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This course will teach you how to use Keras, a neural network API written in Python and integrated with TensorFlow. We will learn how to prepare and process data for artificial neural networks, build and train artificial neural networks from scratch, build and train convolutional neural networks (CNNs), implement fine-tuning and transfer learning, and more! ⭐️🦎 COURSE CONTENTS 🦎⭐️ ⌨️ (00:00:00) Welcome to this course ⌨️ (00:00:16) Keras Course Introduction ⌨️ (00:00:50) Course Prerequisites ⌨️ (00:01:33) DEEPLIZARD Deep Learning Path ⌨️ (00:01:45) Course Resources ⌨️ (00:02:30) About Keras ⌨️ (00:06:41) Keras with TensorFlow - Data Processing for Neural Network Training ⌨️ (00:18:39) Create an Artificial Neural Network with TensorFlow's Keras API ⌨️ (00:24:36) Train an Artificial Neural Network with TensorFlow's Keras API ⌨️ (00:30:07) Build a Validation Set With TensorFlow's Keras API ⌨️ (00:39:28) Neural Network Predictions with TensorFlow's Keras API ⌨️ (00:47:48) Create a Confusion Matrix for Neural Network Predictions ⌨️ (00:52:29) Save and Load a Model with TensorFlow's Keras API ⌨️ (01:01:25) Image Preparation for CNNs with TensorFlow's Keras API ⌨️ (01:19:22) Build and Train a CNN with TensorFlow's Keras API ⌨️ (01:28:42) CNN Predictions with TensorFlow's Keras API ⌨️ (01:37:05) Build a Fine-Tuned Neural Network with TensorFlow's Keras API ⌨️ (01:48:19) Train a Fine-Tuned Neural Network with TensorFlow's Keras API ⌨️ (01:52:39) Predict with a Fine-Tuned Neural Network with TensorFlow's Keras API ⌨️ (01:57:50) MobileNet Image Classification with TensorFlow's Keras API ⌨️ (02:11:18) Process Images for Fine-Tuned MobileNet with TensorFlow's Keras API ⌨️ (02:24:24) Fine-Tuning MobileNet on Custom Data Set with TensorFlow's Keras API ⌨️ (02:38:59) Data Augmentation with TensorFlow' Keras API ⌨️ (02:47:24) Collective Intelligence and the DEEPLIZARD HIVEMIND ⭐️🦎 DEEPLIZARD COMMUNITY RESOURCES 🦎⭐️ 👉 Check out the blog post and other resources for this course: 🔗 🤍 💻 DOWNLOAD ACCESS TO CODE FILES 🤖 Available for members of the deeplizard hivemind: 🔗 🤍 🧠 Support collective intelligence, join the deeplizard hivemind: 🔗 🤍 👋 Hey, we're Chris and Mandy, the creators of deeplizard! 👀 CHECK OUT OUR VLOG: 🔗 🤍 👀 Follow deeplizard: YouTube: 🤍 Our vlog: 🤍 Facebook: 🤍 Instagram: 🤍 Twitter: 🤍 Patreon: 🤍 🎵 deeplizard uses music by Kevin MacLeod 🔗 🤍 🔗 🤍 ❤️ Please use the knowledge gained from deeplizard content for good, not evil. Learn to code for free and get a developer job: 🤍 Read hundreds of articles on programming: 🤍
🔥AI/ML Course for 3-8 Yrs Work Exp: 🤍 🔥AI/ML Course for 0-3 Yrs Work Exp: 🤍 🔥AI/ML Course for 8+ Yrs Work Exp: 🤍 This video on What is Keras will help you understand the basics of a top deep learning library used in data science. You will learn the features of Keras, followed by how Keras performs operations. Finally, we'll look at the applications of Keras. 00:00:00 What is Deep Learning 00:03:34 What is Keras 00:06:33 Why Keras 00:07:41 Building a Model in Keras 00:08:38 Uses of Keras ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 ⏩ Check out the Machine Learning tutorial videos: 🤍 #WhatIsKeras #WhatIsKerasInDeepLearning #KerasTutorial #KerasTutorialForBeginners #WhatIsKerasInMachineLearning #ArtificialIntelligenceCourse #ArtificialIntelligenceTutorial #ArtificialIntelligenceTutorialForBeginners #Simplilearn To learn more about this topic, visit: 🤍 🔥Enroll for Free Deep Learning Course & Get Your Completion Certificate: 🤍 ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… 👉 Learn More At: 🔥 Purdue Post Graduate Program In AI And Machine Learning: 🤍 🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): 🤍 🔥AI Engineer Masters Program (Discount Code - YTBE15): 🤍 🔥AI & Machine Learning Bootcamp(US Only): 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
Getting started with Keras has never been easier! Not only is it built into TensorFlow, but when you combine it with Kaggle Kernels you don’t have to install anything! Plus you get to take advantage of the resources from the Kaggle community. In this episode of AI Adventures, Yufeng shows you how to get started with Keras. Take a look! Associated blog post → 🤍 Get started with Keras → 🤍 Previous video with Fashion-MNIST → 🤍 Watch more AI Adventures → 🤍 Subscribe to the Google Cloud Platform channel → 🤍 #AIAdventures
An updated deep learning introduction using Python, TensorFlow, and Keras. Text-tutorial and notes: 🤍 TensorFlow Docs: 🤍 Keras Docs: 🤍 Discord: 🤍
