The best quality a developer can have isn’t knowing every framework, it’s the ability to figure things out.
Debugging, tracing root causes and understanding why things break. That’s where real skill shows. Tools change, but problem-solving doesn’t.
🔆 t.me/techpsyche
Debugging, tracing root causes and understanding why things break. That’s where real skill shows. Tools change, but problem-solving doesn’t.
🔆 t.me/techpsyche
Forwarded from The World Point
* Setbacks in your twenties sharpen the discipline you’ll need in your thirties.
* Failed ideas teach you to recognize real opportunities when they finally appear.
* Times of isolation train you to value the right relationships, not the loudest ones.
* Financial pressure forces you to build habits that prosperity alone would never teach you.
* Being underestimated gives you the quiet space to build skills without noise or expectation.
* Rejection aligns you with paths you were meant to grow in, not just paths you wanted.
7 Habits to outgrow anyone: t.me/theworldpoint/39
More Growth Tips Here 👇
whatsapp.com/channel/0029VasaQtVGehEUFsVWAn3L
* Failed ideas teach you to recognize real opportunities when they finally appear.
* Times of isolation train you to value the right relationships, not the loudest ones.
* Financial pressure forces you to build habits that prosperity alone would never teach you.
* Being underestimated gives you the quiet space to build skills without noise or expectation.
* Rejection aligns you with paths you were meant to grow in, not just paths you wanted.
7 Habits to outgrow anyone: t.me/theworldpoint/39
More Growth Tips Here 👇
whatsapp.com/channel/0029VasaQtVGehEUFsVWAn3L
❤2
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VIEW IN TELEGRAM
Insane!🙆Tesla Roadster!!
Treat every gap in your knowledge as an invitation, not a flaw.
Forwarded from Artificial Intelligence Resources TP . AI Tools . AI Updates
𝐇𝐨𝐰 𝐭𝐨 𝐃𝐞𝐬𝐢𝐠𝐧 𝐚 𝐍𝐞𝐮𝐫𝐚𝐥 𝐍𝐞𝐭𝐰𝐨𝐫𝐤
→ 𝐃𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦
Clearly outline the type of task:
↬ Classification: Predict discrete labels (e.g., cats vs dogs).
↬ Regression: Predict continuous values
↬ Clustering: Find patterns in unsupervised data.
→ 𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐃𝐚𝐭𝐚
Data quality is critical for model performance.
↬ Normalize and standardize features MinMaxScaler, StandardScaler.
↬ Handle missing values and outliers.
↬ Split your data: Training (70%), Validation (15%), Testing (15%).
→ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐞 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞
𝑰𝐧𝐩𝐮𝐭 𝐋𝐚𝐲𝐞𝐫
↬ Number of neurons equals the input features.
𝐇𝐢𝐝𝐝𝐞𝐧 𝐋𝐚𝐲𝐞𝐫𝐬
↬ Start with a few layers and increase as needed.
↬ Use activation functions:
→ ReLU: General-purpose. Fast and efficient.
→ Leaky ReLU: Fixes dying neuron problems.
→ Tanh/Sigmoid: Use sparingly for specific cases.
𝐎𝐮𝐭𝐩𝐮𝐭 𝐋𝐚𝐲𝐞𝐫
↬ Classification: Use Softmax or Sigmoid for probability outputs.
↬ Regression: Linear activation (no activation applied).
→ 𝐈𝐧𝐢𝐭𝐢𝐚𝐥𝐢𝐳𝐞 𝐖𝐞𝐢𝐠𝐡𝐭𝐬
Proper weight initialization helps in faster convergence:
↬ He Initialization: Best for ReLU-based activations.
↬ Xavier Initialization: Ideal for sigmoid/tanh activations.
→ 𝐂𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧
↬ Classification: Cross-Entropy Loss.
↬ Regression: Mean Squared Error or Mean Absolute Error.
→ 𝐒𝐞𝐥𝐞𝐜𝐭 𝐭𝐡𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐫
Pick the right optimizer to minimize the loss:
↬ Adam: Most popular choice for speed and stability.
↬ SGD: Slower but reliable for smaller models.
