AI Model Compression Techniques - Building Cheaper, Faster, and G...
- Category Other
- Type Tutorials
- Language English
- Total size 258.2 MB
- Uploaded By freecoursewb
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- Last checked 1 week ago
- Date uploaded 5 months ago
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Infohash : 524A66F94501316D6A3B3234059383FBA7F053EC
AI Model Compression Techniques: Building Cheaper, Faster, and Greener AI
https://WebToolTip.com
Released 07/2025
With Tejas Chopra
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Intermediate | Genre: eLearning | Language: English + subtitle | Duration: 1h 55m 10s | Size: 258 MB
Learn how to make AI models faster, smaller, and more sustainable with practical techniques like pruning, quantization, and distillation.
Course details
In this course, Tejas Chopra—an advocate for efficient, green computing—explores how to make AI/ML models more efficient, cost-effective, and environmentally friendly. Dive into practical techniques such as pruning, quantization, and knowledge distillation. Learn how to reduce model size and memory usage without significantly compromising accuracy. Use hands-on coding exercises in TensorFlow and PyTorch to implement these techniques and fine-tune models for optimal performance. Build your understanding of how to balance accuracy, efficiency, and sustainability, giving you the tools to build smarter, faster, and greener AI systems. Whether you’re building edge AI applications, deploying models at scale, or seeking to lower carbon footprints, this course equips you with actionable strategies to address real-world challenges in AI.
Files:
[ WebToolTip.com ] AI Model Compression Techniques - Building Cheaper, Faster, and Greener AI- Get Bonus Downloads Here.url (0.2 KB) ~Get Your Files Here ! 0 - Introduction
- 1. Making AI more efficient and accessible.mp4 (3.8 MB)
- 1. Making AI more efficient and accessible.srt (1.4 KB)
- 2. What you should know.mp4 (4.4 MB)
- 2. What you should know.srt (3.7 KB)
- 3. What is sustainable AI.mp4 (12.3 MB)
- 3. What is sustainable AI.srt (7.8 KB)
- 1. Real-world challenges in AI models.mp4 (6.1 MB)
- 1. Real-world challenges in AI models.srt (3.9 KB)
- 2. Benefits of AI model compression.mp4 (6.0 MB)
- 2. Benefits of AI model compression.srt (4.3 KB)
- 3. Environmental and cost impacts of AI model compression.mp4 (7.2 MB)
- 3. Environmental and cost impacts of AI model compression.srt (4.2 KB)
- 1. What is quantization.mp4 (6.0 MB)
- 1. What is quantization.srt (4.3 KB)
- 2. Static and dynamic quantization.mp4 (22.7 MB)
- 2. Static and dynamic quantization.srt (14.8 KB)
- 3. Quantization-aware training.mp4 (11.2 MB)
- 3. Quantization-aware training.srt (6.6 KB)
- 4. Comparing quantization results.mp4 (5.1 MB)
- 4. Comparing quantization results.srt (4.0 KB)
- 1. What is pruning.mp4 (6.6 MB)
- 1. What is pruning.srt (5.1 KB)
- 2. Implementing layer-based pruning.mp4 (27.9 MB)
- 2. Implementing layer-based pruning.srt (18.0 KB)
- 3. Fine-tuning after pruning.mp4 (20.7 MB)
- 3. Fine-tuning after pruning.srt (16.4 KB)
- 4. Pruning results comparison.mp4 (12.5 MB)
- 4. Pruning results comparison.srt (10.5 KB)
- 1. What is knowledge distillation.mp4 (16.0 MB)
- 1. What is knowledge distillation.srt (12.0 KB)
- 2. Distilling into a smaller model.mp4 (20.5 MB)
- 2. Distilling into a smaller model.srt (12.6 KB)
- 3. Fine-tuning student models.mp4 (14.2 MB)
- 3. Fine-tuning student models.srt (10.7 KB)
- 4. Comparing student and teacher models.mp4 (25.2 MB)
- 4. Comparing student and teacher models.srt (21.0 KB)
- 1. Compressed models in production.mp4 (11.6 MB)
- 1. Compressed models in production.srt (8.3 KB)
- 2. Decision framework for model compression.mp4 (17.9 MB)
- 2. Decision framework for model compression.srt (13.6 KB)
- Bonus Resources.txt (0.1 KB)
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