Pytorch Tutorial
Learn PyTorch fundamentals, from installation to advanced applications. Master tensors, backpropagation, and deep learning techniques for real-world data science projects.
PyTorch for Python 3.5
Learn PyTorch fundamentals, from linear models to advanced CNNs. Master gradient descent, back-propagation, regression, and neural network architectures for efficient deep learning implementation.
Deep Learning Neural Network Tutorials
Comprehensive tutorials on deep learning and neural networks, covering key concepts, architectures, and practical implementation using TensorFlow and Google Colab.
The Fun and Easy Guide to Machine Learning - Tutorials
Comprehensive introduction to machine learning algorithms, from regression to neural networks, with practical Python implementations and engaging whiteboard explanations for beginners and enthusiasts.
Mask R-CNN - Practical Deep Learning Instance Segmentation Tutorials
Learn to implement and train Mask R-CNN for instance segmentation using OpenCV and Python, covering installation, real-time applications, and custom object detection like potholes.
Bank Defaulters Using ANN
Learn to build and apply Artificial Neural Networks for predicting bank defaulters using Keras, covering ANN architecture, activation functions, backpropagation, and practical demonstrations.
AI for Healthcare
Explore AI's transformative impact on healthcare, from diagnosis to drug discovery. Learn key concepts and applications, with hands-on demos illustrating AI's potential in improving patient outcomes.
Learn TensorFlow and Deep Learning Fundamentals with Python - Code-First Introduction Part 1-2
Comprehensive introduction to TensorFlow and deep learning fundamentals, covering tensor operations, neural network regression, and classification. Hands-on coding examples and practical insights for building and improving models.
Learn TensorFlow and Deep Learning Fundamentals with Python - Code-First Introduction Part 2/2
Dive deep into TensorFlow and Python, mastering non-linear models, multi-class classification, and advanced techniques for optimizing neural networks and evaluating their performance.
PyTorch and the Journey in Open Source - Keynote by Soumith Chintala
Soumith Chintala discusses PyTorch's journey, principles, and ecosystem, exploring its development, community impact, and future directions in deep learning and open-source collaboration.
JuliaSim: Machine Learning Accelerated Modeling and Simulation
Explore JuliaSim: Accelerate modeling and simulation with machine learning. Learn about fast differential equation solvers, neural network integration, and surrogate models for enhanced computational efficiency.
Flux - The Elegant Machine Learning Library for Julia
Explore Flux, an intuitive machine learning library for Julia, featuring state-of-the-art performance, automatic differentiation, and innovative compiler technology for efficient and flexible ML development.
Introduction to Convolutional Neural Networks with TensorFlow
Learn to build and implement convolutional neural networks for image classification using TensorFlow. Gain hands-on experience in deep learning techniques for computer vision tasks.
Multilayer Perceptron in Neural Networks - Machine Learning Tutorial
Comprehensive exploration of multilayer perceptrons in neural networks, covering their structure, applications, and practical implementation with hands-on demonstrations.
Artificial Intelligence Tutorial for Beginners - What Is AI?
Comprehensive introduction to AI, covering neural networks, NLP, and computer vision. Explores key concepts, applications, and real-world use cases for beginners entering the field of artificial intelligence.
Python Machine Learning & AI Mega Course - Learn 4 Different Areas of ML & AI
Comprehensive exploration of machine learning and AI in Python, covering algorithms, neural networks, chatbots, and game AI. Hands-on projects and in-depth explanations for practical skills development.
TensorFlow 2 Beginner Course
Comprehensive introduction to TensorFlow 2.0 and Keras, covering key concepts and practical applications in deep learning through hands-on projects and real-world examples.
PyTorch Time Sequence Prediction With LSTM - Forecasting Tutorial
Learn to predict time sequences using PyTorch and LSTMs. Create sine waves, build an LSTM model, implement a training loop, and perform final testing for accurate forecasting.
Deep Learning With PyTorch - Full Course
Comprehensive PyTorch tutorial covering fundamentals, advanced concepts, and practical implementations for deep learning, from tensor basics to CNNs and transfer learning.
Implementation of Perceptron in Deep Learning Using Python from Scratch
Comprehensive guide to implementing perceptrons in Python, covering theory and practical coding from scratch for deep learning enthusiasts and AI developers.
Gradient Descent, How Neural Networks Learn
Explore gradient descent and neural network learning through animations, examples, and expert insights. Gain a deeper understanding of cost functions, network analysis, and cutting-edge research in machine learning.