�ݺ�ߣshows by User: ocampesato

�ݺ�ߣshows by User: ocampesato / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: ocampesato / Wed, 19 Feb 2020 05:53:36 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: ocampesato Working with tf.data (TF 2) /slideshow/working-with-tfdata-tf-2/228607132 h2osftf2data-200219055337
This session for beginners introduces tf.data APIs for creating data pipelines by combining various "lazy operators" in tf.data, such as filter(), map(), batch(), zip(), flatmap(), take(), and so forth. Familiarity with method chaining and TF2 is helpful (but not required). If you are comfortable with FRP, the code samples in this session will be very familiar to you. ]]>
This session for beginners introduces tf.data APIs for creating data pipelines by combining various "lazy operators" in tf.data, such as filter(), map(), batch(), zip(), flatmap(), take(), and so forth. Familiarity with method chaining and TF2 is helpful (but not required). If you are comfortable with FRP, the code samples in this session will be very familiar to you. ]]> Wed, 19 Feb 2020 05:53:36 GMT /slideshow/working-with-tfdata-tf-2/228607132 ocampesato@slideshare.net(ocampesato) Working with tf.data (TF 2) ocampesato This session for beginners introduces tf.data APIs for creating data pipelines by combining various "lazy operators" in tf.data, such as filter(), map(), batch(), zip(), flatmap(), take(), and so forth. Familiarity with method chaining and TF2 is helpful (but not required). If you are comfortable with FRP, the code samples in this session will be very familiar to you. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/h2osftf2data-200219055337-thumbnail.jpg?width=120&height=120&fit=bounds" /> This session for beginners introduces tf.data APIs for creating data pipelines by combining various "lazy operators" in tf.data, such as filter(), map(), batch(), zip(), flatmap(), take(), and so forth. Familiarity with method chaining and TF2 is helpful (but not required). If you are comfortable with FRP, the code samples in this session will be very familiar to you.

Working with tf.data (TF 2) from Oswald Campesato

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0 Introduction to TensorFlow 2 and Keras /slideshow/introduction-to-tensorflow-2-and-keras/195445363 gdgsf-tf2-191120070051
A fast-paced introduction to TensorFlow 2 regarding some important new features (such as generators and the @tf.function decorator), along with tf.data code samples and lazy operators. We'll also delve into the key ideas underlying CNNs, RNNs, and LSTMs, followed by some Keras-based code blocks. ]]>
A fast-paced introduction to TensorFlow 2 regarding some important new features (such as generators and the @tf.function decorator), along with tf.data code samples and lazy operators. We'll also delve into the key ideas underlying CNNs, RNNs, and LSTMs, followed by some Keras-based code blocks. ]]> Wed, 20 Nov 2019 07:00:51 GMT /slideshow/introduction-to-tensorflow-2-and-keras/195445363 ocampesato@slideshare.net(ocampesato) Introduction to TensorFlow 2 and Keras ocampesato A fast-paced introduction to TensorFlow 2 regarding some important new features (such as generators and the @tf.function decorator), along with tf.data code samples and lazy operators. We'll also delve into the key ideas underlying CNNs, RNNs, and LSTMs, followed by some Keras-based code blocks. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gdgsf-tf2-191120070051-thumbnail.jpg?width=120&height=120&fit=bounds" /> A fast-paced introduction to TensorFlow 2 regarding some important new features (such as generators and the @tf.function decorator), along with tf.data code samples and lazy operators. We'll also delve into the key ideas underlying CNNs, RNNs, and LSTMs, followed by some Keras-based code blocks.

Introduction to TensorFlow 2 and Keras from Oswald Campesato

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0 Introduction to Deep Learning /slideshow/introduction-to-deep-learning-163362743/163362743 ucsc-dl-intro-190813045533
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. ]]>
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. ]]> Tue, 13 Aug 2019 04:55:33 GMT /slideshow/introduction-to-deep-learning-163362743/163362743 ocampesato@slideshare.net(ocampesato) Introduction to Deep Learning ocampesato A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ucsc-dl-intro-190813045533-thumbnail.jpg?width=120&height=120&fit=bounds" /> A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.

