TensorFlow In 10 Minutes | Deep Learning & TensorFlow | Edureka
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*** AI and Deep-Learning with TensorFlow: https://www.edureka.co/ai-deep-learning-with-tensorflow *** This Edureka PPT provides you with a basic introduction to TensorFlow: The amazing deep learning framework by Google. Below are the topics covered in this PPT: 1. What is TensorFlow? 2. Companies using TensorFlow 3. Features of TensorFlow 4. What are Tensors? 5. What are Neural Networks? 6. TensorFlow Open Source Community Follow us to never miss an update in the future. YouTube: https://www.youtube.com/user/edurekaIN Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Castbox: https://castbox.fm/networks/505?country=in
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Hands-on Learning with KubeFlow + Keras/TensorFlow 2.0 + TF Extended (TFX) + ...
Title
Hands-on Learning with KubeFlow + Keras/TensorFlow 2.0 + TF Extended (TFX) + Kubernetes + PyTorch + XGBoost + Airflow + MLflow + Spark + Jupyter + TPU
Video
https://youtu.be/vaB4IM6ySD0
Description
In this workshop, we build real-world machine learning pipelines using TensorFlow Extended (TFX), KubeFlow, and Airflow.
Described in the 2017 paper, TFX is used internally by thousands of Google data scientists and engineers across every major product line within Google.
KubeFlow is a modern, end-to-end pipeline orchestration framework that embraces the latest AI best practices including hyper-parameter tuning, distributed model training, and model tracking.
Airflow is the most-widely used pipeline orchestration framework in machine learning.
Pre-requisites
Modern browser - and that's it!
Every attendee will receive a cloud instance
Nothing will be installed on your local laptop
Everything can be downloaded at the end of the workshop
Location
Online Workshop
Agenda
1. Create a Kubernetes cluster
2. Install KubeFlow, Airflow, TFX, and Jupyter
3. Setup ML Training Pipelines with KubeFlow and Airflow
4. Transform Data with TFX Transform
5. Validate Training Data with TFX Data Validation
6. Train Models with Jupyter, Keras/TensorFlow 2.0, PyTorch, XGBoost, and KubeFlow
7. Run a Notebook Directly on Kubernetes Cluster with KubeFlow
8. Analyze Models using TFX Model Analysis and Jupyter
9. Perform Hyper-Parameter Tuning with KubeFlow
10. Select the Best Model using KubeFlow Experiment Tracking
11. Reproduce Model Training with TFX Metadata Store and Pachyderm
12. Deploy the Model to Production with TensorFlow Serving and Istio
13. Save and Download your Workspace
Key Takeaways
Attendees will gain experience training, analyzing, and serving real-world Keras/TensorFlow 2.0 models in production using model frameworks and open-source tools.
Related Links
1. PipelineAI Home: https://pipeline.ai
2. PipelineAI Community Edition: http://community.pipeline.ai
3. PipelineAI GitHub: https://github.com/PipelineAI/pipeline
4. Advanced Spark and TensorFlow Meetup (SF-based, Global Reach): https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup
5. YouTube Videos: https://youtube.pipeline.ai
6. SlideShare Presentations: https://slideshare.pipeline.ai
7. Slack Support: https://joinslack.pipeline.ai
8. Web Support and Knowledge Base: https://support.pipeline.ai
9. Email Support: support@pipeline.ai
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slide-keras-tf.pptx
Keras and TensorFlow are popular Python packages for deep learning. Keras provides high-level neural network APIs that allow users to build and train models quickly with fewer lines of code, running on CPU or GPU. TensorFlow is a more flexible platform that supports lower-level operations and multi-platform deployment, but requires more code. Both packages were used in examples to build and train a basic neural network model to solve the XOR problem, demonstrating their capabilities for machine learning tasks.
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running Tensorflow in Production
Slides used at the Tensorflow Belgium meetup titled running Tensorflow in Production https://www.meetup.com/TensorFlow-Belgium/events/252679670/
Tensorflow in practice by Engineer - donghwi cha
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We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
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Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
- Detailed instructions on setting up Prometheus to monitor the performance and health of your anomaly detection system.
