Deep Learning Models and Their Parameters
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Questions and Answers

The GPT-3 model has 175 million parameters.

False (B)

To train the BERT model, it costs approximately $2,000.

True (A)

RoBERTa was trained using 500 GPUs for one week.

False (B)

PaLM requires around $25 million to train.

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The energy consumption for PaLM is equivalent to what 100 households use in a year.

<p>False (B)</p> Signup and view all the answers

An image of size 200x200 pixels with 3 colors requires a calculation of $200 \times 200 \times 3$ weights to determine outputs.

<p>False (B)</p> Signup and view all the answers

In a 4-way neural network, multiple outputs can be processed from a single input.

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The total number of categories in the neural network output can affect the number of weights required.

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Deep learning mainly focuses on processing linear functions without any complex transformations.

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For an image input of size 200x200 pixels with 500 output categories, the required weights can be calculated without considering color depth.

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In a perceptron learning algorithm, if the score $y$ is positive, the output returned is -1.

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The perceptron learning algorithm starts by setting the weights to random values.

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If $y'$ is less than 0 and the label $l'$ is greater than 0, the weights $w'$ are decreased.

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A perceptron computes $y' = extstyleigsum w' x' > 0$ to determine if the score is positive.

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The perceptron learning algorithm adjusts the weights based solely on the output from the previous iteration.

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If the score $y'$ is greater than 0 and the label $l'$ is less than 0, then the weights $w'$ should be increased.

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The learning rate $ heta$ is used to determine the magnitude of weight adjustments in the perceptron algorithm.

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Bias is always added after multiplying the weights with inputs in the perceptron.

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Deep Learning systems require large amounts of data to be effective.

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Deep Learning algorithms thrive on small datasets.

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Deep Learning is also known for its ability to work well with structured data only.

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The phrase 'Deep Learning is Big Data Hungry' indicates a strong dependency on extensive datasets.

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Deep Learning can function adequately without any data.

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Big Data provides valuable resources for training Deep Learning models.

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Deep Learning is inefficient when working with high-dimensional data compared to traditional learning methods.

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Deep Learning requires less computational power compared to classic machine learning algorithms.

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Frank Rosenblatt is known as one of the pioneers of deep learning.

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Charles W. Wightman contributed significantly to deep learning in the late 1980s.

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The first appearance of deep learning as a recognized field occurred in 1997.

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The term 'deep learning' has been in use since the 1960s.

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Deep learning techniques were first used in the 1970s.

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UVA Deep Learning Course was established in 2006.

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Deep learning is a subfield of machine learning that deals with neural networks.

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The digital age significantly influenced the growth of deep learning research after 2010.

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Deep learning has been extensively used in computer vision applications.

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The Perceptron algorithm was introduced in the 1980s.

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Neural networks are inspired by the structure of the human brain.

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Deep learning has no applications in natural language processing.

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The field of deep learning was stagnant for many decades before recent developments.

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The first deep learning models were implemented in the 1980s with wide practical success.

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The first AI winter occurred between the years 1969 and 1983.

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XOR can easily be solved by a perceptron.

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Recurrent networks were introduced by Rumelhart during the first AI winter.

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The second AI winter took place from 1995 to 2006.

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Support vector machines (SVMs) were developed by Cortes and Vapnik in 1995.

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In the context of AI, manifold learning refers to methods developed before 2000.

<p>False (B)</p> Signup and view all the answers

Sparse coding techniques, such as LASSO, were introduced in 1995.

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Deep learning gained significant attention starting in 2006.

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Decision trees and Random Forests were developed prior to the second AI winter.

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Backpropagation was a notable learning algorithm developed during the second AI winter.

<p>False (B)</p> Signup and view all the answers

The neocognitron, an early convolutional neural network, was introduced by Fukushima.

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The term 'AI winter' refers to periods of increased funding and interest in AI research.

<p>False (B)</p> Signup and view all the answers

Kernel methods were developed during the first AI winter.

<p>False (B)</p> Signup and view all the answers

Flashcards

Model Parameter Scaling

The increasing number of parameters in large language models like BERT, RoBERTa, GPT-3, and PaLM. These models require substantial computing resources and cost.

BERT Model Cost

A large language model, containing 354 million parameters, estimated to cost approximately $2,000 to train.