Introduction to Deep Learning from Oswald Campesato

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0 Introduction to TensorFlow 2 /slideshow/introduction-to-tensorflow-2-152333392/152333392 salesforcetf2-190628053455
A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired). Some concise code samples are presented to illustrate how to use new features of TensorFlow 2.]]>
A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired). Some concise code samples are presented to illustrate how to use new features of TensorFlow 2.]]> Fri, 28 Jun 2019 05:34:55 GMT /slideshow/introduction-to-tensorflow-2-152333392/152333392 ocampesato@slideshare.net(ocampesato) Introduction to TensorFlow 2 ocampesato A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired). Some concise code samples are presented to illustrate how to use new features of TensorFlow 2. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/salesforcetf2-190628053455-thumbnail.jpg?width=120&height=120&fit=bounds" /> A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired). Some concise code samples are presented to illustrate how to use new features of TensorFlow 2.

Introduction to TensorFlow 2 from Oswald Campesato

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0 Introduction to TensorFlow 2 /slideshow/introduction-to-tensorflow-2/147026671 h2omtvtf2-190522040126
A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired). Concise code samples are presented to illustrate how to use new features of TensorFlow 2. You'll also get a quick introduction to lazy operators (if you know FRP this will be super easy), along with a code comparison between TF 1.x/iterators with tf.data.Dataset and TF 2/generators with tf.data.Dataset. Finally, we'll look at some tf.keras code samples that are based on TensorFlow 2. Although familiarity with TF 1.x is helpful, newcomers with an avid interest in learning about TensorFlow 2 can benefit from this session.]]>
A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired). Concise code samples are presented to illustrate how to use new features of TensorFlow 2. You'll also get a quick introduction to lazy operators (if you know FRP this will be super easy), along with a code comparison between TF 1.x/iterators with tf.data.Dataset and TF 2/generators with tf.data.Dataset. Finally, we'll look at some tf.keras code samples that are based on TensorFlow 2. Although familiarity with TF 1.x is helpful, newcomers with an avid interest in learning about TensorFlow 2 can benefit from this session.]]> Wed, 22 May 2019 04:01:26 GMT /slideshow/introduction-to-tensorflow-2/147026671 ocampesato@slideshare.net(ocampesato) Introduction to TensorFlow 2 ocampesato A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired). Concise code samples are presented to illustrate how to use new features of TensorFlow 2. You'll also get a quick introduction to lazy operators (if you know FRP this will be super easy), along with a code comparison between TF 1.x/iterators with tf.data.Dataset and TF 2/generators with tf.data.Dataset. Finally, we'll look at some tf.keras code samples that are based on TensorFlow 2. Although familiarity with TF 1.x is helpful, newcomers with an avid interest in learning about TensorFlow 2 can benefit from this session. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/h2omtvtf2-190522040126-thumbnail.jpg?width=120&height=120&fit=bounds" /> A fast-paced introduction to TensorFlow 2 about some important new features (such as generators and the @tf.function decorator) and TF 1.x functionality that's been removed from TF 2 (yes, tf.Session() has retired). Concise code samples are presented to illustrate how to use new features of TensorFlow 2. You'll also get a quick introduction to lazy operators (if you know FRP this will be super easy), along with a code comparison between TF 1.x/iterators with tf.data.Dataset and TF 2/generators with tf.data.Dataset. Finally, we'll look at some tf.keras code samples that are based on TensorFlow 2. Although familiarity with TF 1.x is helpful, newcomers with an avid interest in learning about TensorFlow 2 can benefit from this session.

Introduction to TensorFlow 2 from Oswald Campesato

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0 "An Introduction to AI and Deep Learning" /ocampesato/an-introduction-to-ai-and-deep-learning humanists-feb-hd-190204225854
This fast-paced session provides a brief history of AI, followed by AI-related topics, such as Machine Learning, Deep Learning and Reinforcement Learning, and the most popular frameworks for Machine Learning. You will learn about some of the successes of AI, and also some of the significant challenges in AI. No specialized knowledge is required, but an avid interest is recommended to derive the maximum benefit from this session. ]]>
This fast-paced session provides a brief history of AI, followed by AI-related topics, such as Machine Learning, Deep Learning and Reinforcement Learning, and the most popular frameworks for Machine Learning. You will learn about some of the successes of AI, and also some of the significant challenges in AI. No specialized knowledge is required, but an avid interest is recommended to derive the maximum benefit from this session. ]]> Mon, 04 Feb 2019 22:58:54 GMT /ocampesato/an-introduction-to-ai-and-deep-learning ocampesato@slideshare.net(ocampesato) "An Introduction to AI and Deep Learning" ocampesato This fast-paced session provides a brief history of AI, followed by AI-related topics, such as Machine Learning, Deep Learning and Reinforcement Learning, and the most popular frameworks for Machine Learning. You will learn about some of the successes of AI, and also some of the significant challenges in AI. No specialized knowledge is required, but an avid interest is recommended to derive the maximum benefit from this session. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/humanists-feb-hd-190204225854-thumbnail.jpg?width=120&height=120&fit=bounds" /> This fast-paced session provides a brief history of AI, followed by AI-related topics, such as Machine Learning, Deep Learning and Reinforcement Learning, and the most popular frameworks for Machine Learning. You will learn about some of the successes of AI, and also some of the significant challenges in AI. No specialized knowledge is required, but an avid interest is recommended to derive the maximum benefit from this session.