🔥AI/ML Course for 3-8 Yrs Work Exp: 🤍 🔥AI/ML Course for 0-3 Yrs Work Exp: 🤍 🔥AI/ML Course for 8+ Yrs Work Exp: 🤍 00:00 TenserFlow Tutorial 44:48 Tenserflow 1.0 vs 2.0 01:27:13 Keras 02:00:59 Implementing Neural Networks 02:38:27 Sequential Model 🔥Enroll for Free Deep Learning Course & Get Your Completion Certificate: 🤍 This Keras full course will helo you understand what is Keras, the working principle of Keras, Keras models, what are neural networks along with the hands-on demo. We will have look at a project where we detect whether a person is wearing a mask or not. To access the slides, click here: 🤍 ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 ⏩ Check out the Machine Learning tutorial videos: 🤍 #Keras #KerasFullCourse #KerasTutorial #KerasTutorialForBeginners #KerasFullTutorial #LearnKeras #DeepLearningWithKeras #Simplilearn Why Do We Need Keras? ✅Keras is an API that was made to be easy to learn for people. Keras was made to be simple. It offers consistent & simple APIs, reduces the actions required to implement common code, and explains user error clearly. ✅Prototyping time in Keras is less. This means that your ideas can be implemented and deployed in a shorter time. Keras also provides a variety of deployment options depending on user needs. ✅Languages with a high level of abstraction and inbuilt features are slow and building custom features in then can be hard. But Keras runs on top of TensorFlow and is relatively fast. Keras is also deeply integrated with TensorFlow, so you can create customized workflows with ease. ✅Keras is used commercially by many companies like Netflix, Uber, Square, Yelp, etc which have deployed products in the public domain which are built using Keras. ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… 👉 Learn More At: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
TensorFlow is a tool for machine learning capable of building deep neural networks with high-level Python code. It provides developer-friendly APIs that help software engineers train, analyze, and deploy ML models. #programming #deeplearning #100secondsofcode 🔗 Resources TensorFlow Docs 🤍 Fashion MNIST Tutorial 🤍 Neural Networks Overview for Data Scientists 🤍 Machine Learning in 100 Seconds 🤍 🔥 Get More Content - Upgrade to PRO Upgrade to Fireship PRO at 🤍 Use code lORhwXd2 for 25% off your first payment. 🎨 My Editor Settings - Atom One Dark - vscode-icons - Fira Code Font 🔖 Topics Covered - What is TensorFlow? - How to build a neural network with TensorFlow - What is TensorFlow used for? - Who created TensorFlow? - How neural networks work - Easy neural network tutorial - What is a mathematical Tensor?
Google Software Engineer Matthew Watson highlights Keras Preprocessing Layers’ ability to streamline model development workflows. Follow along as he builds an end-to-end model showing what you can do with these layers. Chapters 0:00 - Introduction 1:18 - Identifying the problem 6:01 - What are Keras preprocessing layers 9:45 - Preprocessing layers that are offered 17:37 - Transforming inputs from strings to a numeric input 22:17 - Building a simple model 24:13 - Adding a new feature 27:40 - Better performance with tf.data 33:22 - Multi worker training 35:26 - Takeaways Resources: Matthew Watson Github → 🤍 Preprocessing layers guide → 🤍 Text loading tutorial → 🤍 Image loading tutorial → 🤍 Watch more ML Tech Talks → 🤍 Subscribe to TensorFlow → 🤍 product: TensorFlow - General;
🔥AI/ML Course for 3-8 Yrs Work Exp: 🤍 🔥AI/ML Course for 0-3 Yrs Work Exp: 🤍 🔥AI/ML Course for 8+ Yrs Work Exp: 🤍 In this video, Keras Sequential Model Explained, we will talk in depth about sequential models in Keras. You will learn what Keras is and understand about computational graphs. You will get an idea about neural networks and how sequential model works. Finally, we'll see a Keras Sequential Model example with Python. The below topics are covered in this Keras Sequential Model video: 1. What is Keras? 2. Computational Graphs 3. What are Neural Networks? 4. Sequential Models 5. Demo on Sequential models using Python. 🔥Enroll for Free AI Course & Get Your Completion Certificate: 🤍 ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 ⏩ Check out the Artificial Intelligence training videos: 🤍 #KerasSequentialModel #KerasSequentialModelExplained #KerasSequentialModelExample #KerasTutorial #KerasTutorialForBeginners #MachineLearning #Simplilearn What is Keras? Keras is a high-level, deep learning API developed by Google for implementing neural networks. It is written in Python and is used to make the implementation of neural networks easy. It also supports multiple backend neural network computation. Keras is relatively easy to learn and work with because it provides a python frontend with a high level of abstraction while having the option of multiple back-ends for computation purposes. This makes Keras slower than other deep learning frameworks, but extremely beginner-friendly. What is Keras Sequential Model? The core idea of Keras Sequential Model is to arrange the Keras layers in a sequential order. You can create a Sequential model by passing a list of layers to the Sequential constructor. To add a Keras layer, you can just create a layer using Keras API and then pass the layer through the add() function. ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… 👉 Learn More At: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
