application.mp4 -
73.6 MB
1 - Overview of Tools and Agents in LangChain.mp4 -
29.6 MB
2 - Developing Custom Tools with LangChain.mp4 -
73.6 MB
3 - LangChain Built-in Tools DuckDuckGo Search and Wikipedia.mp4 -
70.2 MB
4 - Working with Agents in LangChain.mp4 -
79.1 MB
5 - Building a Memory-Enabled Agent in LangChain.mp4 -
44.5 MB
1 - Overview of LangSmith and Its Capabilities.mp4 -
19.4 MB
2 - Running and Monitoring Applications Using LangSmith.mp4 -
65.3 MB
1 - Introduction to Streamlit.mp4 -
34.6 MB
2 - Building a GUI for Your GenAI App Using Streamlit.mp4 -
50.0 MB
1 - Strengths and Limitations of Generative AI - foundation of AI.pdf -
3.2 MB
1 - Strengths and Limitations of Generative AI.mp4 -
75.0 MB
2 - Generative AI The Magic Behind the Mechanism.mp4 -
71.3 MB
3 - Understanding How AI Learns.mp4 -
92.8 MB
4 - Evolution from Linear Regression to Neural Networks.mp4 -
77.8 MB
5 - Understanding Tokens and Embeddings.mp4 -
61.7 MB
6 - Inside Transformers — The Core Architecture of LLMs.mp4 -
34.5 MB
7 - How Language Models Generate Predictions.mp4 -
47.4 MB
8 - Pre-Training vs Fine-Tuning — How Models Evolve.mp4 -
51.6 MB
9 - Exploring Open-Source LLMs.mp4 -
18.6 MB
1 - What is On-Device AI.mp4 -
22.5 MB
2 - Exploring the Qualcomm AI Hub.mp4 -
42.5 MB
3 - Setting Up and Logging Into Qualcomm AI Hub.mp4 -
17.1 MB
1 - Understanding On-Device Model Deployment Steps.mp4 -
31.0 MB
2 - Model Training Phase – Concepts & Workflow.mp4 -
35.3 MB
3 - Hands-On Model Training in Practice.mp4 -
32.3 MB
1 - Model Compilation – Concepts and Process.mp4 -
43.6 MB
2 - Hands-On Model Compilation.mp4 -
30.1 MB
3 - Model Profiling – Theory & Performance Insights.mp4 -
17.6 MB
4 - Practical Model Profiling Exercise.mp4 -
65.2 MB
1 - Running Model Inference on Device.mp4 -
63.2 MB
2 - Exporting and Downloading Your Model.mp4 -
70.5 MB
3 - Overview to Quantization.mp4 -
25.5 MB
4 - Symmetric Quantization Explained.mp4 -
42.8 MB
5 - Asymmetric Quantization Explained.mp4 -
52.5 MB
6 - Applying Quantization Techniques – Hands-On.mp4 -
69.1 MB
1 - About your certificate.html -
964 bytes
1 - Bonus lecture.html -
9.1 KB
2 - Bonus lecture.html -
9.1 KB
1 - Crafting Effective Prompts Be Detailed and Specific.mp4 -
20.9 MB
10 - Thought structures Skeleton-of-Thought Prompting.mp4 -
24.8 MB
11 - Thought structures Program-of-Thought Prompting.mp4 -
32.9 MB
2 - Best Practices for Prompting.mp4 -
35.2 MB
3 - Using Prompt Templates for Consistency.mp4 -
32.0 MB
4 - Prompting Framework Chain of Thought.mp4 -
114.7 MB
5 - Prompting Framework Step-Back Reasoning.mp4 -
29.5 MB
6 - Prompting Framework Role Prompting.mp4 -
21.0 MB
7 - Prompting Framework Self-Consistency.mp4 -
26.5 MB
8 - Prompting Framework Chain-of-Density.mp4 -
53.9 MB
9 - Thought structure Tree-of-Thought Prompting.mp4 -
102.1 MB
1 - Understanding Prompt Hyperparameters.mp4 -
39.5 MB
2 - Temperature & Top-p Controlling Randomness.mp4 -
61.5 MB
3 - Max Tokens & Stop Sequences Managing Output Length.mp4 -
23.0 MB
4 - Presence & Frequency Penalties Adding Variety.mp4 -
15.7 MB
5 - Tuning Prompt Parameters for Optimal Results - Prompt+parameter+tuning.ipynb -
10.9 KB
5 - Tuning Prompt Parameters for Optimal Results.mp4 -
82.8 MB
1 - Three Methods to Evaluate Prompt Quality.mp4 -
42.2 MB
2 - Conducting Prompt AB Testing.mp4 -
21.4 MB
3 - Evaluating Prompts with PromptFoo - Link to download nodejs.url -
53 bytes
3 - Evaluating Prompts with PromptFoo - Prompts+and+test+cases+for+promptfoo.docx -
13.9 KB
3 - Evaluating Prompts with PromptFoo.mp4 -
136.6 MB
3 -Link to download nodejs.url -
53 bytes
1 - Downloading and Installing Ollama Setup.mp4 -
14.7 MB
2 - Configuring Ollama and downloading models.mp4 -
42.4 MB
3 - Model customization via Command Line or Terminal.mp4 -
47.3 MB
4 - Building, Saving, and Implementing a Custom Ollama Model.mp4 -
33.3 MB
1 - Configuring the Python Environment.mp4 -
21.2 MB
2 - Working with the Ollama Library in Python.mp4 -
56.1 MB
3 - Invoking the Model via the Ollama REST API.mp4 -
23.7 MB
1 - Understanding LangChain Objectives and Core Benefits.mp4 -
25.5 MB
2 - LangChain Fundamentals Prompt Templates and LLM Models.mp4 -
28.9 MB
3 - LangChain Fundamentals Formatting the Output.mp4 -
39.0 MB
1 - Using the Pipe Operator in LCEL.mp4 -
47.6 MB
2 - Understanding Runnables Theoretical Foundations.mp4 -
20.3 MB
3 - Runnable Types Parallel, Passthrough, and Lambda.mp4 -
25.3 MB
4 - Example Managing Execution Flow with LCEL.mp4 -
90.5 MB
5 - Understanding Dynamic Routing in LangChain.mp4 -
21.3 MB
6 - Implementing Dynamic Routing in LCEL.mp4 -
76.4 MB
1 - Overview to Memory in LangChain.mp4 -
28.4 MB
2 - Understanding Conversation Buffer Memory.mp4 -
66.4 MB
3 - Customizing Memory Using Memory Keys and Adding Messages.mp4 -
30.0 MB
4 - Implementing Conversation Chains.mp4 -
26.7 MB
5 - Working with Conversation Buffer Window Memory.mp4 -
25.9 MB
6 - Understanding Conversation Summary Memory.mp4 -
30.9 MB
7 - Using Runnables with Message History.mp4 -
40.3 MB
Bonus Resources.txt -
70 bytes
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