Python Voice Assistant
Learn to build a voice assistant that integrates with Google Calendar, takes notes, and performs various tasks using Python and APIs like gTTS and pyttsx3.
PyQt5 Python 3 Tutorial
Learn to create cross-platform GUI applications using PyQt5, covering setup, buttons, events, Qt Designer, menus, images, popups, and more in this comprehensive tutorial.
Python Checkers AI Tutorial
Implement the minimax algorithm to create an AI for checkers, including explanation and visualization of the technique.
Python ChatBot Tutorial
Learn to build a simple AI chatbot using Python, tflearn, and NLTK in this hands-on tutorial covering deep learning techniques and implementation.
AI Plays Flappy Bird - NEAT Python
Learn to create an AI that plays Flappy Bird using NEAT and Python, covering game development, collision detection, graphics, and machine learning implementation.
Practical Deep Learning for Coders 2018
Comprehensive deep learning program for coders, covering essential concepts and practical applications through seven intensive lessons.
Cutting Edge Deep Learning for Coders
Explore advanced deep learning techniques including object detection, NLP, neural translation, GANs, and image enhancement. Gain practical skills for cutting-edge AI applications.
Practical Deep Learning for Coders 2019
Comprehensive deep learning course covering image classification, NLP, tabular data, and advanced topics. Includes hands-on coding and ethical considerations.
Python for Beginners - Python Tutorial
Learn Python basics and build a CV generator program. Covers functions, loops, classes, and working with Word documents. Includes hands-on exercises and text-to-speech integration.
Restricted Boltzmann Machines
Explore Restricted Boltzmann Machines: their layered structure, hidden and visible layers, statistical independence, and applications in modeling high-dimensional data like images.
Decision Support Systems
Explore decision support systems, from basic concepts to advanced techniques like expert systems and neural networks, enhancing your ability to make informed business choices.
Machine Learning
Explore machine learning models through hands-on tutorials in Python and Julia, covering clustering, regression, neural networks, and more. Practical examples with code provided.
Introduction to Deep Learning for Everyone
Learn deep learning fundamentals using Keras and TensorFlow in R, covering regression, neural networks, regularization, and convolutional networks for practical applications.
Fourier Transform
Comprehensive exploration of Fourier Transform, covering definition, properties, applications, and relationships to other transforms. Includes 2D extension for image processing and practical examples.
Convolution in One Dimension for Neural Networks
Comprehensive exploration of 1D convolution in neural networks, covering theory, implementation, and practical application for time series data analysis and model building.
Convolution in Two Dimensions
Explore 2D convolution, softmax, and batch normalization. Build a CNN for MNIST digits using Cottonwood, covering model architecture, training, and evaluation techniques.
Build a Neural Network Framework
Hands-on tutorial for constructing a custom neural network framework through a series of progressive exercises, enhancing practical machine learning skills.
AssemblyAI
Learn to convert audio to text, summarize audio files, and perform sentiment analysis on audio using Python and speech recognition techniques.
Image Processing
Explore 2D Fourier Transform, its applications in image processing, and techniques for image downsampling and upsampling with practical examples.
Fourier Transform Examples
Explore practical applications of Fourier Transform, including 2D image processing. Learn through examples, from basic cosine functions to complex system analyses and digital signal processing.
Machine Learning 1 - 2020
Comprehensive introduction to machine learning fundamentals, covering probability theory, regression, classification, neural networks, clustering, and advanced topics like kernels and Gaussian processes.