Data Science Full Course for Beginners - Python for Data Science
This video teaches Data Science and its key functions and concepts with a variety of demonstrations & examples to help you get started on the right foot.
Back Propagation
Comprehensive exploration of backpropagation in neural networks, covering its principles, algorithm, types, advantages, and disadvantages for effective machine learning model training.
Batch Normalization
Explore batch normalization in deep learning: its implementation, benefits, and impact on network performance. Learn when and how to use it effectively for improved model training.
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.
Neural Network Tutorial - Artificial Neural Network and Backpropagation in Neural Networks
In this 'Neural Network' Tutorial, you will comprehensively learn about all the concepts of Artificial neural networks and the importance of Back-propagation in Neural Networks.
Deep Learning Full Course - Complete Deep Learning for Beginners
From Google’s Self Driving Car to Apple’s Siri, all of these are applications of Deep Learning. So, keeping in mind, the importance of deep learning, we have come up with this full course on ‘Deep Learning’.
Data Science with Python Full Course
Comprehensive introduction to data science, covering statistics, Python, machine learning, databases, exploratory analysis, visualization, deep learning, and neural networks with practical examples.
Deep Learning Tutorial with Python
Comprehensive tutorial on deep learning with Python, covering essential libraries, machine learning concepts, neural networks, and computer vision with practical demonstrations and examples.
TensorFlow 2.0 Tutorial - Deep Learning
Comprehensive TensorFlow 2.0 tutorial covering installation, neural networks, digit classification, and CNNs. Hands-on sessions provide practical understanding for deep learning enthusiasts.
Natural Language Processing
Explore natural language processing (NLP) concepts, review Text preprocessing and data cleaning, learn forward propagation and back propagation in neural networks, Word Embedding using Keras and deep learning with examples
Complete Deep Learning
Comprehensive deep learning course covering neural networks, CNN, RNN, LSTM, and advanced topics with hands-on tutorials and practical implementations.
Neural Networks
This Course is a friendly introduction series to machine learning, deep learning, neural networks and generative adversarial networks.
Practical Deep Learning for Coders 2018
Comprehensive deep learning program for coders, covering essential concepts and practical applications through seven intensive lessons.
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.
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.
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.
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.
How Neural Networks Work
This video sequence tracks the lecture sequence in course 313 from the End to End Machine Learning School, How Neural Networks Work.