"An Introduction to AI and Deep Learning" from Oswald Campesato

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0 H2 o berkeleydltf /slideshow/h2-o-berkeleydltf/128906177 h2oberkeleydltf-190123060429
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs, followed by a Keras code sample for defining a CNN. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see a short introduction to TensorFlow 1.x and some insights into TF 2 that will be released some time this year.]]>
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs, followed by a Keras code sample for defining a CNN. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see a short introduction to TensorFlow 1.x and some insights into TF 2 that will be released some time this year.]]> Wed, 23 Jan 2019 06:04:29 GMT /slideshow/h2-o-berkeleydltf/128906177 ocampesato@slideshare.net(ocampesato) H2 o berkeleydltf ocampesato A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs, followed by a Keras code sample for defining a CNN. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see a short introduction to TensorFlow 1.x and some insights into TF 2 that will be released some time this year. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/h2oberkeleydltf-190123060429-thumbnail.jpg?width=120&height=120&fit=bounds" /> A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs, followed by a Keras code sample for defining a CNN. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see a short introduction to TensorFlow 1.x and some insights into TF 2 that will be released some time this year.

H2 o berkeleydltf from Oswald Campesato

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0 Introduction to Deep Learning, Keras, and Tensorflow /slideshow/introduction-to-deep-learning-keras-and-tensorflow/127675850 metisdltf-190110060914
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see how to create a Convolutional Neural Network in Keras, followed by a quick introduction to TensorFlow and TensorBoard.]]>
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see how to create a Convolutional Neural Network in Keras, followed by a quick introduction to TensorFlow and TensorBoard.]]> Thu, 10 Jan 2019 06:09:14 GMT /slideshow/introduction-to-deep-learning-keras-and-tensorflow/127675850 ocampesato@slideshare.net(ocampesato) Introduction to Deep Learning, Keras, and Tensorflow ocampesato A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see how to create a Convolutional Neural Network in Keras, followed by a quick introduction to TensorFlow and TensorBoard. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/metisdltf-190110060914-thumbnail.jpg?width=120&height=120&fit=bounds" /> A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see how to create a Convolutional Neural Network in Keras, followed by a quick introduction to TensorFlow and TensorBoard.

Introduction to Deep Learning, Keras, and Tensorflow from Oswald Campesato

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0 Introduction to Deep Learning for Non-Programmers /slideshow/introduction-to-deep-learning-for-nonprogrammers/123330694 mensa-nov-sf-181118060547
This session provides a brief history of AI, followed by AI-related topics, such as robots in AI, Machine Learning and Deep Learning, use cases for AI, some of the successes of AI, and also some of the significant challenges in AI. You will also learn about AI and mobile devices and the ethics of AI. An avid interest is recommended to derive the maximum benefit from this session.]]>
This session provides a brief history of AI, followed by AI-related topics, such as robots in AI, Machine Learning and Deep Learning, use cases for AI, some of the successes of AI, and also some of the significant challenges in AI. You will also learn about AI and mobile devices and the ethics of AI. An avid interest is recommended to derive the maximum benefit from this session.]]> Sun, 18 Nov 2018 06:05:47 GMT /slideshow/introduction-to-deep-learning-for-nonprogrammers/123330694 ocampesato@slideshare.net(ocampesato) Introduction to Deep Learning for Non-Programmers ocampesato This session provides a brief history of AI, followed by AI-related topics, such as robots in AI, Machine Learning and Deep Learning, use cases for AI, some of the successes of AI, and also some of the significant challenges in AI. You will also learn about AI and mobile devices and the ethics of AI. An avid interest is recommended to derive the maximum benefit from this session. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/mensa-nov-sf-181118060547-thumbnail.jpg?width=120&height=120&fit=bounds" /> This session provides a brief history of AI, followed by AI-related topics, such as robots in AI, Machine Learning and Deep Learning, use cases for AI, some of the successes of AI, and also some of the significant challenges in AI. You will also learn about AI and mobile devices and the ethics of AI. An avid interest is recommended to derive the maximum benefit from this session.