🔵 Intellipaat Artificial Intelligence Master's Course: 🤍 🔵 In this video on Keras, you will understand what is Keras and why do we need it, how to compose different models in Keras like the Sequential model and functional model, and later on how to define the inputs, how to connect layers over, and finally hands-on demo. #KerasTutorialForBeginners #WhatIsKeras #KerasTraining #KerasTutorial #KerasForBeginners #KerasSequentialModel #Intellipaat 🔵 The following questions are covered in this video: 00:00 - Keras Tutorial For Beginners 01:30 - Why Keras? 3:56 - What is Keras? 4:34 - Composing Models in Keras 4:40 - Sequential Models 6:22 - Functional Models 07:38 - Defining the input 08:08- Connecting Layers 08:58 - Creating the Model 09:28 - Predefined Neural Network Layers 09:46 - Performing regularization Using Keras 15:48 - Dropout 18:28 - Data Augmentation 21:04 - Hands-on Demo 🔵 To subscribe to the Intellipaat channel & get regular updates on videos: 🤍 🔵 Read the complete Artificial Intelligence tutorial here: 🤍 🔵 Watch Artificial Intelligence video tutorials here: 🤍 🔵 Interested to learn Artificial Intelligence still more? Please check a similar what is Artificial Intelligence Blog here: 🤍 If you’ve enjoyed this Keras video, Like us and Subscribe to our channel for more similar informative videos and free tutorials. Intellipaat Edge 1. 24*7 Lifetime Access & Support 2. Flexible Class Schedule 3. Job Assistance 4. Mentors with +14 yrs 5. Industry Oriented Courseware 6. Lifetime free Course Upgrade 🔵 Why Keras is important Keras is an Open Source Neural Network library written in Python that runs on top of Theano or Tensorflow. It is designed to be modular, fast, and easy to use. Keras is very quick to make a network model. If you want to make a simple network model with a few lines, Keras can help you with that. Call Our Course Advisors IND: +91-7022374614 US: 1-800-216-8930 (Toll-Free) sales🤍intellipaat.com Website: 🤍 Facebook: 🤍 LinkedIn: 🤍 Telegram: 🤍 Instagram: 🤍 Twitter: 🤍 Meetup: 🤍
What is Keras? In this video, I introduce Keras and explain what exactly it is. Keras is a high-level API that runs on top of large machine learning libraries like Tensorflow, Microsoft Cognitive Toolkit (CNTK), and Theano. Watch the video to learn more! Link to Neural Networks series: 🤍
This is a clip from a conversation with Francois Chollet from Sep 2019. New full episodes every Mon & Thu and 1-2 new clips or a new non-podcast video on all other days. You can watch the full conversation here: 🤍 (more links below) Podcast full episodes playlist: 🤍 Podcasts clips playlist: 🤍 Podcast website: 🤍 Podcast on iTunes: 🤍 Podcast on Spotify: 🤍 Podcast RSS: 🤍 Note: I select clips with insights from these much longer conversation with the hope of helping make these ideas more accessible and discoverable. Ultimately, this podcast is a small side hobby for me with the goal of sharing and discussing ideas. For now, I post a few clips every Tue & Fri. I did a poll and 92% of people either liked or loved the posting of daily clips, 2% were indifferent, and 6% hated it, some suggesting that I post them on a separate YouTube channel. I hear the 6% and partially agree, so am torn about the whole thing. I tried creating a separate clips channel but the YouTube algorithm makes it very difficult for that channel to grow unless the main channel is already very popular. So for a little while, I'll keep posting clips on the main channel. I ask for your patience and to see these clips as supporting the dissemination of knowledge contained in nuanced discussion. If you enjoy it, consider subscribing, sharing, and commenting. François Chollet is the creator of Keras, which is an open source deep learning library that is designed to enable fast, user-friendly experimentation with deep neural networks. It serves as an interface to several deep learning libraries, most popular of which is TensorFlow, and it was integrated into TensorFlow main codebase a while back. Aside from creating an exceptionally useful and popular library, François is also a world-class AI researcher and software engineer at Google, and is definitely an outspoken, if not controversial, personality in the AI world, especially in the realm of ideas around the future of artificial intelligence. Subscribe to this YouTube channel or connect on: - Twitter: 🤍 - LinkedIn: 🤍 - Facebook: 🤍 - Instagram: 🤍 - Medium: 🤍 - Support on Patreon: 🤍