RoBERTa Training Cost

Training a RoBERTa language model using 1000 GPUs for one week, estimated to cost around $350,000.

GPT-3 Training Cost

Training GPT-3, a large language model with 175 billion parameters, is approximately $3 million, requiring 1500 GPUs and 2 months of computing time.

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PaLM Training Cost

Training PaLM used 6144 TPUs and cost approximately $25 million. This represents significant computational resources.

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Neural Network Outputs

Neural networks can have multiple outputs, adapting the network to different tasks.

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4-way Neural Network

A neural network with four distinct output nodes, capable of handling multi-dimensional data, like image recognition with 4 predefined categories.

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Image Input Size

Describes the dimensions of an image fed into a neural network; e.g., 200x200 pixels.

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Weights in a Neural Network

Numerical values adjusting signal strength between neural network nodes.

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Neural Network Categories

A set of classification choices or categories that a network outputs; e.g. Recognizing objects in an image.

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Perceptron Learning Algorithm

An algorithm that adjusts weights to improve a perceptron's accuracy in classifying input data.

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Update Weights

Adjusting the weights (w) of a perceptron based on the learning algorithm's rules.

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Input Data (x)

Data used to train or classify via the perceptron.

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Target Labels (l)

Desired output or class for each input data point.

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Predicted Output (y)

The result of applying input data and weights to the perceptron model.

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Score Calculation

Process of calculating the predicted output (y) using the weights, input, and a bias.

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Weight Update (w)

Adapting to an input based on errors from the perceptron's guess.

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Learning Rate (η)

A parameter that controls the magnitude of weight updates in the learning algorithm.

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Deep Learning

A subfield of machine learning that uses artificial neural networks with multiple layers to extract complex patterns from data.

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Artificial Neural Networks

Computational models inspired by the structure and function of the human brain.

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Multiple Layers

The key characteristic of deep learning models, allowing them to learn hierarchical representations of data.

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Machine Learning

A type of artificial intelligence where computer systems learn from data without being explicitly programmed.

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Frank Rosenblatt

A figure in the early development of neural networks.

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Charles W. Wightman

Also contributed to early neural network developments.

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1958

A pivotal year in the development of Perceptrons, an early neural network model.

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1969

Another year recognizing neural networks.

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Perceptron

A simple neural network that processes data based on weighted inputs.

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1970s

A period where neural network research was largely dormant.

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1986

A year that saw important results regarding backpropagation, an algorithm to train neural networks.

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Backpropagation

An algorithm to adjust the weights within a neural network during training.

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1997

A year that has important milestones in neural network development.

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2006

A landmark year in deep learning renaissance as there was a renewed interest in the field.

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2012

A year that shows results achieved with significant improvements in deep learning neural models.

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Deep Learning and Big Data

Deep learning models require a large amount of data for training.

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Data for Deep Learning

Deep learning models need lots of training data to function.

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Model Training Cost

Creating and training complex deep learning models can be expensive.

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Large Language Models

Models that process and understand human language.

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Computational Resources

Hardware like GPUs and TPUs are used during training.

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Model Complexity

Deep learning models can be immensely complex, requiring substantial resources.

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Deep Learning Optimization

Methods used to improve deep learning models' performance.

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VISLab

A research area focused on deep learning development

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AI Winter (1969-1983)

A period where AI research funding decreased due to unmet expectations and disappointing results.

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Backpropagation

A learning algorithm for Multi-Layer Perceptrons (MLPs).

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Recurrent Networks

Networks capable of processing variable-length input sequences.

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CNNs

Convolutional Neural Networks, used for processing image data.

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AI Winter (1995-2006)

A second period of reduced AI research funding, this time alongside advancements in Machine Learning.

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Kernel Methods

Machine learning techniques, like SVMs, that use kernel functions to map data to higher dimensions.

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Support Vector Machines (SVMs)

A powerful machine learning algorithm for classification tasks.

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Ensemble Methods

Combining multiple models to improve prediction accuracy.

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Decision Trees

Machine learning algorithms that build a decision tree to classify data.

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Random Forests

Combining multiple decision trees to increase accuracy and robustness.

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Manifold Learning

Techniques for learning the underlying structure of data, which often lies on lower dimensionality manifolds.

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Sparse Coding

Techniques to represent data using a small set of relevant features.