Introduction to Deep Learning for Non-Programmers from Oswald Campesato

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0 TensorFlow in Your Browser /slideshow/tensorflow-in-your-browser/119409625 svcc2018-dltfjs-181014222004
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, followed by some TensorFlow features, and then a code sample of training a CNN in tensorflow.js. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.]]>
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, followed by some TensorFlow features, and then a code sample of training a CNN in tensorflow.js. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.]]> Sun, 14 Oct 2018 22:20:04 GMT /slideshow/tensorflow-in-your-browser/119409625 ocampesato@slideshare.net(ocampesato) TensorFlow in Your Browser ocampesato A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, followed by some TensorFlow features, and then a code sample of training a CNN in tensorflow.js. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/svcc2018-dltfjs-181014222004-thumbnail.jpg?width=120&height=120&fit=bounds" /> A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, followed by some TensorFlow features, and then a code sample of training a CNN in tensorflow.js. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.

TensorFlow in Your Browser from Oswald Campesato

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0 Deep Learning in Your Browser /slideshow/deep-learning-in-your-browser/119085181 gdg-brkdltfjs-181011075024
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, followed by some TensorFlow features, and then a code sample of training a CNN in tensorflow.js. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.]]>
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, followed by some TensorFlow features, and then a code sample of training a CNN in tensorflow.js. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.]]> Thu, 11 Oct 2018 07:50:24 GMT /slideshow/deep-learning-in-your-browser/119085181 ocampesato@slideshare.net(ocampesato) Deep Learning in Your Browser ocampesato A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, followed by some TensorFlow features, and then a code sample of training a CNN in tensorflow.js. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gdg-brkdltfjs-181011075024-thumbnail.jpg?width=120&height=120&fit=bounds" /> A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, followed by some TensorFlow features, and then a code sample of training a CNN in tensorflow.js. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session.

Deep Learning in Your Browser from Oswald Campesato

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0 Deep Learning and TensorFlow /slideshow/deep-learning-and-tensorflow-116607884/116607884 upd-h2o-dl-180926044151
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see a short introduction to TensorFlow and TensorBoard. ]]>
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see a short introduction to TensorFlow and TensorBoard. ]]> Wed, 26 Sep 2018 04:41:51 GMT /slideshow/deep-learning-and-tensorflow-116607884/116607884 ocampesato@slideshare.net(ocampesato) Deep Learning and TensorFlow ocampesato A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see a short introduction to TensorFlow and TensorBoard. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/upd-h2o-dl-180926044151-thumbnail.jpg?width=120&height=120&fit=bounds" /> A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, back propagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and derivatives is helpful in order to derive the maximum benefit from this session. Then we'll see a short introduction to TensorFlow and TensorBoard.

Deep Learning and TensorFlow from Oswald Campesato

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0 Introduction to Deep Learning and TensorFlow /slideshow/introduction-to-deep-learning-and-tensorflow-114208321/114208321 leanplumdltf-180913062811
A fast-paced introduction to Deep Learning (DL) concepts, starting with a simple yet complete neural network (no frameworks), followed by aspects of deep neural networks, such as back propagation, activation functions, CNNs, and the AUT theorem. Next, a quick introduction to TensorFlow and TensorBoard, along with some code samples with TensorFlow. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, the notion of a derivative, and rudimentary Python is recommended.]]>
A fast-paced introduction to Deep Learning (DL) concepts, starting with a simple yet complete neural network (no frameworks), followed by aspects of deep neural networks, such as back propagation, activation functions, CNNs, and the AUT theorem. Next, a quick introduction to TensorFlow and TensorBoard, along with some code samples with TensorFlow. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, the notion of a derivative, and rudimentary Python is recommended.]]> Thu, 13 Sep 2018 06:28:11 GMT /slideshow/introduction-to-deep-learning-and-tensorflow-114208321/114208321 ocampesato@slideshare.net(ocampesato) Introduction to Deep Learning and TensorFlow ocampesato A fast-paced introduction to Deep Learning (DL) concepts, starting with a simple yet complete neural network (no frameworks), followed by aspects of deep neural networks, such as back propagation, activation functions, CNNs, and the AUT theorem. Next, a quick introduction to TensorFlow and TensorBoard, along with some code samples with TensorFlow. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, the notion of a derivative, and rudimentary Python is recommended. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/leanplumdltf-180913062811-thumbnail.jpg?width=120&height=120&fit=bounds" /> A fast-paced introduction to Deep Learning (DL) concepts, starting with a simple yet complete neural network (no frameworks), followed by aspects of deep neural networks, such as back propagation, activation functions, CNNs, and the AUT theorem. Next, a quick introduction to TensorFlow and TensorBoard, along with some code samples with TensorFlow. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, the notion of a derivative, and rudimentary Python is recommended.

