BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Springshare//LibCal//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-MS-OLK-FORCEINSPECTOROPEN:TRUE
X-WR-TIMEZONE:America/Los_Angeles
X-PUBLISHED-TTL:PT15M
BEGIN:VEVENT
DTSTART:20240409T190000Z
DTEND:20240409T203000Z
DTSTAMP:20240409T000000Z
SUMMARY:Machine Learning\, Deep Learning\, and Natural Language Processing
DESCRIPTION:Caltech Library is offering an introductory sequence of 
 workshops on Machine Learning\, Deep Learning\, and Natural Language 
 Processing. These workshops are designed for researchers in all areas with 
 an interest in machine learning and language processing. \n\nPart One: 
 Introduction to Machine Learning\n\nThe first workshop introduces 
 traditional machine learning techniques using the Python Scikit Learn 
 package.\n\nPart Two: Introduction to Deep Learning\n\nWorkshops 2-5 cover 
 deep learning and neural networks using the Python Keras package and 
 TensorFlow. Topics covered will include the model building\, training\, 
 optimization and performance measures\, and the two principal types of 
 neural network: convolutional and recurrent.\n\nPart Three: Natural 
 Language Processing\n\nWorkshops 6 and 7 will focus on Natural Language 
 Processing\, starting with classic NLP processing methods using NLTK and 
 SpaCy\, and closing with the application of neural networks to NLP\, 
 particularly the revolutionary Transformer architecture that powers 
 Generative Artificial Intelligence.\n\nAbout the workshops\n\nThe workshops 
 are designed to be “hands on” and require some familiarity with basic 
 Python programming and the Unix Shell\, such as that provided through the 
 Library’s introductory workshops. Although concepts and skills are 
 cumulative through the sequence of workshops\, the workshops will be as 
 free-standing as possible. The Deep Learning sequence will be the most 
 inter-dependent. Recordings of workshops will be made available to 
 registered participants.\n\nWorkshops will be online\, on selected Tuesdays 
 and Fridays at Noon. There is one registration for all seven workshops. The 
 Zoom link for all workshops will be included in the registration 
 confirmation.\n\n\n	\n	Tuesday\, April 9\, 2024: An Introduction to Machine 
 Learning with Scikit Learn\n\n	\n		\n		Topics: Scikit Learn\, processing 
 data\, algorithms\, clustering\, dimensionality 
 reduction\n		\n	\n	\n	\n	Tuesday\, April 16\, 2024: Deep Learning 1: 
 Introduction to Deep Learning and Neural Networks\n\n	\n		\n		Topics: 
 characteristics\, learning\, loss\, optimization\, training\, appropriate 
 types of problems\n		\n	\n	\n	\n	Friday\, April 26\, 2024: Deep Learning 2: 
 Building a Neural Network for Classification\n\n	\n		\n		Topics: Keras\, 
 building the model\, training\, prediction\, classification\, 
 performance\n		\n	\n	\n	\n	Friday\, May 3\, 2024: Deep Learning 3: The 
 Neural Network Training Process\n\n	\n		\n		Topics: regression\, 
 optimization\, monitoring\, performance\, fitting\n		\n	\n	\n	\n	Friday\, 
 May 10\, 2024: Deep Learning 4: Advanced Neural Network Layer 
 Types\n\n	\n		\n		Topics: convolutional neural networks (image 
 classification)\, recurrent neural networks (for sequential 
 data)\n		\n	\n	\n	\n	Friday\, May 17\, 2024: Introduction to Natural 
 Language Processing\n\n	\n		\n		Topics: preprocessing text\, word context 
 and semantics\, parts of speech and entity recognition\, sentiment 
 analysis\, word vectors\n		\n	\n	\n	\n	Friday\, May 24\, 2024: NLP with 
 Transformer Models\n\n	\n		\n		Topics: the evolution of Large Language 
 Models\, “Attention” and the Transformer architecture\, using the 
 Python Transformers package for NLP tasks\n		\n	\n	\n\n
LOCATION:Zoom Session (Online)
ORGANIZER;CN="Stephen Davison":MAILTO:sdavison@caltech.edu
CATEGORIES:
CONTACT;CN="Stephen Davison":MAILTO:sdavison@caltech.edu
STATUS:CONFIRMED