"Keras is the preferred high level machine learning (ML) library for many ML practitioners, and it's now fully integrated into TensorFlow. Keras enables a spectrum of workflows, gradually exposing complexity when you need it to build more elaborate models. Using a simple variational autoencoder as an example, this Session introduces you to standard Keras design patterns that let you add customizations and low level TensorFlow code where it matters, keeping things simple everywhere else. Resources: Code → 🤍 Introduction to Keras for Engineers → 🤍 Customizing what happens in fit() → 🤍 Speakers: Martin Gorner, Francois Chollet Watch more: TensorFlow at Google I/O 2021 Playlist → 🤍 All Google I/O 2021 Technical Sessions → 🤍 All Google I/O 2021 Sessions → 🤍 Subscribe to TensorFlow → 🤍 #GoogleIO #ML/AI product: TensorFlow - General; event: Google I/O 2021; fullname: Martin Gorner, Francois Chollet; re_ty: Premiere;
Let's start with implementing your first neural network using Python and Keras on a Jupyter Notebook. In this section, we will build our first neural network and train it using the data we prepared in the previous lesson. Previous lesson: 🤍 Next lesson: 🤍 📙 Here is a lesson notes booklet that summarizes everything you learn in this course in diagrams and visualizations. You can get it here 👉 🤍 👩💻 All course code is in the course repository: 🤍 RESOURCES: 🏃♀️ Data Science Kick-starter mini-course: 🤍 🐼 Pandas cheat sheet: 🤍 📥 Streamlit template (updated in 2023, now for $5): 🤍 📝 NNs hyperparameters cheat sheet: 🤍 📙 Fundamentals of Deep Learning in 25 pages: 🤍 COURSES: 👩💻 Hands-on Data Science: Complete your first portfolio project: 🤍 🌎 Website - 🤍 🐥 Twitter - 🤍
Hello Everyone! I am Sayan Nath, in this video I talked about What is Keras. I hope you liked the video. #keras #deeplearning #googlesummerofcode
Take an inside look into the TensorFlow team’s own internal training sessionstechnical deep dives into TensorFlow by the very people who are building it! On this episode of Inside TensorFlow, creator of Keras, Francois Chollet gives us the overview of tf.Keras. Let us know what you think about this presentation in the comments below and stay tuned for part 2 coming next week! TensorFlow on GitHub → 🤍 Watch more from Inside TensorFlow Playlist → 🤍 Subscribe to the TensorFlow channel → 🤍
Check out a quick introduction to machine learning data processing as well as its challenges. Learn how to easily prepare your data using the new Keras Preprocessing Layers API – in particular, how to do asynchronous preprocessing as part of your data pipeline, and how to export an end-to-end model that embeds its own preprocessing logic. Resource: TensorFlow website → 🤍 Speaker: Francois Chollet Watch more: All Google I/O 2022 Sessions → 🤍 ML/AI at I/O 2022 playlist → 🤍 All Google I/O 2022 technical sessions → 🤍 Subscribe to TensorFlow → 🤍 #GoogleIO
A public livestream about Keras Core which is multi-backend framework that supports TensorFlow, PyTorch, JAX, and NumPy with the founder of Keras - Francois Chollet. PyImageSearch Blog Post on Keras Core: 🤍 #KerasDays #KerasCore #keras
Let's learn how to set up transfer learning in Keras. We will use Xception, a model to identify objects in images and fine-tune it to classify types of flowers. ▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬ 🖥️ Website: 🤍 🐦 Twitter: 🤍 🦾 Discord: 🤍 ▶️ Subscribe: 🤍 🔥 We're hiring! Check our open roles: 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ #MachineLearning #DeepLearning #Shorts
KerasCV and KerasNLP are easy-to-use libraries for state-of-the-art computer vision and natural language processing. With just a few lines of code you’ll employ the latest techniques and models for data augmentation, object detection, image and text generation, and text classification. We also demonstrate integration with the broader TensorFlow ecosystem including TFLite, TPUs, and DTensor. Resources: KerasNLP → 🤍 KerasCV → 🤍 Keras on GitHub → 🤍 KerasNLP on GitHub → 🤍 KerasCV on GitHub → 🤍 Find full set of ML resources here → 🤍 Speaker: Jonathan Bischof Watch more: Watch all the Technical Sessions from Google I/O 2023 → 🤍 Watch the AI/ML Sessions → 🤍 All Google I/O 2023 Sessions → 🤍 Subscribe to TensorFlow → 🤍 #GoogleIO Products mentioned: TensorFlow - General Event: Google I/O 2023 Speakers: Jonathan Bischof
🔥AI/ML Course for 3-8 Yrs Work Exp: 🤍 🔥AI/ML Course for 0-3 Yrs Work Exp: 🤍 🔥AI/ML Course for 8+ Yrs Work Exp: 🤍 With the Deep Learning scene being dominated by three main frameworks, it is very easy to get confused on which one to use? In this video on Keras vs Tensorflow vs Pytorch, we will clear all your doubts on which framework is better and which framework should be used by beginners, intermediates, and professionals. The topics covered in this video are : 00:00:00 What is Keras, Tensorflow and Pytorch? 00:05:27 Differences between Keras, TensorFlow and Pytorch 00:11:46 Which framework should you use? 🔥Free Deep Learning Course: 🤍 ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 ⏩ Check out the Deep Learning tutorial videos: 🤍 #KerasvsTensorflowvsPytorch #KerasvsTensorFlow #DeepLearningFrameworks #DeepLearningFrameworksComparision #ArtificialIntelligenceCourse #ArtificialIntelligenceTutorial #ArtificialIntelligenceTutorialForBeginners #Simplilearn ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… 👉 Learn More At: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