Introduction to Deep Learning and TensorFlow from Oswald Campesato

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0 Intro to Deep Learning, TensorFlow, and tensorflow.js /slideshow/intro-to-deep-learning-tensorflow-and-tensorflowjs/113992914 jsmtvdltf-180912044619
This fast-paced session introduces Deep Learning concepts, such gradient descent, back propagation, activation functions, and CNNs. We'll look at creating Android apps with TensorFlow Lite (pending its availability). Basic knowledge of vectors, matrices, and Android, as well as elementary calculus (derivatives), are strongly recommended in order to derive the maximum benefit from this session.]]>
This fast-paced session introduces Deep Learning concepts, such gradient descent, back propagation, activation functions, and CNNs. We'll look at creating Android apps with TensorFlow Lite (pending its availability). Basic knowledge of vectors, matrices, and Android, as well as elementary calculus (derivatives), are strongly recommended in order to derive the maximum benefit from this session.]]> Wed, 12 Sep 2018 04:46:18 GMT /slideshow/intro-to-deep-learning-tensorflow-and-tensorflowjs/113992914 ocampesato@slideshare.net(ocampesato) Intro to Deep Learning, TensorFlow, and tensorflow.js ocampesato This fast-paced session introduces Deep Learning concepts, such gradient descent, back propagation, activation functions, and CNNs. We'll look at creating Android apps with TensorFlow Lite (pending its availability). Basic knowledge of vectors, matrices, and Android, as well as elementary calculus (derivatives), are strongly recommended in order to derive the maximum benefit from this session. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/jsmtvdltf-180912044619-thumbnail.jpg?width=120&height=120&fit=bounds" /> This fast-paced session introduces Deep Learning concepts, such gradient descent, back propagation, activation functions, and CNNs. We'll look at creating Android apps with TensorFlow Lite (pending its availability). Basic knowledge of vectors, matrices, and Android, as well as elementary calculus (derivatives), are strongly recommended in order to derive the maximum benefit from this session.

Intro to Deep Learning, TensorFlow, and tensorflow.js from Oswald Campesato

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0 Deep Learning and TensorFlow /slideshow/deep-learning-and-tensorflow/111266161 baypython-dltf-180824050421
This slide deck introduces Deep Learning concepts, such gradient descent, back propagation, activation functions, and CNNs. Basic knowledge of vectors, matrices, and Android, as well as elementary calculus (derivatives), are strongly recommended in order to derive the maximum benefit from this session. ]]>
This slide deck introduces Deep Learning concepts, such gradient descent, back propagation, activation functions, and CNNs. Basic knowledge of vectors, matrices, and Android, as well as elementary calculus (derivatives), are strongly recommended in order to derive the maximum benefit from this session. ]]> Fri, 24 Aug 2018 05:04:20 GMT /slideshow/deep-learning-and-tensorflow/111266161 ocampesato@slideshare.net(ocampesato) Deep Learning and TensorFlow ocampesato This slide deck introduces Deep Learning concepts, such gradient descent, back propagation, activation functions, and CNNs. Basic knowledge of vectors, matrices, and Android, as well as elementary calculus (derivatives), are strongly recommended in order to derive the maximum benefit from this session. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/baypython-dltf-180824050421-thumbnail.jpg?width=120&height=120&fit=bounds" /> This slide deck introduces Deep Learning concepts, such gradient descent, back propagation, activation functions, and CNNs. Basic knowledge of vectors, matrices, and Android, as well as elementary calculus (derivatives), are strongly recommended in order to derive the maximum benefit from this session.