UID:LibCal-12317045
URL:https://libcal.caltech.edu/event/12317045
X-MICROSOFT-CDO-BUSYSTATUS:BUSY
BEGIN:VALARM
TRIGGER:-PT15M
ACTION:DISPLAY
DESCRIPTION:Reminder
END:VALARM
END:VEVENT
BEGIN:VEVENT
DTSTART:20240416T190000Z
DTEND:20240416T203000Z
DTSTAMP:20240416T000000Z
SUMMARY:Machine Learning\, Deep Learning\, and Natural Language Processing
DESCRIPTION:Caltech Library is offering an introductory sequence of 
 workshops on Machine Learning\, Deep Learning\, and Natural Language 
 Processing. These workshops are designed for researchers in all areas with 
 an interest in machine learning and language processing. \n\nPart One: 
 Introduction to Machine Learning\n\nThe first workshop introduces 
 traditional machine learning techniques using the Python Scikit Learn 
 package.\n\nPart Two: Introduction to Deep Learning\n\nWorkshops 2-5 cover 
 deep learning and neural networks using the Python Keras package and 
 TensorFlow. Topics covered will include the model building\, training\, 
 optimization and performance measures\, and the two principal types of 
 neural network: convolutional and recurrent.\n\nPart Three: Natural 
 Language Processing\n\nWorkshops 6 and 7 will focus on Natural Language 
 Processing\, starting with classic NLP processing methods using NLTK and 
 SpaCy\, and closing with the application of neural networks to NLP\, 
 particularly the revolutionary Transformer architecture that powers 
 Generative Artificial Intelligence.\n\nAbout the workshops\n\nThe workshops 
 are designed to be “hands on” and require some familiarity with basic 
 Python programming and the Unix Shell\, such as that provided through the 
 Library’s introductory workshops. Although concepts and skills are 
 cumulative through the sequence of workshops\, the workshops will be as 
 free-standing as possible. The Deep Learning sequence will be the most 
 inter-dependent. Recordings of workshops will be made available to 
 registered participants.\n\nWorkshops will be online\, on selected Tuesdays 
 and Fridays at Noon. There is one registration for all seven workshops. The 
 Zoom link for all workshops will be included in the registration 
 confirmation.\n\n\n	\n	Tuesday\, April 9\, 2024: An Introduction to Machine 
 Learning with Scikit Learn\n\n	\n		\n		Topics: Scikit Learn\, processing 
 data\, algorithms\, clustering\, dimensionality 
 reduction\n		\n	\n	\n	\n	Tuesday\, April 16\, 2024: Deep Learning 1: 
 Introduction to Deep Learning and Neural Networks\n\n	\n		\n		Topics: 
 characteristics\, learning\, loss\, optimization\, training\, appropriate 
 types of problems\n		\n	\n	\n	\n	Friday\, April 26\, 2024: Deep Learning 2: 
 Building a Neural Network for Classification\n\n	\n		\n		Topics: Keras\, 
 building the model\, training\, prediction\, classification\, 
 performance\n		\n	\n	\n	\n	Friday\, May 3\, 2024: Deep Learning 3: The 
 Neural Network Training Process\n\n	\n		\n		Topics: regression\, 
 optimization\, monitoring\, performance\, fitting\n		\n	\n	\n	\n	Friday\, 
 May 10\, 2024: Deep Learning 4: Advanced Neural Network Layer 
 Types\n\n	\n		\n		Topics: convolutional neural networks (image 
 classification)\, recurrent neural networks (for sequential 
 data)\n		\n	\n	\n	\n	Friday\, May 17\, 2024: Introduction to Natural 
 Language Processing\n\n	\n		\n		Topics: preprocessing text\, word context 
 and semantics\, parts of speech and entity recognition\, sentiment 
 analysis\, word vectors\n		\n	\n	\n	\n	Friday\, May 24\, 2024: NLP with 
 Transformer Models\n\n	\n		\n		Topics: the evolution of Large Language 
 Models\, “Attention” and the Transformer architecture\, using the 
 Python Transformers package for NLP tasks\n		\n	\n	\n\n
LOCATION:Zoom Session (Online)
ORGANIZER;CN="Stephen Davison":MAILTO:sdavison@caltech.edu
CATEGORIES:
CONTACT;CN="Stephen Davison":MAILTO:sdavison@caltech.edu
STATUS:CONFIRMED
UID:LibCal-12317046
