Deep Learning Essentials for GenAI

Mar 07, 2025

0 likes | 20 Vues

Explore the key insights from a Generative AI course for managers, unpacking the mechanics of neural networks and their pivotal role in AI models. Here, we will check how GPT models and GANs drive innovation while providing actionable tips for leaders. Discover how a Gen AI course for managers equips you to harness AI's potential for organizational success.<br>

Share Presentation

Embed Code

Link

Suhas14

Télécharger la présentation

Deep Learning Essentials for GenAI

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

1.19k 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

344 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

585 views • 10 slides

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!.

393 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:

1.09k views • 17 slides

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

789 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

466 views • 28 slides

Deep Learning

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

611 views • 39 slides

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

699 views • 32 slides

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?.

583 views • 23 slides

Learning Deep Architectures for AI

Learning Deep Architectures for AI. Yoshua Bengio. Deep Architecture in our Mind.  Humans organize their ideas and concepts hierarchically  Humans first learn simpler concepts and then compose them to represent more abstract ones

707 views • 16 slides

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?

1.17k 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.

486 views • 19 slides

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 –

287 views • 8 slides

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

1.52k views • 78 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/

110 views • 3 slides

Deep Learning for Big Data

Deep Learning for Big Data. P. Baldi University of California, Irvine. Department of Computer Science Institute for Genomics and Bioinformatics Center for Machine Learning and Intelligent Systems. Intelligence in Brains and Machines. Intelligence in Brains and Machines. LEARNING.

457 views • 22 slides

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

1.59k views • 119 slides

More Related