Deep Learning and TensorFlow from Oswald Campesato

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0 Introduction to Deep Learning and Tensorflow /slideshow/introduction-to-deep-learning-and-tensorflow/107693292 gdg-berkeley-180727073834
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and elementary calculus (derivatives), are helpful in order to derive the maximum benefit from this session. Next we'll see a simple neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)]]>
A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and elementary calculus (derivatives), are helpful in order to derive the maximum benefit from this session. Next we'll see a simple neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)]]> Fri, 27 Jul 2018 07:38:34 GMT /slideshow/introduction-to-deep-learning-and-tensorflow/107693292 ocampesato@slideshare.net(ocampesato) Introduction to Deep Learning and Tensorflow ocampesato A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and elementary calculus (derivatives), are helpful in order to derive the maximum benefit from this session. Next we'll see a simple neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/gdg-berkeley-180727073834-thumbnail.jpg?width=120&height=120&fit=bounds" /> A fast-paced introduction to Deep Learning concepts, such as activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Basic knowledge of vectors, matrices, and elementary calculus (derivatives), are helpful in order to derive the maximum benefit from this session. Next we'll see a simple neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. (Bonus points if you know Zorn's Lemma, the Well-Ordering Theorem, and the Axiom of Choice.)

Introduction to Deep Learning and Tensorflow from Oswald Campesato

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0 Deep Learning, Scala, and Spark /slideshow/deep-learning-scala-and-spark/93519960 scala-dl-sf-180411063533
This fast-paced session starts with an introduction to neural networks and linear regression models, along with a quick view of TensorFlow, followed by some Scala APIs for TensorFlow. You'll also see a simple dockerized image of Scala and TensorFlow code and how to execute the code in that image from the command line. No prior knowledge of NNs, Keras, or TensorFlow is required (but you must be comfortable with Scala).]]>
This fast-paced session starts with an introduction to neural networks and linear regression models, along with a quick view of TensorFlow, followed by some Scala APIs for TensorFlow. You'll also see a simple dockerized image of Scala and TensorFlow code and how to execute the code in that image from the command line. No prior knowledge of NNs, Keras, or TensorFlow is required (but you must be comfortable with Scala).]]> Wed, 11 Apr 2018 06:35:33 GMT /slideshow/deep-learning-scala-and-spark/93519960 ocampesato@slideshare.net(ocampesato) Deep Learning, Scala, and Spark ocampesato This fast-paced session starts with an introduction to neural networks and linear regression models, along with a quick view of TensorFlow, followed by some Scala APIs for TensorFlow. You'll also see a simple dockerized image of Scala and TensorFlow code and how to execute the code in that image from the command line. No prior knowledge of NNs, Keras, or TensorFlow is required (but you must be comfortable with Scala). <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/scala-dl-sf-180411063533-thumbnail.jpg?width=120&height=120&fit=bounds" /> This fast-paced session starts with an introduction to neural networks and linear regression models, along with a quick view of TensorFlow, followed by some Scala APIs for TensorFlow. You'll also see a simple dockerized image of Scala and TensorFlow code and how to execute the code in that image from the command line. No prior knowledge of NNs, Keras, or TensorFlow is required (but you must be comfortable with Scala).

Deep Learning, Scala, and Spark from Oswald Campesato

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0 Deep Learning in your Browser: powered by WebGL /slideshow/deep-learning-in-your-browser-powered-by-webgl/92142961 webgldl-180328062120
"A fast-paced introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, and CNNs. We'll also look at JavaScript-based toolkits (such as TensorFire and deeplearning.js) that leverage the power of WebGL. Basic knowledge of elementary calculus (e.g., derivatives) is recommended in order to derive the maximum benefit from this session.]]>
"A fast-paced introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, and CNNs. We'll also look at JavaScript-based toolkits (such as TensorFire and deeplearning.js) that leverage the power of WebGL. Basic knowledge of elementary calculus (e.g., derivatives) is recommended in order to derive the maximum benefit from this session.]]> Wed, 28 Mar 2018 06:21:20 GMT /slideshow/deep-learning-in-your-browser-powered-by-webgl/92142961 ocampesato@slideshare.net(ocampesato) Deep Learning in your Browser: powered by WebGL ocampesato "A fast-paced introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, and CNNs. We'll also look at JavaScript-based toolkits (such as TensorFire and deeplearning.js) that leverage the power of WebGL. Basic knowledge of elementary calculus (e.g., derivatives) is recommended in order to derive the maximum benefit from this session. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/webgldl-180328062120-thumbnail.jpg?width=120&height=120&fit=bounds" /> "A fast-paced introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, and CNNs. We'll also look at JavaScript-based toolkits (such as TensorFire and deeplearning.js) that leverage the power of WebGL. Basic knowledge of elementary calculus (e.g., derivatives) is recommended in order to derive the maximum benefit from this session.