→ 𝐒𝐩𝐞𝐜𝐢𝐟𝐲 𝐄𝐩𝐨𝐜𝐡𝐬 𝐚𝐧𝐝 𝐁𝐚𝐭𝐜𝐡 𝐒𝐢𝐳𝐞
↬ Epochs: Define total passes over the training set. Start with 50–100 epochs.
↬ Batch Size: Small batches train faster but are less stable. Larger batches stabilize gradients.
→ 𝐏𝐫𝐞𝐯𝐞𝐧𝐭 𝐎𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠
↬ Add Dropout Layers to randomly deactivate neurons.
↬ Use L2 Regularization to penalize large weights.
→ 𝐇𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫 𝐓𝐮𝐧𝐢𝐧𝐠
Optimize your model parameters to improve performance:
↬ Adjust learning rate, dropout rate, layer size, and activations.
↬ Use Grid Search or Random Search for hyperparameter optimization.
→ 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐚𝐧𝐝 𝐈𝐦𝐩𝐫𝐨𝐯𝐞
↬ Monitor metrics for performance:
→ Classification: Accuracy, Precision, Recall, F1-score, AUC-ROC.
→ Regression: RMSE, MAE, R² score.
→ 𝐃𝐚𝐭𝐚 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧
↬ For image tasks, apply transformations like rotation, scaling, and flipping to expand your dataset.
Neural Networks Overview: t.me/airesourcestp/119
NVIDIA AI, LLM Free Courses: intercomuniversity.com/free-courses/artificial-intelligence/nvidia-is-offering-6-ai-llm-courses-for-free
AI & ML Free Courses by Top Institutions: t.me/airesourcestp/100
AI & ML Courses by Amazon: intercomuniversity.com/free-courses/artificial-intelligence/amazon-free-courses-100-no-fee-ai-machine-learning
5 Free NLP Courses: t.me/airesourcestp/110
Best Courses for AI from Universities with YouTube Playlists
t.me/airesourcestp/111
Like if you want me to continue data science series 😄❤️
More Resources Here:
whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
ENJOY LEARNING 👍👍
→ 𝐃𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦
Clearly outline the type of task:
↬ Classification: Predict discrete labels (e.g., cats vs dogs).
↬ Regression: Predict continuous values
↬ Clustering: Find patterns in unsupervised data.
→ 𝐏𝐫𝐞𝐩𝐫𝐨𝐜𝐞𝐬𝐬 𝐃𝐚𝐭𝐚
Data quality is critical for model performance.
↬ Normalize and standardize features MinMaxScaler, StandardScaler.
↬ Handle missing values and outliers.
↬ Split your data: Training (70%), Validation (15%), Testing (15%).
→ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐭𝐡𝐞 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞
𝑰𝐧𝐩𝐮𝐭 𝐋𝐚𝐲𝐞𝐫
↬ Number of neurons equals the input features.
𝐇𝐢𝐝𝐝𝐞𝐧 𝐋𝐚𝐲𝐞𝐫𝐬
↬ Start with a few layers and increase as needed.
↬ Use activation functions:
→ ReLU: General-purpose. Fast and efficient.
→ Leaky ReLU: Fixes dying neuron problems.
→ Tanh/Sigmoid: Use sparingly for specific cases.
𝐎𝐮𝐭𝐩𝐮𝐭 𝐋𝐚𝐲𝐞𝐫
↬ Classification: Use Softmax or Sigmoid for probability outputs.
↬ Regression: Linear activation (no activation applied).
→ 𝐈𝐧𝐢𝐭𝐢𝐚𝐥𝐢𝐳𝐞 𝐖𝐞𝐢𝐠𝐡𝐭𝐬
Proper weight initialization helps in faster convergence:
↬ He Initialization: Best for ReLU-based activations.
↬ Xavier Initialization: Ideal for sigmoid/tanh activations.
→ 𝐂𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐋𝐨𝐬𝐬 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧
↬ Classification: Cross-Entropy Loss.
↬ Regression: Mean Squared Error or Mean Absolute Error.
