AI Geek Programmer Blog
Machine learning, computer vision, deep learning and artificial intelligence engineering.

Convolutional neural network 2: architecture
- AI Geek Programmer
- COMPUTER VISION
- 25 December 2019
Convolutional neural network - everything you would like to know about ConvNets, but were afraid to ask ;-). Comprehensive multipart tutorial: part 2
Read article
Convolutional neural network 1: convolutions
- AI Geek Programmer
- COMPUTER VISION
- 24 November 2019
Convolutional neural network - everything you would like to know about ConvNets, but were afraid to ask ;-). Comprehensive multipart tutorial: part 1.
Read article
Naive Bayes in machine learning
- AI Geek Programmer
- MACHINE LEARNING
- 2 November 2019
Why is Naive Bayes so naive in machine learning? A step-by-step explanation for one of the popular and effective classifiers.
Read article
Development environment for machine learning
- AI Geek Programmer
- MACHINE LEARNING
- 20 October 2019
How to build your local development environment for machine learning and specyfically Tensorflow using Anaconda, conda and pip.
Read article
Logistic regression and Keras for classification
- AI Geek Programmer
- COMPUTER VISION
- 14 October 2019
In this tutoarial, you will learn how to use logistic regression and Keras to do a simple binary classification.
Read article
Handwritten Digit Recognition with Keras
- AI Geek Programmer
- COMPUTER VISION
- 30 August 2019
Tutorial on handwritten digit recognition using Keras, Tensorflow and Python. Step-by-step instructions and coding. Thorough explanations.
Read articleTags
- machine learning
- artificial intelligence
- deep learning
- shape recognition
- python
- keras
- convolutional neural networks
- numpy
- cifar10
- convnets
- tensorflow
- ai
- overfitting
- anaconda
- mnist
- pytorch
- data augmentation
- development environment
- dropout
- GPU
- nlp
- scikit-learn
- transformer architecture
- batch normalization
- blockchain
- digits dataset
- google colab
- huggingface
- internal covariate shift
- k-nn