Deep Learning in your Browser: powered by WebGL from Oswald Campesato

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0 Deep Learning, Keras, and TensorFlow /slideshow/deep-learning-keras-and-tensorflow/90598110 h2o-dl-180314050645
A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful. ]]>
A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful. ]]> Wed, 14 Mar 2018 05:06:45 GMT /slideshow/deep-learning-keras-and-tensorflow/90598110 ocampesato@slideshare.net(ocampesato) Deep Learning, Keras, and TensorFlow ocampesato A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/h2o-dl-180314050645-thumbnail.jpg?width=120&height=120&fit=bounds" /> A fast-paced introduction to Deep Learning that starts with a simple yet complete neural network (no frameworks), followed by an overview of activation functions, cost functions, backpropagation, and then a quick dive into CNNs. Next we'll create a neural network using Keras, followed by an introduction to TensorFlow and TensorBoard. For best results, familiarity with basic vectors and matrices, inner (aka "dot") products of vectors, and rudimentary Python is definitely helpful.

Deep Learning, Keras, and TensorFlow from Oswald Campesato

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0 C++ and Deep Learning /slideshow/c-and-deep-learning/87493446 cppdl-180208084600
A fast-paced introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, CNNs, RNNs (if time permits), and the CLT/AUT/fixed-point theorems, along with a basic code sample in TensorFlow. During this session you will learn how to manually create a basic neural network that acts as a classifier, and also the segue from linear regression to a neural network. You'll also learn about GANs (Generative Adversarial Networks) for static images as well as voice, and the former case, their potential impact on self-driving cars. ]]>
A fast-paced introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, CNNs, RNNs (if time permits), and the CLT/AUT/fixed-point theorems, along with a basic code sample in TensorFlow. During this session you will learn how to manually create a basic neural network that acts as a classifier, and also the segue from linear regression to a neural network. You'll also learn about GANs (Generative Adversarial Networks) for static images as well as voice, and the former case, their potential impact on self-driving cars. ]]> Thu, 08 Feb 2018 08:46:00 GMT /slideshow/c-and-deep-learning/87493446 ocampesato@slideshare.net(ocampesato) C++ and Deep Learning ocampesato A fast-paced introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, CNNs, RNNs (if time permits), and the CLT/AUT/fixed-point theorems, along with a basic code sample in TensorFlow. During this session you will learn how to manually create a basic neural network that acts as a classifier, and also the segue from linear regression to a neural network. You'll also learn about GANs (Generative Adversarial Networks) for static images as well as voice, and the former case, their potential impact on self-driving cars. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cppdl-180208084600-thumbnail.jpg?width=120&height=120&fit=bounds" /> A fast-paced introduction to Deep Learning (DL) concepts, such as neural networks, back propagation, activation functions, CNNs, RNNs (if time permits), and the CLT/AUT/fixed-point theorems, along with a basic code sample in TensorFlow. During this session you will learn how to manually create a basic neural network that acts as a classifier, and also the segue from linear regression to a neural network. You'll also learn about GANs (Generative Adversarial Networks) for static images as well as voice, and the former case, their potential impact on self-driving cars.

C++ and Deep Learning from Oswald Campesato

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0 https://cdn.slidesharecdn.com/profile-photo-ocampesato-48x48.jpg?cb=1607232125 https://cdn.slidesharecdn.com/ss_thumbnails/h2osftf2data-200219055337-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/working-with-tfdata-tf-2/228607132 Working with tf.data (... https://cdn.slidesharecdn.com/ss_thumbnails/gdgsf-tf2-191120070051-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/introduction-to-tensorflow-2-and-keras/195445363 Introduction to Tensor... https://cdn.slidesharecdn.com/ss_thumbnails/ucsc-dl-intro-190813045533-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/introduction-to-deep-learning-163362743/163362743 Introduction to Deep L...