Introduction

As the demand for smarter, more automated systems grows across industries, machine learning (ML) and deep learning (DL) have become the backbone of innovation. From self-driving cars to personalized recommendations, AI applications are transforming the way we live and work. One of the key frameworks driving this revolution is TensorFlow.

TensorFlow is an open-source library developed by Google that enables developers to build, train, and deploy machine learning and deep learning models. It is known for its flexibility, scalability, and efficiency in handling large datasets and complex algorithms. Whether you’re a researcher, data scientist, or developer, TensorFlow provides a powerful toolkit to solve a wide variety of problems using AI.

In this blog, we will explore what TensorFlow is, its top 10 use cases, the features that make it popular, how TensorFlow works and its architecture, the process to install TensorFlow, and provide basic tutorials to help you get started with TensorFlow.

What is TensorFlow?

TensorFlow is an open-source software library primarily used for machine learning (ML) and deep learning (DL) applications. Developed by the Google Brain team, it provides a robust platform for building and training machine learning models, performing numerical computation, and conducting research in AI. TensorFlow supports a wide range of tasks, from simple linear regression to complex neural network models used in image and speech recognition.

TensorFlow offers a high-level interface for ease of use and is highly optimized for both CPU and GPU processing, making it ideal for large-scale machine learning applications. It is widely used by researchers, data scientists, and engineers to develop state-of-the-art AI models and applications.

Top 10 Use Cases of TensorFlow

TensorFlow’s versatility allows it to be applied in various domains. Below are the top 10 use cases where TensorFlow excels:

1. Image Classification and Computer Vision

One of the most popular use cases for TensorFlow is image classification. Using deep learning models, TensorFlow can be trained to recognize objects within images. Applications include facial recognition, object detection, and medical image analysis, where TensorFlow models can help identify diseases from scans like X-rays or MRIs.

2. Natural Language Processing (NLP)

TensorFlow is widely used in natural language processing (NLP) for tasks such as sentiment analysis, text classification, language translation, and speech recognition. With the help of recurrent neural networks (RNNs) and transformers, TensorFlow enables machines to understand and process human language more effectively.

3. Recommendation Systems

Recommendation systems, such as the ones used by Netflix, Amazon, and YouTube, rely heavily on machine learning algorithms. TensorFlow is often used to build and train recommendation models that analyze user preferences and behaviors to suggest relevant content, products, or services.

4. Speech Recognition and Synthesis

TensorFlow plays a key role in speech recognition systems, such as voice assistants like Google Assistant or Alexa. It is used to train models that convert spoken language into text (speech-to-text) and vice versa (text-to-speech). Additionally, TensorFlow can be used to build systems that recognize specific voice commands or transcribe audio recordings.

5. Time Series Prediction and Forecasting

In industries like finance, energy, and healthcare, TensorFlow is used to predict future values based on historical data. Time series forecasting models built with TensorFlow can help forecast stock prices, energy consumption, demand for products, and patient health outcomes.

6. Autonomous Vehicles

Self-driving cars rely on deep learning and computer vision to navigate and make decisions. TensorFlow is used in training models that help autonomous vehicles interpret sensor data (like cameras, LiDAR, and radar), identify obstacles, and make real-time decisions on the road.

7. Anomaly Detection and Fraud Detection

TensorFlow is widely used in anomaly detection applications, where it identifies unusual patterns in data. For example, in fraud detection, TensorFlow models can analyze transaction data in real time and flag suspicious activities, such as unauthorized credit card usage or identity theft.

8. Generative Models (GANs)

TensorFlow is used to create Generative Adversarial Networks (GANs), which are a class of machine learning models that can generate new, synthetic data based on patterns learned from existing datasets. GANs are widely used in image generation, video creation, art generation, and more.

9. Healthcare and Medical Research

In healthcare, TensorFlow is applied to analyze medical images, predict disease outbreaks, and help researchers find new drug treatments. TensorFlow can be used for disease prediction (e.g., cancer detection), genomics, and personalized medicine, enabling better outcomes for patients.

10. Robotics and AI in Manufacturing

TensorFlow is used in the development of intelligent robots that can perform tasks like object manipulation, picking, and assembly in manufacturing environments. These robots rely on deep learning models to interpret sensory data and make autonomous decisions to carry out complex tasks.

What Are the Features of TensorFlow?

