picPerfc

RePost FTW
I like lewd things :))

[picPerfc @grwl12]

🤤🤤🤤

[Pregnant Journey @PregJourney]

New session, same result.
Conversation stays on track and video comes quick.
→ https://t.co/WgamUA6Ts1

[徐伦 @Jolyne_AI]

在 GitHub 挖到一个很扎实的 IoT 项目合集：100 Days 100 IoT Projects，用 ESP32、ESP8266 和 Raspberry Pi Pico 做了 100 个物联网和嵌入式系统项目。

GitHub：https://t.co/L352o4dNDg

每个项目都包含完整的代码、电路图和详细说明，从基础的传感器读取到 Web 仪表盘全覆盖。

主要特点：
- 基于 MicroPython 开发，上手门槛低
- 涵盖 DHT11、PIR、超声波、气体传感器等常见模块
- 覆盖 ADC、PWM、Wi-Fi、WebSocket、蓝牙等核心概念
- 包含 Web 服务器、Blynk IoT、Telegram 通知等实用场景
- 支持 Wokwi 仿真测试，不需要实体硬件也能跑

已被 Adafruit Newsletter、https://t.co/ih04nXUMso、墨尔本 MicroPython Meetup 推荐收录。

想入门 IoT 开发或找实战案例的可以看看，项目持续更新中。

[Suka CDN2 @enzs8a]

📁
https://t.co/gULBMfxyna
📁
https://t.co/IqgwVg6ZJ6
📁
https://t.co/S3AGXq68fd
📁
https://t.co/9DG3gwkiDh

[nesya @cyeerie]

https://t.co/BhMdcOkYtz
https://t.co/HHvC2xVi2y

[ulla @awviate]

📁
https://t.co/ExI4NjjUXp
📁
https://t.co/cM0B2bu5kP
📁
https://t.co/V2m0D0sqdq
📁
https://t.co/q64QgJ8tvR
📁
https://t.co/nrMVCGteGx
📁
https://t.co/ZyHDC1LXwC
📁
https://t.co/0YeLZoTj4Y
📁
https://t.co/Ew2FjODRLq
📁
https://t.co/Reja9AEiFV
📁
https://t.co/PyQKXD0Yvy

[Rohit Kumar Tiwari @_rohit_tiwari_]

This playlist has 26 deep learning lectures.
One of the best free resources online.
Go from beginner to confident in just 5 hours. https://t.co/KjaS7jAt0S

Foundations of Deep Learning
> What deep learning is
> The math basics you need, including linear algebra and calculus
> How a single neuron works as a computing unit

Training Mechanics
> How a neuron is trained using gradient descent
> Why the data analysis pipeline matters
> How out of sample validation helps check model reliability

Network Architectures
> Feed Forward Neural Networks explained
> Backpropagation and why it lets multi layer networks learn from errors

Optimization for Classification
> Activation functions like Softmax
> Loss functions like Categorical Cross-entropy
> How these handle complex classification problems

Efficiency and Stability
> Making networks faster with vectorization
> How to spot and fix vanishing or exploding gradient issues

Generalization and Regularization
> Ways to avoid overfitting
> How to make sure models work well on new data, not just training data

Computer Vision and Transfer Learning
> Convolutional layers for image tasks
> Using transfer learning and data augmentation
> Training large models with limited data

Advanced Network Features
> Residual Networks explained
> Why skip connections help build very deep models

Natural Language Processing
> How machines understand text
> Word embeddings
> Recurrent Neural Networks and LSTMs
> Transformers and self attention

Generative and Specialized Models
> Auto Encoders for data compression
> Generative Adversarial Networks for creating new data
> The ideas behind AlphaGo and reinforcement learning