→ 𝐒𝐞𝐥𝐞𝐜𝐭 𝐭𝐡𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐫
Pick the right optimizer to minimize the loss:
↬ Adam: Most popular choice for speed and stability.
↬ SGD: Slower but reliable for smaller models.
→ 𝐒𝐩𝐞𝐜𝐢𝐟𝐲 𝐄𝐩𝐨𝐜𝐡𝐬 𝐚𝐧𝐝 𝐁𝐚𝐭𝐜𝐡 𝐒𝐢𝐳𝐞
↬ Epochs: Define total passes over the training set. Start with 50–100 epochs.
↬ Batch Size: Small batches train faster but are less stable. Larger batches stabilize gradients.
→ 𝐏𝐫𝐞𝐯𝐞𝐧𝐭 𝐎𝐯𝐞𝐫𝐟𝐢𝐭𝐭𝐢𝐧𝐠
↬ Add Dropout Layers to randomly deactivate neurons.
↬ Use L2 Regularization to penalize large weights.
→ 𝐇𝐲𝐩𝐞𝐫𝐩𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫 𝐓𝐮𝐧𝐢𝐧𝐠
Optimize your model parameters to improve performance:
↬ Adjust learning rate, dropout rate, layer size, and activations.
↬ Use Grid Search or Random Search for hyperparameter optimization.
→ 𝐄𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐚𝐧𝐝 𝐈𝐦𝐩𝐫𝐨𝐯𝐞
↬ Monitor metrics for performance:
→ Classification: Accuracy, Precision, Recall, F1-score, AUC-ROC.
→ Regression: RMSE, MAE, R² score.
→ 𝐃𝐚𝐭𝐚 𝐀𝐮𝐠𝐦𝐞𝐧𝐭𝐚𝐭𝐢𝐨𝐧
↬ For image tasks, apply transformations like rotation, scaling, and flipping to expand your dataset.
Neural Networks Overview: t.me/airesourcestp/119
NVIDIA AI, LLM Free Courses: intercomuniversity.com/free-courses/artificial-intelligence/nvidia-is-offering-6-ai-llm-courses-for-free
AI & ML Free Courses by Top Institutions: t.me/airesourcestp/100
AI & ML Courses by Amazon: intercomuniversity.com/free-courses/artificial-intelligence/amazon-free-courses-100-no-fee-ai-machine-learning
5 Free NLP Courses: t.me/airesourcestp/110
Best Courses for AI from Universities with YouTube Playlists
t.me/airesourcestp/111
Like if you want me to continue data science series 😄❤️
More Resources Here:
whatsapp.com/channel/0029VahGttK5a24AXAJDjm2R
ENJOY LEARNING 👍👍
Top 28 Cyber Security Tools
1. 🔒 Wireshark
2. 🛡 Snort
3. 🖥 Metasploit
4. 📡 Nmap
5. 🛠 Burp Suite
6. 🌐 OWASP ZAP
7. 📊 Security Onion
8. 🚀 Kali Linux
9. 🛡 Suricata
10. 🌐 Nessus
11. 🚪 OpenVAS
12. 🛡 ModSecurity
13. 🗝 Hashcat
14. 🧱 OSSEC
15. 🛡 Fail2ban
16. 🤖 YARA
17. 📜 SELinux
18. 🧬 ClamAV
19. 🛡 Security+Plus
20. 🔐 GPG
21. 📡 Aircrack-ng
22. 🛠 Sysinternals Suite
23. 🖥 Wireshark
24. 🔑 KeePass
25. 🛡 Tripwire
26. 📝 Ophcrack
27. 📡 Netcat
28. 🛡 Wazuh
Cybersecurity Roadmap: t.me/zerotrusthackers/238
Hacking manuals: t.me/zerotrusthackers/236
Cyber Security WhatsApp Channel
whatsapp.com/channel/0029VaxVv551iUxRku094918
1. 🔒 Wireshark
2. 🛡 Snort
3. 🖥 Metasploit
4. 📡 Nmap
5. 🛠 Burp Suite
6. 🌐 OWASP ZAP
7. 📊 Security Onion
8. 🚀 Kali Linux
9. 🛡 Suricata
10. 🌐 Nessus
11. 🚪 OpenVAS
12. 🛡 ModSecurity
13. 🗝 Hashcat
14. 🧱 OSSEC
15. 🛡 Fail2ban
16. 🤖 YARA
17. 📜 SELinux
18. 🧬 ClamAV
19. 🛡 Security+Plus
20. 🔐 GPG
21. 📡 Aircrack-ng
22. 🛠 Sysinternals Suite
23. 🖥 Wireshark
24. 🔑 KeePass
25. 🛡 Tripwire
26. 📝 Ophcrack
27. 📡 Netcat
28. 🛡 Wazuh
Cybersecurity Roadmap: t.me/zerotrusthackers/238
Hacking manuals: t.me/zerotrusthackers/236
Cyber Security WhatsApp Channel
whatsapp.com/channel/0029VaxVv551iUxRku094918
The internet is having a very bad cybersecurity day
Yesterday was a chaotic day for cybersecurity, highlighted by several major vulnerabilities and breaches across the tech landscape.