TensorFlow offers a wide range of features that make it a popular choice for machine learning practitioners. Some key features include:

• Open-Source: TensorFlow is open-source, which means it is free to use and can be customized to meet specific needs.
• Scalability: TensorFlow is designed for scalability, enabling users to run models on everything from personal computers to distributed clusters and cloud environments.
• Cross-Platform Support: TensorFlow supports various platforms, including desktop, mobile (Android/iOS), and embedded systems.
• GPU/TPU Acceleration: TensorFlow supports GPU and TPU acceleration, which enables faster training of deep learning models.
• TensorFlow Serving: For deploying machine learning models in production, TensorFlow provides tools like TensorFlow Serving for serving models at scale.
• TensorFlow Lite: A lightweight version of TensorFlow designed for mobile and embedded devices, allowing AI models to be deployed on smartphones, IoT devices, and edge computing platforms.
• Pre-trained Models: TensorFlow offers many pre-trained models, which can be fine-tuned for specific use cases, reducing the time required to build and train models from scratch.
• Eager Execution: TensorFlow supports eager execution for immediate feedback and debugging, which allows you to run operations immediately as they are called.
• Keras Integration: Keras, a high-level neural network API, is integrated into TensorFlow, providing an easy-to-use interface for building deep learning models.
• Extensive Ecosystem: TensorFlow has a rich ecosystem of tools and libraries, such as TensorFlow Extended (TFX), TensorFlow Hub, and TensorFlow.js, to help with model deployment, feature engineering, and more.

How TensorFlow Works and Architecture

TensorFlow’s architecture is designed for flexibility and scalability. The framework is based on the concept of dataflow graphs, where computations are represented as a graph of nodes, with each node performing a mathematical operation. These graphs are made up of tensors, which are multi-dimensional arrays that flow through the graph during computation.

Here’s how TensorFlow works:

1. Graph Construction: You first define a graph that specifies how data will flow through the operations.
2. Session Execution: Once the graph is defined, you can execute it within a session. The data is passed through the graph, and the operations are executed.
3. Tensors: Data within the graph is represented as tensors. Tensors are the fundamental data structure in TensorFlow and are used to represent data arrays of any shape and dimension.
4. Operations: Operations are mathematical functions (e.g., addition, multiplication) that are applied to tensors to process and transform data.

TensorFlow can run these computations on a variety of devices, including CPUs, GPUs, and TPUs (Tensor Processing Units). This makes TensorFlow highly scalable, allowing it to be used for both small-scale projects and large, distributed systems.

How to Install TensorFlow?

Installing TensorFlow is easy and can be done in a few simple steps. Here’s how to install TensorFlow on your system:

1. Install TensorFlow via pip

The easiest way to install TensorFlow is using pip, the Python package manager. Run the following command in your terminal:

pip install tensorflow

If you’re using Python 3, use:

pip3 install tensorflow

2. Install TensorFlow with GPU Support

To take advantage of GPU acceleration, you can install the GPU version of TensorFlow by running:

pip install tensorflow-gpu

Ensure that you have the required GPU drivers and CUDA toolkit installed for GPU support.

3. Verify Installation

Once installed, verify that TensorFlow is installed correctly by running the following code in Python:

import tensorflow as tf
print(tf.__version__)

If TensorFlow is installed correctly, it will display the version number.

Basic Tutorials of TensorFlow: Getting Started

1. Building a Simple Neural Network

A common starting point is building a simple neural network for classification tasks. Here’s a basic example of building a neural network to classify the MNIST dataset (handwritten digits):

import tensorflow as tf
from tensorflow.keras import layers, models

# Load dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Preprocess data
x_train, x_test = x_train / 255.0, x_test / 255.0

# Build model
model = models.Sequential([
    layers.Flatten(input_shape=(28, 28)),
    layers.Dense(128, activation='relu'),
    layers.Dropout(0.2),
    layers.Dense(10, activation='softmax')
])

# Compile model
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train model
model.fit(x_train, y_train, epochs=5)

# Evaluate model
model.evaluate(x_test, y_test)

2. Working with TensorFlow Datasets

TensorFlow provides a convenient way to work with datasets, including built-in datasets like MNIST. You can load and preprocess datasets using tf.data API, which provides an efficient way to input data into your model.

3. Saving and Loading Models

Once a model is trained, you can save it and reload it for future use:

# Save model
model.save('my_model.h5')

# Load model
loaded_model = tf.keras.models.load_model('my_model.h5')

The Power of TensorFlow for Machine Learning and AI

TensorFlow is a powerful, flexible, and scalable framework that enables businesses, researchers, and developers to build cutting-edge machine learning and deep learning models. Whether you’re working on image recognition, natural language processing, or time series forecasting, TensorFlow provides the tools and infrastructure needed to train, test, and deploy complex AI systems.

With its comprehensive features, rich ecosystem, and strong community support, TensorFlow continues to be a top choice for machine learning practitioners and organizations looking to leverage AI for innovative solutions.

The post What is TensorFlow and Use Cases of TensorFlow? appeared first on Artificial Intelligence.

While organizations are increasingly leveraging ML tools, most of the projects fail during the test phase.

Machine learning (ML) has been touted as one of the key enablers of the Fourth Industrial Revolution. In recent times, businesses explore new approaches to maximize their profits and reach, without compromising on customer services. Machine learning helps them mine data from relevant sources and analyze it to understand trends, behavior and more. As IT enterprises integrate ML-driven insights into their organizational framework, MLOps is leveraged to enhance the operations and deployment during the lifecycle of machine learning model development and usage.

