ESP32 new boards in stock

ESP32-PICO-KITb

Espressif never stop to surprise us with new products. Their new ESP32-PICO-D4 IC has build in 4MB SPI Flash and quartz crystal, antenna matching circuit, so this chip requires just 5 external resistors and capacitors to work! If your design has to be small this is the chip for you!

ESP32-PICO-KIT is low cost development board for this new chip and has build in USB serial programmer, antenna and breadboard friendly DIL header connectors at 0.1″ step.

ESP32-CAM-F

ESP32-CAM is WiFi IP camera with OV2640 2 Mpix sensor it works with internal PCB antenna and needs just 3.3V external power supply to  operate. You also have SD-card connector and GPIOs to which you can connect PIR sensors and other components to control.

There is Arduino support for this board and tons of interesting projects like face recognition, motion detectors with storage of the pictures on the SD card and etc.

Here are few of them:

We also got ESP32-CAM-UFL which is same board but with U.FL connector where you can connect external antennas.

ESP32-CAM-ANT1

ESP32-CAM-EA is ESP32-CAM with small external WiFi/BT antenna which increased the WiFi coverage.

ESP32-CAM-ANT2

If you need more coverage we have ESP32-CAM-EAX which has higher gain antenna, but with quite bigger size.

New Board from Espressif – ESP32-LyraT in stock

ESP32_LyraT3

ESP32-LyraT is low cost IoT speech recognition board. It can work with many audio sources and have provisions to connect speakers, camera and LCD display.

The board documentation is skinny as this board is very new and we got limited quantity of one of the very first produced boards.

 

Free online Machine Learning course CS 229 from Stanford University

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This course provides a broad introduction to machine learning and statistical pattern recognition.

Topics include:

  • supervised learning (generative/discriminative learning,
  • parametric/non-parametric learning, neural networks,
  • support vector machines);
  • unsupervised learning (clustering, dimensionality reduction, kernel methods);
  • learning theory (bias/variance tradeoffs; VC theory; large margins);
  • reinforcement learning and adaptive control.

The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.

You can access course materials at http://cs229.stanford.edu/materials.html