Key incidents included:
PHP Vulnerabilities: AI discovered critical flaws impacting the programming language PHP, causing global government website outages and leading the U.S. National Security Agency (NSA) advised systems to update or go offline.
Node.js Patches: Node.js patched four high-severity vulnerabilities, including a remote code execution risk.
LiteLLM Compromise: LiteLLM was compromised with malicious code distributed via PyPI, Docker Hub, and GHCR.
Certificate Authority Breaches: Indian certificate authority eMudhra had its private keys compromised, raising fears of fraudulent certificates.
These events—alongside recent breaches at TiDB Cloud, Plex Media Server, and BeyondTrust—underscore mounting security pressures as AI increasingly aids attackers in exploiting discovered flaws.
🔆 t.me/techpsyche
Yesterday was a chaotic day for cybersecurity, highlighted by several major vulnerabilities and breaches across the tech landscape.
Key incidents included:
PHP Vulnerabilities: AI discovered critical flaws impacting the programming language PHP, causing global government website outages and leading the U.S. National Security Agency (NSA) advised systems to update or go offline.
Node.js Patches: Node.js patched four high-severity vulnerabilities, including a remote code execution risk.
LiteLLM Compromise: LiteLLM was compromised with malicious code distributed via PyPI, Docker Hub, and GHCR.
Certificate Authority Breaches: Indian certificate authority eMudhra had its private keys compromised, raising fears of fraudulent certificates.
These events—alongside recent breaches at TiDB Cloud, Plex Media Server, and BeyondTrust—underscore mounting security pressures as AI increasingly aids attackers in exploiting discovered flaws.
🔆 t.me/techpsyche
Forwarded from Crypto Stract | Web 3, Finance, Business, Blockchain, Crypto, Growth, Stock Market, Money
📈How To Do Your Own Analysis?
⠀
Friends, we have a lot of newbies who are just starting out in cryptocurrency. So I decided to make a post about how to do your own analysis of projects. In other words - DYOR (Do Your Own Research).
One post will not reveal in details all the aspects we need to take into account when analyzing. I plan to do a second post like this, but it will depend on what assets you show under this post. It's up to you.
Below we break down the main metrics for DYOR.
✅Project website
On the website, you will always be greeted with a brief squeeze of the project. Be sure to read the white paper to learn all the basic details:
• What is the project?
• What are its benefits?
• What opportunities does the project offer?
• The project roadmap.
When analyzing the roadmap, pay attention to whether the project is on track. If it is lagging behind, it indicates some difficulty in implementation.
✅Funds
Funds are analyzed to see who is acting as the guarantor of the project's success?
🟢It is important:
• Which foundations have already supported the project?
• What successful cases do these foundations have?
• Will the funds be interested in further development of the project and its token?
Having good funds "on board" increases the likelihood that the launch will be successful and the project will have a better chance of getting support from new investors in the future.
A resource for fund analysts and where they invest is Chain Broker
(chainbroker.io)✅Community and Social Media
You need to gauge the excitement around the project and audience engagement.
🟢Important:
• How active the project's social networks are: Telegram, Twitter, Discord, Medium, GitHub, and so on.