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Rise of Deep Learning (2006-present)

Deep learning research and application increased significantly.

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Study Notes

A Brief History of Deep Learning

  • Deep learning is a field of artificial intelligence that has shown significant progress over time.
  • Key figures and developments in deep learning, including perceptrons, Adaline, and backpropagation, are marked on a timeline.
  • Milestones include Perceptrons by Rosenblatt (1958), Adaline by Widrow and Hoff (1960), Perceptrons by Minsky & Papert (1969), Backpropagation (1974), LSTM by Hochreiter and Schmidhuber (1998), Deep Learning (2006), Imagenet (2009), AlexNet (2012), Resnet & GO (2015).

First Appearance (Roughly)

  • A timeline shows the approximate first appearance of various deep learning concepts.
  • Perceptrons were introduced by Frank Rosenblatt in 1958.
  • Adaline was developed by Widrow and Hoff in 1960.
  • Perceptrons by Minsky and Papert in 1969.
  • Backpropagation by several researchers, including Werbos and Rumelhart, Hinton, and Williams, emerged in the 1970s and 1980s.
  • Milestones include LSTM, OCR, Deep Learning, Imagenet, AlexNet, Resnet, and GO.

Rosenblatt: The Design of an Intelligent Automaton (1958)

  • Rosenblatt's work described a machine resembling a biological brain.
  • The machine would recognize, remember, and respond like a human mind.
  • Graphs and diagrams illustrate the organization of biological brains and perceptrons.

Perceptrons

  • McCulloch and Pitts introduced binary inputs and outputs, but had no learning component.
  • Rosenblatt proposed perceptrons as a model for binary classifications.
  • A perceptron comprises weights for each input (xi), added to a bias (b = w0x0), to produce a weighted sum (y= Σ(wjxj) + b).
  • The output is 1 if the weighted sum is positive and -1 otherwise.

Training a Perceptron

  • The primary innovation was a learning algorithm for perceptrons.
  • Weights (wj) assigned randomly
  • Samples (xi, Li) used to compute a weighted sum (y)
  • If the output is incorrect, the weights are adjusted using a learning rate (η\etaη)

From a Single Output to Many Outputs

  • A perceptron was originally designed for binary decisions.
  • To handle multiple decisions (e.g., digit classification), multiple outputs can be appended to create a neural network.
  • The diagram shows a 4-way neural network with input and output layers.

From a Single Output to Many Outputs (Quiz)

  • Calculating the number of weights necessary for a visual input and a large number of categories demonstrates the significant increase in complexity that large datasets represent for neural networks.
  • The calculations show large-scale datasets are needed.

XOR & 1-layer Perceptrons

  • Initially, perceptrons failed with simple non-linear tasks like XOR.
  • XOR's input patterns cannot be separated by a single line.
  • The weighted sum needs to evaluate combinations, beyond simple linear separation.

Multi-layer Perceptrons to the Rescue

  • Minsky did not establish XOR as unsolvable by neural networks.
  • Multi-layer perceptrons (MLPs) can be used to solve the XOR problem.
  • MLPs use multiple layers and nonlinearities (like sigmoid functions) to improve the model's capacity.

Multi-layer Perceptrons to the Rescue (Why not Rosenblatt’s method)

  • Rosenblatt's algorithm cannot train intermediate layers of an MLP.
  • The absence of a clear learning target in hidden layers of perceptrons made training them very problematic.

The "AI winter" despite notable successes

  • A timeline shows the approximate first appearance of various deep learning concepts.
  • Progress was made through the period, but there was also a period of reduced funding and interest after initial promise.

The first "AI winter" (1969-1983)

  • The prevalent view was that perceptrons could not solve basic logical problems, therefore, investment declined.
  • However, significant discoveries occurred during this period, such as the development of backpropagation, recurrent networks and CNNs.

The second "AI winter" (1995-2006)

  • New machine learning models were developed with similar accuracy but provided mathematical models and proof.
  • Kernel methods (SVMs, decision trees, random forests) and manifold learning were developed.

The Rise of Deep Learning (2006-Present)

  • Significant advancement and progress in the field.
  • Large-scale datasets became available, allowing training of very deep neural networks.
  • The increasing computing power of hardware (especially Graphics Processing Units or GPUs) made large-scale training possible.