URL:https://libcal.caltech.edu/event/12317046
X-MICROSOFT-CDO-BUSYSTATUS:BUSY
BEGIN:VALARM
TRIGGER:-PT15M
ACTION:DISPLAY
DESCRIPTION:Reminder
END:VALARM
END:VEVENT
BEGIN:VEVENT
DTSTART:20240426T190000Z
DTEND:20240426T203000Z
DTSTAMP:20240426T000000Z
SUMMARY:Machine Learning\, Deep Learning\, and Natural Language Processing
DESCRIPTION:Caltech Library is offering an introductory sequence of 
 workshops on Machine Learning\, Deep Learning\, and Natural Language 
 Processing. These workshops are designed for researchers in all areas with 
 an interest in machine learning and language processing. \n\nPart One: 
 Introduction to Machine Learning\n\nThe first workshop introduces 
 traditional machine learning techniques using the Python Scikit Learn 
 package.\n\nPart Two: Introduction to Deep Learning\n\nWorkshops 2-5 cover 
 deep learning and neural networks using the Python Keras package and 
 TensorFlow. Topics covered will include the model building\, training\, 
 optimization and performance measures\, and the two principal types of 
 neural network: convolutional and recurrent.\n\nPart Three: Natural 
 Language Processing\n\nWorkshops 6 and 7 will focus on Natural Language 
 Processing\, starting with classic NLP processing methods using NLTK and 
 SpaCy\, and closing with the application of neural networks to NLP\, 
 particularly the revolutionary Transformer architecture that powers 
 Generative Artificial Intelligence.\n\nAbout the workshops\n\nThe workshops 
 are designed to be “hands on” and require some familiarity with basic 
 Python programming and the Unix Shell\, such as that provided through the 
 Library’s introductory workshops. Although concepts and skills are 
 cumulative through the sequence of workshops\, the workshops will be as 
 free-standing as possible. The Deep Learning sequence will be the most 
 inter-dependent. Recordings of workshops will be made available to 
 registered participants.\n\nWorkshops will be online\, on selected Tuesdays 
 and Fridays at Noon. There is one registration for all seven workshops. The 
 Zoom link for all workshops will be included in the registration 
 confirmation.\n\n\n	\n	Tuesday\, April 9\, 2024: An Introduction to Machine 
 Learning with Scikit Learn\n\n	\n		\n		Topics: Scikit Learn\, processing 
 data\, algorithms\, clustering\, dimensionality 
 reduction\n		\n	\n	\n	\n	Tuesday\, April 16\, 2024: Deep Learning 1: 
 Introduction to Deep Learning and Neural Networks\n\n	\n		\n		Topics: 
 characteristics\, learning\, loss\, optimization\, training\, appropriate 
 types of problems\n		\n	\n	\n	\n	Friday\, April 26\, 2024: Deep Learning 2: 
 Building a Neural Network for Classification\n\n	\n		\n		Topics: Keras\, 
 building the model\, training\, prediction\, classification\, 
 performance\n		\n	\n	\n	\n	Friday\, May 3\, 2024: Deep Learning 3: The 
 Neural Network Training Process\n\n	\n		\n		Topics: regression\, 
 optimization\, monitoring\, performance\, fitting\n		\n	\n	\n	\n	Friday\, 
 May 10\, 2024: Deep Learning 4: Advanced Neural Network Layer 
 Types\n\n	\n		\n		Topics: convolutional neural networks (image 
 classification)\, recurrent neural networks (for sequential 
 data)\n		\n	\n	\n	\n	Friday\, May 17\, 2024: Introduction to Natural 
 Language Processing\n\n	\n		\n		Topics: preprocessing text\, word context 
 and semantics\, parts of speech and entity recognition\, sentiment 
 analysis\, word vectors\n		\n	\n	\n	\n	Friday\, May 24\, 2024: NLP with 
 Transformer Models\n\n	\n		\n		Topics: the evolution of Large Language 
 Models\, “Attention” and the Transformer architecture\, using the 
 Python Transformers package for NLP tasks\n		\n	\n	\n\n
LOCATION:Zoom Session (Online)
ORGANIZER;CN="Stephen Davison":MAILTO:sdavison@caltech.edu
CATEGORIES:
CONTACT;CN="Stephen Davison":MAILTO:sdavison@caltech.edu
STATUS:CONFIRMED
UID:LibCal-12317047
URL:https://libcal.caltech.edu/event/12317047