• How involved is the team in maintaining social networks?
• Ambassador Program. Is it active or does it only have a name?
When analyzing social networks, you need to assess not the number of subscribers, likes and reposts, but how active the audience is among themselves. You can write any question in the discord chat and see how quickly you get an answer.
🟢Also, importantly:
• Who is doing the reposts? If they make them "real people" and not bots it is a definite plus.
• Who among the major projects and celebrities is a Twitter follower.
(coinsguru.io) 💭DYOR is not the easiest topic for a newbie. Like this post if you want me to do the second part of the post?
What are the signs of a bearish trend in the crypto market?: t.me/cryptostract/163
Candlestick patterns: t.me/cryptostract/159
How can I spot a bullish trend in the crypto market?: t.me/cryptostract/167
More Resources Here:
whatsapp.com/channel/0029VajB00n0LKZ7cSloGR1M
⠀
Friends, we have a lot of newbies who are just starting out in cryptocurrency. So I decided to make a post about how to do your own analysis of projects. In other words - DYOR (Do Your Own Research).
One post will not reveal in details all the aspects we need to take into account when analyzing. I plan to do a second post like this, but it will depend on what assets you show under this post. It's up to you.
Below we break down the main metrics for DYOR.
✅Project website
On the website, you will always be greeted with a brief squeeze of the project. Be sure to read the white paper to learn all the basic details:
• What is the project?
• What are its benefits?
• What opportunities does the project offer?
• The project roadmap.
When analyzing the roadmap, pay attention to whether the project is on track. If it is lagging behind, it indicates some difficulty in implementation.
✅Funds
Funds are analyzed to see who is acting as the guarantor of the project's success?
🟢It is important:
• Which foundations have already supported the project?
• What successful cases do these foundations have?
• Will the funds be interested in further development of the project and its token?
Having good funds "on board" increases the likelihood that the launch will be successful and the project will have a better chance of getting support from new investors in the future.
A resource for fund analysts and where they invest is Chain Broker
(chainbroker.io)✅Community and Social Media
You need to gauge the excitement around the project and audience engagement.
🟢Important:
• How active the project's social networks are: Telegram, Twitter, Discord, Medium, GitHub, and so on.
• How involved is the team in maintaining social networks?
• Ambassador Program. Is it active or does it only have a name?
When analyzing social networks, you need to assess not the number of subscribers, likes and reposts, but how active the audience is among themselves. You can write any question in the discord chat and see how quickly you get an answer.
🟢Also, importantly:
• Who is doing the reposts? If they make them "real people" and not bots it is a definite plus.
• Who among the major projects and celebrities is a Twitter follower.
(coinsguru.io) 💭DYOR is not the easiest topic for a newbie. Like this post if you want me to do the second part of the post?
What are the signs of a bearish trend in the crypto market?: t.me/cryptostract/163
Candlestick patterns: t.me/cryptostract/159
How can I spot a bullish trend in the crypto market?: t.me/cryptostract/167
More Resources Here:
whatsapp.com/channel/0029VajB00n0LKZ7cSloGR1M
Forwarded from Intercom University
Intercom University
How to Stay Consistent While Learning to Code (Even With a Busy Schedule) - Intercom University
Learning to code is one of the most rewarding skills you can pick up today, whether you want to switch careers, build your own project or simply automate
How to Stay Consistent While Learning to Code (Even With a Busy Schedule)
Read Here: https://intercomuniversity.com/blog/how-to-stay-consistent-while-learning-to-code-even-with-a-busy-schedule/
Read Here: https://intercomuniversity.com/blog/how-to-stay-consistent-while-learning-to-code-even-with-a-busy-schedule/
Curiosity is a muscle. If you don’t feed it, it atrophies. If you push it, it compounds.
The moment you feel “comfortable,” that’s usually the warning sign that you’re stagnating.
🔆 t.me/techpsyche
The moment you feel “comfortable,” that’s usually the warning sign that you’re stagnating.
🔆 t.me/techpsyche
Forwarded from The World Point
The audacity of people and companies that run ads claiming you can become a website owner in less than five minutes, without any coding knowledge! 😅