🔥AI/ML Course for 3-8 Yrs Work Exp: 🤍 🔥AI/ML Course for 0-3 Yrs Work Exp: 🤍 🔥AI/ML Course for 8+ Yrs Work Exp: 🤍 This video on Keras Image Classification Tutorial covers the basics of image classification and how to create neural networks using the Keras library. You will use the Intel data to peform image classification using Deep Learning. The video will also teach you how to use VGG16 CNN model to classify images. The below topics are covered in this video: 00:00 What is Image Classification? 01:49 Intel Image Classification Data 02:16 Creating Neural Networks with Keras 03:41 VGG16 Model 🔥Enroll for Free Deep Learning Course & Get Your Completion Certificate: 🤍 To learn more about Deep Learning, subscribe to our YouTube channel: 🤍 Watch more videos on Deep Learning: 🤍 #ImageClassification #KerasImageClassification #KerasImageClassificationTutorial #ImageClassificationUsingDeepLearning #ImageClassificationFromScratch #DeepLearning #DeepLearning #SkillUp #simplilearn Why Deep Learning? It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks. Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… 👉 Learn More At: 🤍 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688
What is transfer learning? Why do people use it to develop projects? Let's learn all about it in this video. In the second part, we also see how to implement an example transfer learning model using the Xception model in the Keras Applications library with a dataset from TensorFlow datasets. Find the notebook here to follow along with the tutorial: 🤍 Get your free AssemblyAI API token here 👇 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬ CONNECT ▬▬▬▬▬▬▬▬▬▬▬▬ 🖥️ Website: 🤍 🐦 Twitter: 🤍 🦾 Discord: 🤍 ▶️ Subscribe: 🤍 🔥 We're hiring! Check our open roles: 🤍 ▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬▬ #MachineLearning #DeepLearning
In this part we're going to be covering recurrent neural networks. The idea of a recurrent neural network is that sequences and order matters. For many operations, this definitely does. Text tutorials and sample code: 🤍 Discord: 🤍 Support the content: 🤍 Twitter: 🤍 Facebook: 🤍 Twitch: 🤍 G+: 🤍
🔥AI/ML Course for 3-8 Yrs Work Exp: 🤍 🔥AI/ML Course for 0-3 Yrs Work Exp: 🤍 🔥AI/ML Course for 8+ Yrs Work Exp: 🤍 To access the slides, click here: 🤍 ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 ⏩ Check out the Machine Learning tutorial videos: 🤍 #Keras #KerasFullCourse #KerasTutorial #KerasTutorialForBeginners #KerasFullTutorial #LearnKeras #DeepLearningWithKeras #Simplilearn 🔥Enroll for Free Deep Learning Course & Get Your Completion Certificate: 🤍 What Is Keras? Keras is a high-level, deep learning API developed by Google for implementing neural networks. It is written in Python and is used to make the implementation of neural networks easy. It also supports multiple backend neural network computation. Keras is relatively easy to learn and work with because it provides a python frontend with a high level of abstraction while having the option of multiple back-ends for computation purposes. This makes Keras slower than other deep learning frameworks, but extremely beginner-friendly. Why Do We Need Keras? ✅Keras is an API that was made to be easy to learn for people. Keras was made to be simple. It offers consistent & simple APIs, reduces the actions required to implement common code, and explains user error clearly. ✅Prototyping time in Keras is less. This means that your ideas can be implemented and deployed in a shorter time. Keras also provides a variety of deployment options depending on user needs. ✅Keras is used commercially by many companies like Netflix, Uber, Square, Yelp, etc which have deployed products in the public domain which are built using Keras. ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… Become An AI & ML Expert Today: 🎓Enhance your expertise in the below technologies to secure lucrative, high-paying job opportunities: 🟡 AI & Machine Learning - 🟢 Cyber Security - 🤍 🔴 Data Analytics - 🤍 🟠 Data Science - 🤍 🔵 Cloud Computing - 🤍
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PyTorch is a deep learning framework for used to build artificial intelligence software with Python. Learn how to build a basic neural network from scratch with PyTorch 2. #ai #python #100SecondsOfCode 💬 Chat with Me on Discord 🤍 🔗 Resources PyTorch Docs 🤍 Tensorflow in 100 Seconds Python in 100 Seconds 🤍 🔥 Get More Content - Upgrade to PRO Upgrade at 🤍 Use code YT25 for 25% off PRO access 🎨 My Editor Settings - Atom One Dark - vscode-icons - Fira Code Font 🔖 Topics Covered - What is PyTorch? - PyTorch vs Tensorflow - Build a basic neural network with PyTorch - PyTorch 2 basics tutorial - What is a tensor? - Which AI products use PyTorch?