X-MICROSOFT-CDO-BUSYSTATUS:BUSY
BEGIN:VALARM
TRIGGER:-PT15M
ACTION:DISPLAY
DESCRIPTION:Reminder
END:VALARM
END:VEVENT
BEGIN:VEVENT
DTSTART:20240503T190000Z
DTEND:20240503T203000Z
DTSTAMP:20240503T000000Z
SUMMARY:Machine Learning\, Deep Learning\, and Natural Language Processing
DESCRIPTION:Caltech Library is offering an introductory sequence of 
 workshops on Machine Learning\, Deep Learning\, and Natural Language 
 Processing. These workshops are designed for researchers in all areas with 
 an interest in machine learning and language processing. \n\nPart One: 
 Introduction to Machine Learning\n\nThe first workshop introduces 
 traditional machine learning techniques using the Python Scikit Learn 
 package.\n\nPart Two: Introduction to Deep Learning\n\nWorkshops 2-5 cover 
 deep learning and neural networks using the Python Keras package and 
 TensorFlow. Topics covered will include the model building\, training\, 
 optimization and performance measures\, and the two principal types of 
 neural network: convolutional and recurrent.\n\nPart Three: Natural 
 Language Processing\n\nWorkshops 6 and 7 will focus on Natural Language 
 Processing\, starting with classic NLP processing methods using NLTK and 
 SpaCy\, and closing with the application of neural networks to NLP\, 
 particularly the revolutionary Transformer architecture that powers 
 Generative Artificial Intelligence.\n\nAbout the workshops\n\nThe workshops 
 are designed to be “hands on” and require some familiarity with basic 
 Python programming and the Unix Shell\, such as that provided through the 
 Library’s introductory workshops. Although concepts and skills are 
 cumulative through the sequence of workshops\, the workshops will be as 
 free-standing as possible. The Deep Learning sequence will be the most 
 inter-dependent. Recordings of workshops will be made available to 
 registered participants.\n\nWorkshops will be online\, on selected Tuesdays 
 and Fridays at Noon. There is one registration for all seven workshops. The 
 Zoom link for all workshops will be included in the registration 
 confirmation.\n\n\n	\n	Tuesday\, April 9\, 2024: An Introduction to Machine 
 Learning with Scikit Learn\n\n	\n		\n		Topics: Scikit Learn\, processing 
 data\, algorithms\, clustering\, dimensionality 
 reduction\n		\n	\n	\n	\n	Tuesday\, April 16\, 2024: Deep Learning 1: 
 Introduction to Deep Learning and Neural Networks\n\n	\n		\n		Topics: 
 characteristics\, learning\, loss\, optimization\, training\, appropriate 
 types of problems\n		\n	\n	\n	\n	Friday\, April 26\, 2024: Deep Learning 2: 
 Building a Neural Network for Classification\n\n	\n		\n		Topics: Keras\, 
 building the model\, training\, prediction\, classification\, 
 performance\n		\n	\n	\n	\n	Friday\, May 3\, 2024: Deep Learning 3: The 
 Neural Network Training Process\n\n	\n		\n		Topics: regression\, 
 optimization\, monitoring\, performance\, fitting\n		\n	\n	\n	\n	Friday\, 
 May 10\, 2024: Deep Learning 4: Advanced Neural Network Layer 
 Types\n\n	\n		\n		Topics: convolutional neural networks (image 
 classification)\, recurrent neural networks (for sequential 
 data)\n		\n	\n	\n	\n	Friday\, May 17\, 2024: Introduction to Natural 
 Language Processing\n\n	\n		\n		Topics: preprocessing text\, word context 
 and semantics\, parts of speech and entity recognition\, sentiment 
 analysis\, word vectors\n		\n	\n	\n	\n	Friday\, May 24\, 2024: NLP with 
 Transformer Models\n\n	\n		\n		Topics: the evolution of Large Language 
 Models\, “Attention” and the Transformer architecture\, using the 
 Python Transformers package for NLP tasks\n		\n	\n	\n\n
LOCATION:Zoom Session (Online)
ORGANIZER;CN="Stephen Davison":MAILTO:sdavison@caltech.edu
CATEGORIES:
CONTACT;CN="Stephen Davison":MAILTO:sdavison@caltech.edu
STATUS:CONFIRMED
UID:LibCal-12317048
URL:https://libcal.caltech.edu/event/12317048
X-MICROSOFT-CDO-BUSYSTATUS:BUSY
