Neural Networks from Scratch in Python
Build and understand neural networks from the ground up, covering key concepts like neurons, layers, activation functions, and loss calculation in Python.
Subspace Capsule Network
Explore Subspace Capsule Networks: a novel approach to neural networks that preserves information, norms, and angles. Learn about its principles, applications, and advantages in image classification and generation.
Computer Vision: Classification Techniques - Neural Networks and CNN Architecture - Lecture 12
Explore machine learning frameworks, neural networks, and CNN architecture, focusing on AlexNet and fully convolutional networks. Learn about activation functions and network composition.
Introduction to Neural Networks - Lecture 5
Introduction to neural networks, covering object classification, image representation, and machine learning fundamentals. Explores biological inspiration, computational implementation, and practical applications in image recognition.
Computer Vision Architecture Evolution: ConvNets to Transformers - Lecture 21
Explore the evolution of vision architectures, comparing SWIN and CNNs, and learn about modernizing ConvNets with macro and micro design changes for improved performance.
Classification in Computer Vision - Part II - Lecture 19
Explore advanced classification techniques in computer vision, including SVMs, neural networks, and fully convolutional networks. Learn about activation functions, loss functions, and optimization for effective model training.
Introduction to Neural Networks - Part II - Lecture 8
Explore neural network fundamentals, including activation functions, multi-class classification, and multi-layer perceptrons. Learn about their implementation and performance on image datasets like MNIST.
Multilayer Perceptron - Back Propagation in Neural Networks
This tutorial on "Multi-Layer Perceptron" will help you to master all the core concepts of multi layer perceptrons and deep neural networks.
Artificial Intelligence Tutorial for Beginners - AI Full Course
Keeping the importance of artificial intelligence in mind, we have come up with this full course on Artificial Intelligence.
Complete Deep Learning
Comprehensive deep learning course covering neural networks, CNN, RNN, LSTM, and advanced topics with hands-on tutorials and practical implementations.
Group Equivariant Deep Learning - 2022
Explore group equivariant deep learning, covering group theory, convolutional neural networks, steerable kernels, and equivariant graph neural networks for advanced machine learning applications.
Deep Learning with Keras - Python
Explore deep learning concepts and implement models using Keras in Python. Learn about neural networks, CNNs, RNNs, LSTM, and more for practical applications like chatbots.
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.
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.
Deep Learning Indepth Tutorials With Krish Naik
Comprehensive deep learning tutorial covering key concepts, techniques, and practical implementations, from perceptrons to CNNs, with hands-on examples and expert insights.
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.
TensorFlow and Deep Reinforcement Learning, Without a PhD - Google I/O '18
Learn to implement deep reinforcement learning with TensorFlow, teaching a neural network to play Pong using only pixel data. No prior expertise required.
An Overview of Deep Learning and Neural Networks
Comprehensive introduction to deep learning concepts, covering neural networks, convolution, pooling, normalization, and more. Includes code examples for practical implementation.
Introduction to Deep Learning
Comprehensive introduction to deep learning fundamentals, covering perceptrons, neural networks, loss functions, gradient descent, backpropagation, and regularization techniques.
MIT: Introduction to Deep Learning
Foundations of deep learning: perceptrons, neural networks, loss functions, backpropagation, optimization techniques, and strategies to prevent overfitting in neural network training.
Sequence Modeling with Neural Networks
Explore sequence modeling with neural networks, focusing on RNNs, their challenges, and solutions like gated cells. Learn applications in music generation and machine translation.