9. What is Camel K?
- Introduction to Camel K, a lightweight integration framework built on Apache Camel, designed for Kubernetes.
10. Configuring Camel K Integrations for Data Pipelines
- Learn how to configure Camel K for seamless data pipeline integrations in your anomaly detection workflow.
11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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Chris Jones, Director of Product Management at BrainChip , presents the “Temporal Event Neural Networks: A More Efficient Alternative to the Transformer” tutorial at the May 2024 Embedded Vision Summit.
The expansion of AI services necessitates enhanced computational capabilities on edge devices. Temporal Event Neural Networks (TENNs), developed by BrainChip, represent a novel and highly efficient state-space network. TENNs demonstrate exceptional proficiency in handling multi-dimensional streaming data, facilitating advancements in object detection, action recognition, speech enhancement and language model/sequence generation. Through the utilization of polynomial-based continuous convolutions, TENNs streamline models, expedite training processes and significantly diminish memory requirements, achieving notable reductions of up to 50x in parameters and 5,000x in energy consumption compared to prevailing methodologies like transformers.
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Manage and optimize your license adoption and consumption with SAM4U, an SAP free customer software asset management tool.
SAM4U, an SAP complimentary software asset management tool for customers, delivers a detailed and well-structured overview of license inventory and usage with a user-friendly interface. We offer a hosted, cost-effective, and performance-optimized SAM4U setup in the Skybuffer Cloud environment. You retain ownership of the system and data, while we manage the ABAP 7.58 infrastructure, ensuring fixed Total Cost of Ownership (TCO) and exceptional services through the SAP Fiori interface.
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Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
Lambda, Elastic Beanstalk, Lightsail, Amplify, S3 (and more!) can each host websites + APIs. But which one should we choose?
Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
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An English 🇬🇧 translation of a presentation to the speech I gave about the main changes brought by CCS TSI 2023 at the biggest Czech conference on Communications and signalling systems on Railways, which was held in Clarion Hotel Olomouc from 7th to 9th November 2023 (konferenceszt.cz). Attended by around 500 participants and 200 on-line followers.
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TensorFlow In 10 Minutes | Deep Learning & TensorFlow | Edureka
- 3. www.edureka.co TensorFlow is an open source library for Numerical Computation and Large-Scale Machine Learning
- 7. www.edureka.co Adopted Keras For High-Level APIs Eager Execution With Graphs
- 8. www.edureka.co Tensors are the standard way of representing data in deep learning
- 10. www.edureka.co
- 11. www.edureka.co
- 12. www.edureka.co Most of the work! Few lines of code! Just one line! Just one line! Just one line!Collect a dataset Train the Network Predict Build your model Evaluate
- 13. www.edureka.co
- 14. www.edureka.co
- 15. www.edureka.co import tensorflow as tf a = tf.variable(0.) b = tf.variable(1.) op_add = a.assign(a + b) op_mul = b.assign(b * 2) init = tf.global_variables_initializer with tf.Session() as sess: init.run() for iteration in range(50): sess.run(add_op) sess.run(mul_op) print(a.eval())
- 16. www.edureka.co import tensorflow as tf a = tf.variable(0.) b = tf.variable(1.) op_add = a.assign(a + b) op_mul = b.assign(b * 2) init = tf.global_variables_initializer with tf.Session() as sess: init.run() for iteration in range(50): sess.run(add_op) sess.run(mul_op) print(a.eval()) CONSTRUCTION PHASE EXECUTION PHASE
- 17. www.edureka.co import tensorflow as tf a = tf.variable(0.) b = tf.variable(1.) op_add = a.assign(a + b) op_mul = b.assign(b * 2) init = tf.global_variables_initializer with tf.Session() as sess: init.run() for iteration in range(50): sess.run(add_op) sess.run(mul_op) print(a.eval()) import tensorflow as tf tf.enable_eager_execution() a = tf.contrib.eager.Variable(0.) b = tf.contrib.eager.Variable(1.) for iteration in range(50): a.assign(a + b) b.assign(b * 2) print(a.numpy())
- 18. Copyright © 2017, edureka and/or its affiliates. All rights reserved. www.edureka.co
- 19. www.edureka.co