Want to build a deep learning model? Struggling to get your head around Tensorflow? Just want a clear walkthrough of which layer to use and why? I got you! Building neural networks with Tensorflow doesn’t need to be a nightmare. If you follow a couple of key steps you can be up and running and using Tensorflow to predict a whole bunch of stuff. In fact, you can learn how to do it with Python in just 10 minutes. By the end of this video you’ll have built your very own Tensorflow model to predict churn inside of a Jupyter Notebook. What you'll learn: 1. Build a simple Tensorflow model to predict Churn 2. Training the model and make predictions on test data with Pandas 3. Save your model to disc and reload it to a Jupyter Notebook for reuse Chapters 0:00 - Start 0:18 - Introduction 0:26 - What is Tensorflow 1:03 - Start of Coding 2:47 - Importing Tensorflow into a Notebook 3:48 - Building a Deep Neural Network with Fully Connected Layers 7:13 - Training/Fitting a Tensorflow Network 8:24 - Making Predictions with Tensorflow 9:15 - Calculating Accuracy from Tensorflow Predictions 9:50 - Saving Tensorflow Models 10:09 - Loading Tensorflow Models GET THE CODE! 🤍 Links Mentioned Tensorflow Documentation: 🤍 Pandas Crash Course: 🤍 If you have any questions, please drop a comment below! Oh, and don't forget to connect with me! LinkedIn: 🤍 Facebook: 🤍 GitHub: 🤍 Happy coding! Nick P.s. Let me know how you go and drop a comment if you need a hand!
Visualize your Keras experiments in Weights & Biases (wandb) in LESS than a minute! #machinelearning #mlops #keras #tensorflow #deeplearning #ai
Welcome to a tutorial where we'll be discussing how to load in our own outside datasets, which comes with all sorts of challenges! First, we need a dataset. Let's grab the Dogs vs Cats dataset from Microsoft: 🤍 Text tutorials and sample code: 🤍 Discord: 🤍 Support the content: 🤍 Twitter: 🤍 Facebook: 🤍 Twitch: 🤍 G+: 🤍
In this video, we will understand what is Keras and Tensorflow. Tensorflow is a free and open-source library for machine learning and artificial intelligence. It was developed by Google. And it can be used for developing large-scale machine learning applications, and it is also highly used by researchers to push the state of the art in machine learning. Keras is also an open-source library for machine learning and neural network but it higher-level API compared to Tensorflow and can run on top of Tensorflow. Training of model and execution is fast in Tensorflow, while it is slow in Keras. Thus Keras is used mainly for rapid prototyping and applications dealing with small datasets. Whereas, Tensorflow is used for creating large-scale applications. ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Timestamps: 0:00 Keras and Tensorflow Overview 1:45 Difference between Keras and Tensorflow 3:02 End ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ Follow my entire playlist on Convolutional Neural Network (CNN) : 📕 CNN Playlist: 🤍 At the end of some videos, you will also find quizzes 📑 that can help you to understand the concept and retain your learning. ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ ✔ Complete Neural Network Playlist:🤍 ✔ Complete Logistic Regression Playlist: 🤍 ✔ Complete Linear Regression Playlist: 🤍 ➖➖➖➖➖➖➖➖➖➖➖➖➖➖➖ If you want to ride on the Lane of Machine Learning, then Subscribe ▶ to my channel here: 🤍
AI & Deep Learning with Tensorflow Training: 🤍 This Edureka video on "Keras vs TensorFlow vs PyTorch" will provide you with a crisp comparison among the top three deep learning frameworks. It provides a detailed and comprehensive knowledge about Keras, TensorFlow and PyTorch and which one to use for what purposes. Following topics will be covered in this video: 1:06 - Introduction to keras, Tensorflow, Pytorch 2:13 - Parameters of Comparison 2:18 - Level of API 3:06 - Speed 3:28 - Architecture 4:03 - Ease of Code 4:27 - Debugging 4:59 - Community Support 5:19 - Datasets 5:37 - Popularity 6:14 - Suitable use cases Subscribe to our channel to get video updates. Hit the subscribe button above 🤍 PG in Artificial Intelligence and Machine Learning with NIT Warangal : 🤍 Post Graduate Certification in Data Science with IIT Guwahati - 🤍 (450+ Hrs || 9 Months || 20+ Projects & 100+ Case studies) Instagram: 🤍 Facebook: 🤍 Twitter: 🤍 LinkedIn: 🤍 Check our complete Deep Learning With TensorFlow playlist here: 🤍 #keras #tensorflow #pytorch #deeplearning #machinelearning #frameworks - - - - - - - - - - - - - - How it Works? 1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each. 2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course. 3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate! - - - - - - - - - - - - - - About the Course Edureka's Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders. Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course. - - - - - - - - - - - - - - Who should go for this course? The following professionals can go for this course: 1. Developers aspiring to be a 'Data Scientist' 2. Analytics Managers who are leading a team of analysts 3. Business Analysts who want to understand Deep Learning (ML) Techniques 4. Information Architects who want to gain expertise in Predictive Analytics 5. Professionals who want to captivate and analyze Big Data 6. Analysts wanting to understand Data Science methodologies However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio. - - - - - - - - - - - - - - Why Learn Deep Learning With TensorFlow? TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning. - Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information, please write back to us at sales🤍edureka.co or call us at IND: 9606058406 / US: 18338555775 (toll-free). -
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🔥AI/ML Course for 3-8 Yrs Work Exp: 🤍 🔥AI/ML Course for 0-3 Yrs Work Exp: 🤍 🔥AI/ML Course for 8+ Yrs Work Exp: 🤍 Learn to implement neural networks faster and easier with this tutorial on Keras! Learn how to prepare data from scratch, make, train, and compile a neural network and build your fast mask detection model! Keras tutorial for beginners, Keras for deep learning, deep learning tutorial, Keras tutorial, Keras tutorial for beginners, Keras tutorial TensorFlow, Keras tutorial python, Keras tutorial deep learning, Keras tutorial image classification, Keras neural network tutorial, Keras model tutorial, Keras layers tutorial, Keras explained, keras TensorFlow tutorial, keras python, keras example, keras install, keras tutorial CNN, CNN tutorial, convolutional neural network tutorial, deep learning tutorial, simplilearn 🔥Enroll for Free Deep Learning Course & Get Your Completion Certificate: 🤍 ✅Subscribe to our Channel to learn more about the top Technologies: 🤍 ⏩ Check out the Machine Learning tutorial videos: 🤍 #KerasTutorial #KerasTutorialForBeginners #WhatIsKeras #WhatIsKerasInDeepLearning #WhatIsKerasInMachineLearning #KerasForDeepLearning #DeepLearningTutorial #ArtificialIntelligence #Simplilearn What Is Keras? Keras is a high-level, deep-learning API developed by Google for implementing neural networks. It is written in Python and is used to simplify the implementation of neural networks. It also supports multiple backend neural network computations. Keras is relatively easy to learn and work with because it provides a python frontend with a high level of abstraction while having the option of multiple backends for computation purposes. Keras allows you to switch between different back ends. Why Do We Need Keras? ✅ Keras is an API that was made easy to learn for people. Keras was made to be simple. It offers consistent & simple APIs, reduces the actions required to implement common code, and explains user errors clearly. ✅ Prototyping time in Keras is less. This means that your ideas can be implemented and deployed in a shorter time. Keras also provides a variety of deployment options depending on user needs. ✅ The research community for Keras is vast and highly developed. The documentation and help available are far more extensive than other deep learning frameworks. ✅ Keras is used commercially by many companies like Netflix, Uber, Square, Yelp, etc., which have deployed products in the public domain built using Keras. ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… 👉 Learn More At: 🤍
We will go over what is the difference between pytorch, tensorflow and keras in this video. Pytorch and Tensorflow are two most popular deep learning frameworks. Pytorch is by facebook and Tensorflow is by Google. Keras is not a full fledge deep learning framework, it is just a wrapper around Tensorflow that provides some convenient APIs. 🔖 Hashtags 🔖 #pytorch #tensorflow #keras #tensorflowtutorial #keratutorial #pytorchtutorial Do you want to learn technology from me? Check 🤍 for my affordable video courses. Next video: 🤍 Previous video: 🤍 Deep learning playlist: 🤍 Prerequisites for this series: 1: Python tutorials (first 16 videos): 🤍 2: Pandas tutorials(first 8 videos): 🤍 3: Machine learning playlist (first 16 videos): 🤍 #️⃣ Social Media #️⃣ 🔗 Discord: 🤍 📸 Dhaval's Personal Instagram: 🤍 📸 Instagram: 🤍 🔊 Facebook: 🤍 📝 Linkedin (Personal): 🤍 📝 Linkedin (Codebasics): 🤍 📱 Twitter: 🤍 🔗 Patreon: 🤍