BEGIN:VALARM
TRIGGER:-PT15M
ACTION:DISPLAY
DESCRIPTION:Reminder
END:VALARM
END:VEVENT
BEGIN:VEVENT
DTSTART:20240510T190000Z
DTEND:20240510T203000Z
DTSTAMP:20240510T000000Z
SUMMARY:Machine Learning\, Deep Learning\, and Natural Language Processing
DESCRIPTION:Caltech Library is offering an introductory sequence of 
 workshops on Machine Learning\, Deep Learning\, and Natural Language 
 Processing. These workshops are designed for researchers in all areas with 
 an interest in machine learning and language processing. \n\nPart One: 
 Introduction to Machine Learning\n\nThe first workshop introduces 
 traditional machine learning techniques using the Python Scikit Learn 
 package.\n\nPart Two: Introduction to Deep Learning\n\nWorkshops 2-5 cover 
 deep learning and neural networks using the Python Keras package and 
 TensorFlow. Topics covered will include the model building\, training\, 
 optimization and performance measures\, and the two principal types of 
 neural network: convolutional and recurrent.\n\nPart Three: Natural 
 Language Processing\n\nWorkshops 6 and 7 will focus on Natural Language 
 Processing\, starting with classic NLP processing methods using NLTK and 
 SpaCy\, and closing with the application of neural networks to NLP\, 
 particularly the revolutionary Transformer architecture that powers 
 Generative Artificial Intelligence.\n\nAbout the workshops\n\nThe workshops 
 are designed to be “hands on” and require some familiarity with basic 
 Python programming and the Unix Shell\, such as that provided through the 
 Library’s introductory workshops. Although concepts and skills are 
 cumulative through the sequence of workshops\, the workshops will be as 
 free-standing as possible. The Deep Learning sequence will be the most 
 inter-dependent. Recordings of workshops will be made available to 
 registered participants.\n\nWorkshops will be online\, on selected Tuesdays 
 and Fridays at Noon. There is one registration for all seven workshops. The 
 Zoom link for all workshops will be included in the registration 
 confirmation.\n\n\n	\n	Tuesday\, April 9\, 2024: An Introduction to Machine 
 Learning with Scikit Learn\n\n	\n		\n		Topics: Scikit Learn\, processing 
 data\, algorithms\, clustering\, dimensionality 
 reduction\n		\n	\n	\n	\n	Tuesday\, April 16\, 2024: Deep Learning 1: 
 Introduction to Deep Learning and Neural Networks\n\n	\n		\n		Topics: 
 characteristics\, learning\, loss\, optimization\, training\, appropriate 
 types of problems\n		\n	\n	\n	\n	Friday\, April 26\, 2024: Deep Learning 2: 
 Building a Neural Network for Classification\n\n	\n		\n		Topics: Keras\, 
 building the model\, training\, prediction\, classification\, 
 performance\n		\n	\n	\n	\n	Friday\, May 3\, 2024: Deep Learning 3: The 
 Neural Network Training Process\n\n	\n		\n		Topics: regression\, 
 optimization\, monitoring\, performance\, fitting\n		\n	\n	\n	\n	Friday\, 
 May 10\, 2024: Deep Learning 4: Advanced Neural Network Layer 
 Types\n\n	\n		\n		Topics: convolutional neural networks (image 
 classification)\, recurrent neural networks (for sequential 
 data)\n		\n	\n	\n	\n	Friday\, May 17\, 2024: Introduction to Natural 
 Language Processing\n\n	\n		\n		Topics: preprocessing text\, word context 
 and semantics\, parts of speech and entity recognition\, sentiment 
 analysis\, word vectors\n		\n	\n	\n	\n	Friday\, May 24\, 2024: NLP with 
 Transformer Models\n\n	\n		\n		Topics: the evolution of Large Language 
 Models\, “Attention” and the Transformer architecture\, using the 
 Python Transformers package for NLP tasks\n		\n	\n	\n\n
LOCATION:Zoom Session (Online)
ORGANIZER;CN="Stephen Davison":MAILTO:sdavison@caltech.edu
CATEGORIES:
CONTACT;CN="Stephen Davison":MAILTO:sdavison@caltech.edu
STATUS:CONFIRMED
UID:LibCal-12317049
URL:https://libcal.caltech.edu/event/12317049
X-MICROSOFT-CDO-BUSYSTATUS:BUSY
BEGIN:VALARM
TRIGGER:-PT15M
