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Learn Backpropagation, earn certificates with paid and free online courses from Alexander Amini, IIT Madras, NPTEL, IISc Bangalore and other top universities around the world. Read reviews to decide if a class is right for you.
The MOST in-depth look at neural network theory for machine learning, with both pure Python and Tensorflow code
Build Deep Learning Algorithms with TensorFlow 2.0, Dive into Neural Networks and Apply Your Skills in a Business Case
Deep Learning and Neural Networks Theory and Applications with PyTorch! Including Transformers, BERT and GPT!
Explore deep learning fundamentals with Université de Montréal's 5-week course. Learn from Turing Award recipient, Yoshua Bengio, and tackle bias in machine learning.
Hopfield networks, neural networks, gradient descent and backpropagation algorithms explained step by step
Learn to write a Python program for image recognition using neural networks from scratch in this free, 2-hour course. No libraries used, just high school precalculus knowledge required.
The MOST in-depth look at neural network theory, and how to code one with pure Python and Numpy
New to deep learning? Start with this course, that will not only introduce you to the field of deep learning but give you the opportunity to build your first deep learning model using thepopular Keras library.
In this short series, we will build and train a complete Artificial Neural Network in python.
In this course, we will learn about the building blocks used in these Deep Learning based solutions. Specifically, we will learn about feedforward neural networks, convolutional neural networks, recurrent neural networks and attention mechanisms.
This playlist has everything you need to know about Neural Networks, from the basics, all the way to image classification with Convolutional Neural Networks.
This course provides a fairly comprehensive view of the fundamentals of pattern classification and regression. Topics covered in the lectures include an overview of pattern classification and regression; Bayesian decision making and Bayes classifier
The course places machine learning in its context within AI and gives an introduction to the most important core techniques such as decision tree-based inductive learning, inductive logic programming, reinforcement learning and deep learning through deci…
Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kind of…
Learn the key concepts behind artificial neural networks. Discover how to configure a neural network and use that network to find patterns in massive data sets.
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