Deep Learning - AD3501 - Notes - Codes for Practical

Aug 06, 2025

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Deep Learning - AD3501 - Notes - Codes for Practical

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Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 10, Deep learning

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 10, Deep learning of Data Mining by I. H. Witten, E. Frank, M. A. Hall, and C. J. Pal. Introducing Deep Learning.

1.53k views • 123 slides

SUPPORTING NOTES FOR PRACTICAL UNIT

SUPPORTING NOTES FOR PRACTICAL UNIT . “In addition to presenting the extract (s) from their selected play, the students will need to provide supporting notes which will demonstrate their understanding and application of the ideas of their chosen practitioner”. Brief overview.

247 views • 7 slides

Deep Learning for NL

Deep Learning for NL. Giuseppe Attardi Dipartimento di Informatica Università di Pisa. Statistical Machine Learning. Training on large document collections Requires ability to process Big Data If we used same algorithms 10 years ago they would still be running

1.18k views • 107 slides

Deep Learning

Deep Learning. PAINT AND CLAY. Where to buy clay and paint. Clay and bulk paint can be purchased locally or from: www.schoolsupplies.co.nz online@schoolsupplies.co.nz (order online) Or Freephone 0800.222.111. The clay used in these slides is air dried

333 views • 13 slides

Deep Learning

Deep Learning. Deep learning?. MLP-> NN-> Deep Learning Deep networks trained with backpropagation (without unsupervised pretraining ) perform worse than shallow networks Supervised/unsupervised Each layer. Advantage. Deep Architectures can be representationally efficient

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Deep Learning!!!!

Deep Learning!!!!. Decorate your gingerbread decoration but DON’T eat it !!!!! p.s. yet!!. Now start to eat it and think about how it tastes!!. Write a paragraph to describe your ginger bread decoration!. Share your description with a partner! Write one description between you!.

373 views • 8 slides

Deep learning

Deep learning. Harrison Littler and Peter Jones Monday 15 th April 2013. Do any of these statements match your experience? . Reflecting on our current practice. Aim of this session:

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Deep Learning

Deep Learning. Early Work Why Deep Learning Stacked Auto Encoders Deep Belief Networks. Deep Learning Overview. Train networks with many layers (vs. shallow nets with just a couple of layers) Multiple layers work to build an improved feature space

778 views • 34 slides

Deep Learning

Deep Learning. Roger S. Gaborski. Visual System Visual cortex is defined in terms of hierarchical regions: V1  V2  V3  V4  V5  MST Some regions may be bypassed, depending on the features being extracted

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Deep Learning

Deep Learning. Why?. Source: Huang et al., Communications ACM 01/2014.

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Deep Learning

Deep Learning. Bing-Chen Tsai 1/21. outline. Neural networks Graphical model Belief nets Boltzmann machine DBN Reference. Neural networks. Supervised learning The training data consists of input information with their corresponding output information. Unsupervised learning

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Deep Learning for Vision

Deep Learning for Vision. Adam Coates Stanford University (Visiting Scholar: Indiana University, Bloomington). What do we want ML to do?. Given image, predict complex high-level patterns:. “Cat”. Object recognition. Detection. Segmentation. [Martin et al., 2001]. How is ML done?.

1.07k views • 78 slides

Deep Learning

Deep Learning. Supervised Learning. Works well if we have right features Domains like computer vision, audio processing, and natural language processing requires feature engineering. Feature Engineering is tough job Manually finding right features does not scale well. What?.

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Deep Learning

Deep Learning. Yann Le Cun The Courant Institute of Mathematical Sciences New York University http://yann.lecun.com. The Challenges of Machine Learning. How can we use learning to progress towards AI? Can we find learning methods that scale?

1.16k views • 69 slides

Assessment for Deep Learning

Assessment for Deep Learning. Dr. Julian Hermida Algoma University Workshop on Teaching and Learning February 25, 2009. Agenda. Objective: Student assessment that promotes deep learning. Interactive talk: Review of main concepts. Assessment and deep learning.

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Scaffolding: for deep learning

Scaffolding: for deep learning. 3. Scaffolding/ Clear Guidelines for Students. MINO: ‘Making Interdisciplinary Connections’. GALGUERA: ‘Encouraging Student Teachers to Document Reflective Practice’. Snapshot Tool –

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Deep learning

And Deep Networks for Natural Language Processing. Deep learning. Overview of the Talk. Overview of Deep Learning Justification Properties of Deep Learning Neural Networks 101 Brief History of Deep Learning Implementation Details RBM’s and DBN’s Auto-Encoders Deep Learning for NLP

1.51k views • 78 slides

SUPPORTING NOTES FOR PRACTICAL UNIT

SUPPORTING NOTES FOR PRACTICAL UNIT. “In addition to presenting the extract (s) from their selected play, the students will need to provide supporting notes which will demonstrate their understanding and application of the ideas of their chosen practitioner”. Brief overview.

187 views • 7 slides

Deep Learning

One of the most talked-about buzzwords of late is u201cdeep learning,u201d which is an area of machine learning that enables computers to be trained and learn. Deep learning-which can be accomplished through architectures such as artificial neural networks-imitates the way the human brain works by processing data and creating patterns for use in decision making. Source : https://www.onlsol.com/blog/deep-learning-software/

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Deep Learning

Deep Learning. Artificial Neural Networks. Artificial neural networks (ANNs) are cognitive models Inspired by the structure and function of biological neurons Appear in perceptron and back propagation prediction systems Tries to model the complex relationships between inputs

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