Search result for Neural networks and deep learning github Online Courses & Certifications
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Computer Vision
by Sebastian Thrun , Cezanne Camacho , Alexis Cook , Juan Delgado , Jay Alammar , Ortal Arel , Luis Serrano- 0.0
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3 Months
Write programs to analyze images, implement feature extraction, and recognize objects using deep learning models. Learn cutting-edge computer vision and deep learning techniques—from basic image processing, to building and customizing convolutional neural networks. Work on a variety of computer vision and deep learning applications from basic image processing to automatic image captioning....
人工智慧：機器學習與理論基礎 (Artificial Intelligence - Learning & Theory)
by 于天立- 4.6
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Approx. 12 hours to complete
1-6 Biased and Unbiased Hypothesis Space, Futility of Bias-Free Learning Neural Network and Deep learning 4-2 Single-Layer Network and Perceptron Learning Rule 4-4 Cascade Correlation Neural Networks, Deep or Shallow Structure 4-5 Deep Learning: Convolutional Neural Networks 4-7 Recurrent Networks, Long Short-Term Memory (LSTM), Neural Turing Machine, Memory-Augmented Neural Networks (MANN)...
Natural Language Processing
by Luis Serrano , Jay Alammar , Arpan Chakraborty , Dana Sheahen- 0.0
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3 Months
Learn cutting-edge natural language processing techniques to process speech and analyze text. Build probabilistic and deep learning models, such as hidden Markov models and recurrent neural networks, to teach the computer to do tasks such as speech recognition, machine translation, and more! Build models using probabilistic and deep learning techniques and apply them to speech recognition, machine translation, and more!...
Reinforcement Learning for Trading Strategies
by Jack Farmer , Ram Seshadri- 3.7
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Approx. 12 hours to complete
In the final course from the Machine Learning for Trading specialization, you will be introduced to reinforcement learning (RL) and the benefits of using reinforcement learning in trading strategies. You will learn how RL has been integrated with neural networks and review LSTMs and how they can be applied to time series data....
Introduction to Deep Learning
by Evgeny Sokolov , Зимовнов Андрей Вадимович , Alexander Panin , Ekaterina Lobacheva , Nikita Kazeev- 4.5
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Approx. 34 hours to complete
The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. What Deep Learning is and is not...
Sequences, Time Series and Prediction
by Laurence Moroney- 4.7
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Approx. 13 hours to complete
The Machine Learning course and Deep Learning Specialization from Andrew Ng teach the most important and foundational principles of Machine Learning and Deep Learning. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization. Deep Neural Networks for Time Series Deep neural network training, tuning and prediction...
Using SAS Viya REST APIs with Python and R
by Jordan Bakerman , Ari Zitin- 0.0
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Approx. 18 hours to complete
You’ll learn how to create both machine learning and deep learning models to tackle a variety of data sets and complex problems. Deep Learning Traditional Neural Networks Deep Neural Networks (DNN) versus Recurrent Neural Networks (RNN) Demo: Deep Learning Sentiment Prediction Using the R API Convolutional Neural Networks for Image Classification...
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Machine Learning and Deep Learning using Tensor Flow & Keras
by Indra Programmer- 2
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11 hours on-demand video
This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning and also the basics of Machine learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand and its application ....
$12.99
AI Workflow: Machine Learning, Visual Recognition and NLP
by Mark J Grover , Ray Lopez, Ph.D.- 4.5
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Approx. 14 hours to complete
Explain the use of linear and logistic regression in supervised learning applications Explain the use of Neural Networks in supervised learning applications Discuss the major variants of neural networks and recent advances Building Machine Learning and Deep Learning Models Neural Networks Introduction to neural networks Getting Started with Convolutional Neural Networks and TensorFlow (Hands-on)...
Intro to TensorFlow for Deep Learning
by Magnus Hyttsten , Juan Delgado , Paige Bailey- 0.0
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Approx. 2 months
Learn how to build deep learning applications with TensorFlow. and take advantage of it for TensorFlow-Lite and TensorFlow-Serving lesson 7 Time Series Forecasting Learning from sequential data with recurrent neural networks lesson 8 Introduction to TensorFlow Lite Learn how you can use TensorFlow lite to build machine learning apps on Android iOS and iOT devices...
Free
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