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- Deep Learning for Pedestrians: Backpropagation in Cnns
- Backpropagation and Reinforcement Learning Chapters 20 & 21
- Machine Learning I Lecture 18 Multi-Layer Perceptron
- Machine Learning and Data Mining
- Artificial Intelligence As a New Era in Medicine
- ARTIFICIAL INTELLIGENCE and MACHINE LEARNING Industry Insights and Applications
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- Multimodal Autoencoder: a Deep Learning Approach to Filling in Missing Sensor Data and Enabling Better Mood Prediction
- Artificial Neural Networks Supplement to 2001 Bioinformatics Lecture on Neural Nets
- Reinforcement Learning: a Survey
- CARRNN: a Continuous Autoregressive Recurrent Neural Network for Deep Representation Learning from Sporadic Temporal Data
- Categorization of Web News Documents Using Word2vec and Deep Learning
- Machine Learning What Is Machine Learning?
- Learning Recurrent Neural Networks with Hessian-Free Optimization
- Speedy Q-Learning
- Machine Learning Basics Lecture 3: Perceptron Princeton University COS 495 Instructor: Yingyu Liang Perceptron Overview
- The Perceptron
- Backpropagation
- Lecture #25: Artificial Intelligence and Machine Learning CS106E Spring 2018, Payette & Lu
- R for Machine Learning
- Human-Level Control Through Deep Reinforcement Liia Butler but First
- US FDA Artificial Intelligence and Machine Learning Discussion Paper
- Reinforcement Learning Chapter 21
- Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion
- Machine Learning for Dummies®, IBM Limited Edition
- The Discipline of Machine Learning
- Machine Learning and Deep Learning
- Machine Learning and Data Mining Lecture Notes CSC 411/D11 Computer Science Department University of Toronto Version: February 6, 2012
- Machine Learning Applications in Bioinformatics
- Reinforcement Learning with Recurrent Neural Networks
- An Executive's Guide to AI
- Double Q-Learning
- Postprocessing in Machine Learning and Data Mining
- Causal Mapping for Auditors: Can AI Help? Yves Genest Portland , Ore
- Machine Learning: a Revolution in Risk Management and Compliance?
- Autoencoders
- Technical Note Q,-Learning
- Lecture 4: Static Word Embeddings
- Part 1: Nonlinear Classifiers and the Backpropagation Algorithm
- Data Science and Digital Systems: the 3Ds of Machine Learning
- Training and Analysing Deep Recurrent Neural Networks
- Lecture 10 Recurrent Neural Networks
- A Review of the Use of Artificial Neural Network Models for Energy and Reliability Prediction
- Reinforcement Learning with Factored States and Actions
- Machine Learning for Data Scientists and Developers
- Deep Reinforcement Learning: an Overview
- Word Embedding and Text Classification Based on Deep Learning Methods
- Machine Learning and Artificial Neural Networks (Ref: Negnevitsky, M
- Machine Learning, Artificial Intelligence, and the Use of Force by States
- Word2vec, Node2vec, Graph2vec, X2vec: Towards a Theory of Vector Embeddings of Structured Data
- Machine Learning: Backpropagation • in This Module, I’Ll Discuss Backpropagation, an Algorithm to Automatically Compute Gradients
- Foundations of Machine Learning Reinforcement Learning
- Improving the Accuracy of Pre-Trained Word Embeddings for Sentiment Analysis
- Neural Networks for Machine Learning Lecture
- Machine Learning with Python
- Deep Reinforcement Learning for the Control of Robotic Manipulation: a Focussed Mini-Review
- Random Backpropagation and the Deep Learning Channel
- Incorporating Machine Learning Into Established Bioinformatics Frameworks
- 1 Reinforcement Learning and Its Relationship to Supervised Learning
- The Application of Machine Learning in Data Mining Under Big Data Environment
- Neural Networks for Machine Learning Lecture 15C Deep Autoencoders for Document Retrieval and Visualization
- Distributed Representations of Words and Phrases and Their Compositionality
- A Review on Data Mining & Big Data, Machine Learning Techniques
- A Tutorial on Deep Learning Part 2: Autoencoders, Convolutional Neural Networks and Recurrent Neural Networks
- Supervised Autoencoders: Improving Generalization Performance with Unsupervised Regularizers
- A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
- CR02/2020 the Use of Artificial Intelligence and Machine Learning
- Incremental Multi-Step Q-Learning
- The Perceptron Algorithm
- Playing Atari with Deep Reinforcement Learning
- Introduction to Machine Learning
- Machine Learning Chapter 4. Artificial Neural Networks
- Lnknet: Neural Network, Machine-Learning, and Statistical Software for Pattern Classification
- Deep Learning in Bioinformatics: Introduction, Application, and Perspective in Big Data Era
- Learning About Word Vector Representations and Deep Learning Through Implementing Word2vec
- Deep Learning Autoencoder Approach for Handwritten Arabic Digits Recognition
- From Classical Machine Learning to Deep Neural Networks: a Simplified Scientometric Review
- An Autoencoder-Based Deep Learning Classifier for Efficient
- Lecture 6. Notes on Linear Algebra. Perceptron
- Learning Rates for Q-Learning
- Machine Learning (CSE 446): the Perceptron Algorithm
- CNN Encoder to Reduce the Dimensionality of Data Image for Motion Planning
- An Overview of Word Embedding-Based Machine Learning Methods in Topic Recognition Tasks
- Artificial Intelligence Definitions