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Thrilled to announce a major milestone in our professional development journey! 🚀 We are excited to unveil a strategic, curated ecosystem of 800+ high-impact Computer Science learning modules from industry titans like MIT, Harvard, and other top-tier global institutions. 🎓✨
This centralized repository represents a powerful synergy of knowledge, meticulously organized by key verticals including algorithms, ML, networks, and robotics, ensuring seamless alignment with your career growth objectives. 📈💡
Say goodbye to fragmented roadmaps and hello to a ready-made, optimized pathway for Computer Science excellence—empowering you to leverage these resources without the need for manual assembly or redundant effort. ⚙️🌟
Unlock your full potential and scale your expertise today:
⛓️ Strategic Resource Hub:
https://github.com/Developer-Y/cs-video-courses
#ContinuousLearning #GrowthMindset #TechExcellence #CareerStrategy #Innovation
This centralized repository represents a powerful synergy of knowledge, meticulously organized by key verticals including algorithms, ML, networks, and robotics, ensuring seamless alignment with your career growth objectives. 📈💡
Say goodbye to fragmented roadmaps and hello to a ready-made, optimized pathway for Computer Science excellence—empowering you to leverage these resources without the need for manual assembly or redundant effort. ⚙️🌟
Unlock your full potential and scale your expertise today:
⛓️ Strategic Resource Hub:
https://github.com/Developer-Y/cs-video-courses
#ContinuousLearning #GrowthMindset #TechExcellence #CareerStrategy #Innovation
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cnn-vgg19-model-tranform-learning.pdf
7 MB
Excited to share latest Deep Learning project: Faulty Solar Panel Detection using CNN + VGG19! 🚀
☀️ Problem: Manual solar panel inspection is slow, costly, and error-prone due to environmental degradation.
💡 Solution: An image classification model detecting 6 fault types via VGG19 Transfer Learning (ImageNet pretrained).
📂 Dataset: 885 images across 6 classes:
• 🐦 Bird-drop
• ✅ Clean
• 🌫 Dusty
• ⚡️ Electrical-damage
• 💥 Physical-Damage
• ❄️ Snow-Covered
🏗 Architecture:
• Base: VGG19 (frozen for feature extraction)
• Head: GlobalAveragePooling2D → Dropout(0.3) → Dense(90)
• Training: Phase 1 (Head only, 46K params) → Phase 2 (Fine-tune top layers, lr=0.0001)
📊 Results (2 epochs):
✅ Val Accuracy: 81.36%
📉 Val Loss: 0.589
🔍 Takeaways:
→ Transfer learning works well on small datasets (~885 images).
→ Fine-tuning significantly boosted performance over feature extraction alone.
→ Model effectively distinguishes subtle differences (e.g., dusty vs. bird-drop).
🛠 Stack: Python | TensorFlow/Keras | VGG19 | OpenCV | Scikit-learn | Seaborn | Matplotlib
https://t.iss.one/CodeProgrammer🔰
☀️ Problem: Manual solar panel inspection is slow, costly, and error-prone due to environmental degradation.
💡 Solution: An image classification model detecting 6 fault types via VGG19 Transfer Learning (ImageNet pretrained).
📂 Dataset: 885 images across 6 classes:
• 🐦 Bird-drop
• ✅ Clean
• 🌫 Dusty
• ⚡️ Electrical-damage
• 💥 Physical-Damage
• ❄️ Snow-Covered
🏗 Architecture:
• Base: VGG19 (frozen for feature extraction)
• Head: GlobalAveragePooling2D → Dropout(0.3) → Dense(90)
• Training: Phase 1 (Head only, 46K params) → Phase 2 (Fine-tune top layers, lr=0.0001)
📊 Results (2 epochs):
✅ Val Accuracy: 81.36%
📉 Val Loss: 0.589
🔍 Takeaways:
→ Transfer learning works well on small datasets (~885 images).
→ Fine-tuning significantly boosted performance over feature extraction alone.
→ Model effectively distinguishes subtle differences (e.g., dusty vs. bird-drop).
🛠 Stack: Python | TensorFlow/Keras | VGG19 | OpenCV | Scikit-learn | Seaborn | Matplotlib
https://t.iss.one/CodeProgrammer
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🔥 Google Colab has added the option of retraining 500+ open-source neural networks
Unsloth has released a convenient notebook for configuring models.
Instructions:
1. Open the page in Colab: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb
2. Run the blocks and the Unsloth Studio itself.
3. Select a model and a dataset.
4. Click "Start Training" and monitor the progress in real time.
5. Everything is ready - you can immediately compare the regular and fine-tuned versions of the model in the chat.
Unsloth has released a convenient notebook for configuring models.
Instructions:
1. Open the page in Colab: https://colab.research.google.com/github/unslothai/unsloth/blob/main/studio/Unsloth_Studio_Colab.ipynb
2. Run the blocks and the Unsloth Studio itself.
3. Select a model and a dataset.
4. Click "Start Training" and monitor the progress in real time.
5. Everything is ready - you can immediately compare the regular and fine-tuned versions of the model in the chat.
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CVE | Cyber Vulnerabilities Exchange
Group dedicated to sharing and discussing CVEs, zero-days, critical vulnerabilities, exploits, PoCs, and technical analyses of offensive and defensive security.
What you'll find here:
• Newly disclosed CVEs
• Public and private exploits
• Technical analysis and bypasses
• Offensive/defensive security
• Penetration testing and red team discussions
Technical, direct, and straightforward content.
Channel=> https://t.iss.one/cve0day
Think. Break. Secure.
Group dedicated to sharing and discussing CVEs, zero-days, critical vulnerabilities, exploits, PoCs, and technical analyses of offensive and defensive security.
What you'll find here:
• Newly disclosed CVEs
• Public and private exploits
• Technical analysis and bypasses
• Offensive/defensive security
• Penetration testing and red team discussions
Technical, direct, and straightforward content.
Channel=> https://t.iss.one/cve0day
Think. Break. Secure.
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