ACTION:DISPLAY
DESCRIPTION:Reminder
END:VALARM
END:VEVENT
BEGIN:VEVENT
DTSTART:20240517T190000Z
DTEND:20240517T203000Z
DTSTAMP:20240517T000000Z
SUMMARY:Machine Learning\, Deep Learning\, and Natural Language Processing
DESCRIPTION:Caltech Library is offering an introductory sequence of 
 workshops on Machine Learning\, Deep Learning\, and Natural Language 
 Processing. These workshops are designed for researchers in all areas with 
 an interest in machine learning and language processing. \n\nPart One: 
 Introduction to Machine Learning\n\nThe first workshop introduces 
 traditional machine learning techniques using the Python Scikit Learn 
 package.\n\nPart Two: Introduction to Deep Learning\n\nWorkshops 2-5 cover 
 deep learning and neural networks using the Python Keras package and 
 TensorFlow. Topics covered will include the model building\, training\, 
 optimization and performance measures\, and the two principal types of 
 neural network: convolutional and recurrent.\n\nPart Three: Natural 
 Language Processing\n\nWorkshops 6 and 7 will focus on Natural Language 
 Processing\, starting with classic NLP processing methods using NLTK and 
 SpaCy\, and closing with the application of neural networks to NLP\, 
 particularly the revolutionary Transformer architecture that powers 
 Generative Artificial Intelligence.\n\nAbout the workshops\n\nThe workshops 
 are designed to be “hands on” and require some familiarity with basic 
 Python programming and the Unix Shell\, such as that provided through the 
 Library’s introductory workshops. Although concepts and skills are 
 cumulative through the sequence of workshops\, the workshops will be as 
 free-standing as possible. The Deep Learning sequence will be the most 
 inter-dependent. Recordings of workshops will be made available to 
 registered participants.\n\nWorkshops will be online\, on selected Tuesdays 
 and Fridays at Noon. There is one registration for all seven workshops. The 
 Zoom link for all workshops will be included in the registration 
 confirmation.\n\n\n	\n	Tuesday\, April 9\, 2024: An Introduction to Machine 
 Learning with Scikit Learn\n\n	\n		\n		Topics: Scikit Learn\, processing 
 data\, algorithms\, clustering\, dimensionality 
 reduction\n		\n	\n	\n	\n	Tuesday\, April 16\, 2024: Deep Learning 1: 
 Introduction to Deep Learning and Neural Networks\n\n	\n		\n		Topics: 
 characteristics\, learning\, loss\, optimization\, training\, appropriate 
 types of problems\n		\n	\n	\n	\n	Friday\, April 26\, 2024: Deep Learning 2: 
 Building a Neural Network for Classification\n\n	\n		\n		Topics: Keras\, 
 building the model\, training\, prediction\, classification\, 
 performance\n		\n	\n	\n	\n	Friday\, May 3\, 2024: Deep Learning 3: The 
 Neural Network Training Process\n\n	\n		\n		Topics: regression\, 
 optimization\, monitoring\, performance\, fitting\n		\n	\n	\n	\n	Friday\, 
 May 10\, 2024: Deep Learning 4: Advanced Neural Network Layer 
 Types\n\n	\n		\n		Topics: convolutional neural networks (image 
 classification)\, recurrent neural networks (for sequential 
 data)\n		\n	\n	\n	\n	Friday\, May 17\, 2024: Introduction to Natural 
 Language Processing\n\n	\n		\n		Topics: preprocessing text\, word context 
 and semantics\, parts of speech and entity recognition\, sentiment 
 analysis\, word vectors\n		\n	\n	\n	\n	Friday\, May 24\, 2024: NLP with 
 Transformer Models\n\n	\n		\n		Topics: the evolution of Large Language 
 Models\, “Attention” and the Transformer architecture\, using the 
 Python Transformers package for NLP tasks\n		\n	\n	\n\n
LOCATION:Zoom Session (Online)
ORGANIZER;CN="Stephen Davison":MAILTO:sdavison@caltech.edu
CATEGORIES:
CONTACT;CN="Stephen Davison":MAILTO:sdavison@caltech.edu
STATUS:CONFIRMED
UID:LibCal-12317050
URL:https://libcal.caltech.edu/event/12317050
X-MICROSOFT-CDO-BUSYSTATUS:BUSY
BEGIN:VALARM
TRIGGER:-PT15M
ACTION:DISPLAY
DESCRIPTION:Reminder