🔥Intellipaat Artificial Intelligence Master's Course: 🤍 In this video on keras vs tensorflow you will understand about the top deep learning frameworks used in the IT industry, and which one should you use for better performance. So in this keras vs tensorflow comparison some important parameters have been taken into consideration to tell you the difference between keras and tensorflow also which one is preferred over the other in certain aspects in detail. #KerasvsTensorflow #DeepLearningFrameworksComparison #TensorflowvsKeras #Intellipaat 📌 Do subscribe to Intellipaat channel & get regular updates on videos: 🤍 📕 Read complete Artificial Intelligence tutorial here: 🤍 📝Following topics are covered in this Keras vs Tensorflow comparison tutorial: 01:35 - What is Keras? 02:03 - What is Tensorflow? 02:40 - Differentiating between Keras and Tensorflow 05:21 - Benifits of using Keras 06:05 - Benifits of using Tensorflow 06:45 - Limitation of using Keras 07:45 - Limitation of using Tensorflow 09:00 - Popularity and trends in Keras and Tensorflow 10:00 - Which is better to choose? 11:40 -Quiz 🔗 Watch Artificial Intelligence video tutorials here: 🤍 📰Interested to learn Artificial Intelligence still more? Please check similar what is Artificial Intelligence Blog here: 🤍 If you’ve enjoyed this Keras vs Tensorflow which is better video, Like us and Subscribe to our channel for more similar informative videos and free tutorials. What do you think which one of them is better among Tensorflow vs Keras according to you? Tell us in the comment section below. Intellipaat Edge 1. 24*7 Life time Access & Support 2. Flexible Class Schedule 3. Job Assistance 4. Mentors with +14 yrs 5. Industry Oriented Course ware 6. Life time free Course Upgrade Why Keras is important Keras is an Open Source Neural Network library written in Python that runs on top of Theano or Tensorflow. It is designed to be modular, fast and easy to use. Keras is very quick to make a network model. If you want to make a simple network model with a few lines, Keras can help you with that. Why Tensorflow is important TensorFlow is an open source machine learning framework for carrying out high-performance numerical computations. It provides excellent architecture support which allows easy deployment of computations across a variety of platforms ranging from desktops to clusters of servers, mobiles, and edge devices. For more Information: Please write us to sales🤍intellipaat.com, or call us at: +91- 7847955955 Website: 🤍 Facebook: 🤍 LinkedIn: 🤍 Telegram: 🤍 Instagram: 🤍 Twitter: 🤍
Get notified of the free Python course on the home page at 🤍 Github repo for the code: 🤍 Sign up for the Full Stack course here and use YOUTUBE50 to get 50% off: 🤍 Hopefully you enjoyed this video. 💼 Find AWESOME ML Jobs: 🤍 Oh, and don't forget to connect with me! LinkedIn: 🤍 Facebook: 🤍 GitHub: 🤍 Patreon: 🤍 Join the Discussion on Discord: 🤍 Happy coding! Nick P.s. Let me know how you go and drop a comment if you need a hand! #machinelearning #python #datascience
What is Keras and how can you use it with Transformers models. This video is part of the Hugging Face course: 🤍 Related videos: - Datasets overview: 🤍 - Fine-tuning with TensorFlow: 🤍 - Learning Rate Scheduling in TensorFlow: 🤍 - Prediction and metrics: 🤍 Have a question? Checkout the forums: 🤍 Subscribe to our newsletter: 🤍
Установка пакета Keras - оболочки над TensorFlow. Сервис colabs от Google для экспериментов по построению и обучению нейросетей. Пример использования API Keras для задачи перевода градусов Цельсия в градусы Фаренгейта. Последовательная модель нейронной сети (keras.Sequential). Создание полносвязного слоя нейронов (Dense). Линейная активационная функция: activation='linear'. Компиляция модели сети: model.compile(). Запуск обучения сети: model.fit(). Подача на вход сети данных и вычисление выходного значения: model.predict(). Получение значений весовых коэффициентов: model.get_weights(). Телеграм-канал: 🤍 Инфо-сайт: 🤍 lesson 7. keras_grads.py: 🤍 Коллаборатория Google: 🤍 Keras (документация): 🤍
Tuning and optimizing neural networks with the Keras-Tuner package: 🤍 Kite AI autocomplete for Python download: 🤍 Text-based tutorial and sample code: 🤍 Starting model: from tensorflow import keras from tensorflow.keras.layers import Conv2D, MaxPooling2D, Dense, Flatten, Activation model = keras.models.Sequential() model.add(Conv2D(32, (3, 3), input_shape=x_train.shape[1:])) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(32, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) # this converts our 3D feature maps to 1D feature vectors model.add(Dense(10)) model.add(Activation("softmax")) model.compile(optimizer="adam", loss="sparse_categorical_crossentropy", metrics=["accuracy"]) model.fit(x_train, y_train, batch_size=64, epochs=1, validation_data = (x_test, y_test)) Channel membership: 🤍 Discord: 🤍 Support the content: 🤍 Twitter: 🤍 Instagram: 🤍 Facebook: 🤍 Twitch: 🤍 #deeplearning #tutorial #keras
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