Neural Networks Demystified
In this short series, we will build and train a complete Artificial Neural Network in python.
Neural Networks
This playlist has everything you need to know about Neural Networks, from the basics, all the way to image classification with Convolutional Neural Networks.
Gradient Boost
Explore the fundamentals and advanced concepts of Gradient Boost for regression and classification in machine learning.
Practical Machine Learning with Tensorflow
Learn essential machine learning concepts and apply them using TensorFlow, covering key topics from basic principles to deep learning and data visualization.
Lucas-Kanade Tracker (KLT) - Global Motion Estimation - Lecture 10
Explore Lucas-Kanade Tracker (KLT) for computer vision, covering global motion estimation, object tracking, and 2D transformations. Gain insights into advanced tracking algorithms.
CAP5415 - Training Neural Networks Part 2 - Fall 2020 - Lecture 7
Explore advanced neural network training techniques, including loss functions, backpropagation, gradient descent optimization, and strategies to address common challenges in deep learning.
CAP5415 - Training Neural Networks Part 1 - Lecture 7 - Fall 2020
Explore neural network training techniques, including learning kernels, loss functions, and gradient descent, to enhance your understanding of deep learning fundamentals.
Training Neural Networks for Computer Vision - Part I - Lecture 10
Explore neural network training techniques, including gradient descent, loss functions, and backpropagation, for effective computer vision model optimization and performance improvement.
The Challenges of Training Infinitely Large Neural Networks
Explore challenges in training infinitely large neural networks with Mikhail Belkin. Gain insights into foundational advances in machine learning for biomedical applications.
Stochastic Gradient Descent
Explore stochastic gradient descent, its advantages, and implementation. Learn about mini-batches, momentum, and convergence in this optimization algorithm for machine learning models.
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.
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.
PyTorch Zero to All
Learn essential machine learning and deep learning concepts using PyTorch, covering linear models, neural networks, CNNs, and RNNs in a comprehensive series of lectures.
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.
Artificial Intelligence Projects
Explore AI projects using neural networks for bank default prediction, stock forecasting, and image processing. Learn CNN, RNN, and time series analysis techniques.
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.
Seam Carving and Dynamic Programming - Week 2 - MIT Fall 2020
Explore seam carving algorithm for intelligent image resizing, covering edge detection with gradients and optimal path finding using dynamic programming.
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.
Deep Learning - All Optimizers in One Video - SGD with Momentum, Adagrad, Adadelta, RMSprop, Adam Optimizers
Comprehensive exploration of deep learning optimizers, covering gradient descent, SGD, momentum, Adagrad, Adadelta, RMSprop, and Adam, with detailed explanations and comparisons.
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.