END:VALARM
END:VEVENT
BEGIN:VEVENT
DTSTART:20240524T190000Z
DTEND:20240524T203000Z
DTSTAMP:20240524T000000Z
SUMMARY:Machine Learning\, Deep Learning\, and Natural Language Processing
DESCRIPTION:Caltech Library is offering an introductory sequence of 
 workshops on Machine Learning\, Deep Learning\, and Natural Language 
 Processing. These workshops are designed for researchers in all areas with 
 an interest in machine learning and language processing. \n\nPart One: 
 Introduction to Machine Learning\n\nThe first workshop introduces 
 traditional machine learning techniques using the Python Scikit Learn 
 package.\n\nPart Two: Introduction to Deep Learning\n\nWorkshops 2-5 cover 
 deep learning and neural networks using the Python Keras package and 
 TensorFlow. Topics covered will include the model building\, training\, 
 optimization and performance measures\, and the two principal types of 
 neural network: convolutional and recurrent.\n\nPart Three: Natural 
 Language Processing\n\nWorkshops 6 and 7 will focus on Natural Language 
 Processing\, starting with classic NLP processing methods using NLTK and 
 SpaCy\, and closing with the application of neural networks to NLP\, 
 particularly the revolutionary Transformer architecture that powers 
 Generative Artificial Intelligence.\n\nAbout the workshops\n\nThe workshops 
 are designed to be “hands on” and require some familiarity with basic 
 Python programming and the Unix Shell\, such as that provided through the 
 Library’s introductory workshops. Although concepts and skills are 
 cumulative through the sequence of workshops\, the workshops will be as 
 free-standing as possible. The Deep Learning sequence will be the most 
 inter-dependent. Recordings of workshops will be made available to 
 registered participants.\n\nWorkshops will be online\, on selected Tuesdays 
 and Fridays at Noon. There is one registration for all seven workshops. The 
 Zoom link for all workshops will be included in the registration 
 confirmation.\n\n\n	\n	Tuesday\, April 9\, 2024: An Introduction to Machine 
 Learning with Scikit Learn\n\n	\n		\n		Topics: Scikit Learn\, processing 
 data\, algorithms\, clustering\, dimensionality 
 reduction\n		\n	\n	\n	\n	Tuesday\, April 16\, 2024: Deep Learning 1: 
 Introduction to Deep Learning and Neural Networks\n\n	\n		\n		Topics: 
 characteristics\, learning\, loss\, optimization\, training\, appropriate 
 types of problems\n		\n	\n	\n	\n	Friday\, April 26\, 2024: Deep Learning 2: 
 Building a Neural Network for Classification\n\n	\n		\n		Topics: Keras\, 
 building the model\, training\, prediction\, classification\, 
 performance\n		\n	\n	\n	\n	Friday\, May 3\, 2024: Deep Learning 3: The 
 Neural Network Training Process\n\n	\n		\n		Topics: regression\, 
 optimization\, monitoring\, performance\, fitting\n		\n	\n	\n	\n	Friday\, 
 May 10\, 2024: Deep Learning 4: Advanced Neural Network Layer 
 Types\n\n	\n		\n		Topics: convolutional neural networks (image 
 classification)\, recurrent neural networks (for sequential 
 data)\n		\n	\n	\n	\n	Friday\, May 17\, 2024: Introduction to Natural 
 Language Processing\n\n	\n		\n		Topics: preprocessing text\, word context 
 and semantics\, parts of speech and entity recognition\, sentiment 
 analysis\, word vectors\n		\n	\n	\n	\n	Friday\, May 24\, 2024: NLP with 
 Transformer Models\n\n	\n		\n		Topics: the evolution of Large Language 
 Models\, “Attention” and the Transformer architecture\, using the 
 Python Transformers package for NLP tasks\n		\n	\n	\n\n
LOCATION:Zoom Session (Online)
ORGANIZER;CN="Stephen Davison":MAILTO:sdavison@caltech.edu
CATEGORIES:
CONTACT;CN="Stephen Davison":MAILTO:sdavison@caltech.edu
STATUS:CONFIRMED
UID:LibCal-12317051
URL:https://libcal.caltech.edu/event/12317051
X-MICROSOFT-CDO-BUSYSTATUS:BUSY
BEGIN:VALARM
TRIGGER:-PT15M
ACTION:DISPLAY
DESCRIPTION:Reminder
END:VALARM
END:VEVENT

END:VCALENDAR