The thaw of the "AI winter"

  • The timeline shows the approximate first appearance of various deep learning concepts.
  • Backpropagation, significant progress in various sub-fields such as RNNs, CNNs and more, helped overcome some prior limitations in the field.

The Rise of Deep learning

  • Hinton and Salakhutdinov developed multilayer feed-forward networks that could be pretrained layer by layer and fine-tuned with backpropagation.
  • Deep Belief Networks utilize Boltzmann machines.

Neural Networks: A decade ago

  • Challenges in the field included limited processing power, small datasets, and difficulties in training deeply layered perceptrons effectively

Neural Networks: Today

  • The limitations described in the previous point are mitigated by advancements in technology and hardware, thereby enabling effective training.

Deep Learning arrives

  • Deep learning training became easier because of the layer-by-layer approach.
  • Multiple layers of networks yield better performance, but the training process for single layers is comparatively less complex.

Deep Learning Renaissance

  • Timeline, which marks the approximate first appearance of various deep learning concepts, showing significant growth in the field since its initial development.

Turns out: Deep Learning is Big Data Hungry!

  • ImageNet dataset introduced in 2009 was a large-scale visual dataset that became crucial for advances in deep learning.
  • The dataset consisted of 1 million images with 1000 categories, crucial to evaluation and enabling the training of deep learning models in various fields.

ImageNet 2012 Winner: AlexNet

  • AlexNet was a significant architecture for image processing, showcasing the increased complexity and number of weights needed for training deep learning models with large datasets.
  • More weights were needed for training deep learning models when training image recognition programs.

Why now?

  • Advancements in hardware and datasets are key factors in recent deep learning successes.
  • Processing power, availability of larger datasets, and improved algorithms have combined to enable significant progress.
  • Graph demonstrating evolution of computer hardware, datasets, and algorithms, showcasing increasing power.
  • Recent advances in deep learning models.

The current scaling of models

  • Scaling of models and associated computational requirements for training deep learning models.
  • The increasing cost of training large language models highlights the large computational power and data resources required for deep learning models.

Deep Learning: The "Field"

  • Deep learning has made significant advancements in scientific study.
  • Publication counts from Google Scholar highlight deep learning's increasing importance in different scientific fields.

Deep Learning Golden Era

  • Deep learning's journey through various stages of development, highlighting key milestones like the development of perceptrons, backpropagation, and deep learning itself (with associated figures).
  • Timeline of key developments from perceptrons to modern tools to assist in development.

How research gets done part I

  • Deep learning research involves theoretical foundations and practical application.
  • Begin by solidifying fundamentals and reading various research articles.

Deep Learning in practice

  • Examples of practical applications of deep learning in various domains, such as image recognition and video classification, were used in the years 2013-2016.

Deep Learning even for the Arts

  • Deep learning methods are used to create and edit images.
  • Illustrations showcasing various types of art created by deep learning models.

The "wow" what Deep learning can do! - 2022 edition

  • A summary of interesting and new applications for deep learning and its capabilities which were recently developed.

AI beyond human capacity

  • Deep learning models have demonstrated accomplishments surpassing human capabilities in complex domains like Go.
  • The computations required, far exceeding the number of atoms in the universe, are illustrative of the vast computational power needed.

Vision-text Multi-modal Learning

  • Focuses on multimodal approaches, which utilize both visual and textual information, for a more complete and integrated representation of data.
  • Research highlights the scale of training data necessary.

Generative Pretraining

  • Deep learning models can produce new or unique generated data types such as texts.
  • It involves pretraining massive amounts of data to generate new, original text.

Music from AI

  • AI is being used to generate music.
  • Demonstration through code and example prompts.

Deep learning in robotics too

  • Recent development of deep learning in robotics.
  • Limitations of prior approaches and how deep learning is helping.

There's a lot more

  • Resources to continue learning about current research in deep learning.
  • Links to relevant newsletters, relevant websites, courses and news sources.

Conclusion

  • Deep learning has made significant progress across several domains, with datasets playing an increasingly important role.
  • The field keeps evolving rapidly.

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Related Documents

Lecture 1 - Introduction PDF

Description

This quiz explores various deep learning models, their parameters, and the costs associated with training them. It covers important concepts such as weight calculations, neural network outputs, and energy consumption. Test your knowledge on the fundamentals of deep learning and its applications.

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