Machine learning, is a category of artificial intelligence that enables an AI model or system to learn from the datasets provided and retrain itself from the feedback and analysis carried over time. With the influx of big data innovations and advancements in AI and computing power, the capability of machine learning has grown tremendously over the years. Yet companies struggle to deploy its products or are clueless about where to apply them. This is where MLOps proves to be handy.

In general terms, MLOps is DevOps with enhanced capabilities of machine learning.  It helps data scientists and IT teams to manage the production machine learning lifecycle. This enables automation and monitoring at all steps of machine learning system construction, including integration, testing, releasing, deployment and infrastructure management. Further, it facilitates rapid innovation through robust machine learning lifecycle management. As per the ‘State of AI’ report by Ian Hogarth and Nathan Benaich, MLOps accounts for 25% of GitHub’s fastest-growing projects in 2020.

There are numerous MLOps frameworks for managing the life cycle of machine learning. Analytics Insight brings you a list of top 10 MLOps platform:

MLFlow:

It is an open-source platform for managing the end-to-end machine learning lifecycle including experimentation, reproducibility, deployment, and a central model registry. It provides three primary functionalities: tracking experiments, packaging ML code (projects), and managing and deploying models. This implies that a user can track an experiment, organize it, describe it for other ML engineers and pack it into a machine learning model.

Amazon SageMaker Studio:

This fully managed integrated environment, web-based tool by Amazon, allows data scientists to manage an entire machine learning lifecycle, i.e. from building and training to deploying machine learning models. One can also quickly upload data, create new notebooks, train and tune models, move back and forth between steps to adjust experiments, compare results, and deploy models to production all in one place, thus enabling more productivity.

Kubeflow:

It is a software with the main goal of run orchestration and making deployments of machine learning workflows, primarily on Kubernetes easier. It comprises of services to create and manage interactive Jupyter notebooks. It helps user to handle distributed TensorFlow training jobs. It also offers Multi-framework integration, e.g.  Istio and Ambassador for ingress.

Algorithmia:

It is an enterprise MLOps platform that allows users to deploy, manage, and scale their machine learning portfolio, on-demand (serverless) using CPUs and GPUs. It can either run on its own cloud, on user premises, on VMware, or on a public cloud. Besides, it also maintains models in its own Git repository or on GitHub, manages model versioning automatically, and can implement pipelining.

TensorFlow Extended (TFX):

It is a scalable end-to-end platform, based on TensorFlow, for production and deployment of machine learning pipelines. It provides a configuration framework and shared libraries to integrate common components needed to define, launch, and monitor ML systems. TFX is based on pipeline concept, which helps it define a data flow through several components, with the goal of implementing a specific ML task.

HPE Ezmeral ML Ops:

It brings, DevOps-like agility to the entire machine learning lifecycle at enterprise level using containers. It supports every stage of ML lifecycle—data preparation, model build, model training, model deployment, collaboration, and monitoring. This end-to-end data science solution also offers the flexibility to run on-premises, in multiple public clouds, or a hybrid model and responds to dynamic business requirements in a variety of use cases.

Seldon:

It is an open-source platform that allows data scientists and IT developers to develop core building blocks of machine learning. Seldon’s open-source machine learning deployment platform helps data science teams solve problems faster and more effectively. It’s designed to streamline the data science workflow, with audit trails, advanced experiments, continuous integration and deployment, rolling updates, scaling, model explanations, and more.

Paperspace:

It is a high-performance cloud computing and ML development platform for building, training and deploying machine learning models. Its Gradient feature is a suite of tools for exploring data, training neural networks, and building production-grade machine learning pipelines; while Paperspace Core can manage virtual machines with CPUs and optionally GPUs, running in Paperspace’s own cloud or on AWS.

Azure Machine Learning:

It is a cloud-based environment that companies can use to train, deploy, automate, manage, and track machine learning models. It has its’ own open-source MLOps environment. Also Azure Machine learning, can interoperate with popular open source tools, such as PyTorch, TensorFlow, Scikit-learn, Git, and the MLflow platform to manage the machine learning lifecycle.

Data Version Control (DVC):

It is an open-source tool for data science and machine learning. Its key features include a simple command line similar to Git and it does not require any database or proprietary online services. It enables data scientists to share the machine learning models and make them reproducible. The DVC user interface can cope with versioning and organization of big amounts of data and store them in a well-organized, accessible way.

The post TOP MLOPS BASED TOOLS FOR ENABLING EFFECTIVE MACHINE LEARNING LIFECYCLE appeared first on Artificial Intelligence.

Google’s Android Studio IDE team has released the stable version of Android Studio 4.1, featuring machine learning improvements and a database inspector.

With the 4.1 release, Android Studio improves on-device machine learning support via backing for TensorFlow Lite models in Android projects. Android Studio generates classes so models can be run with better type safety and less code.

The database inspector, meanwhile, enables querying of an app’s database, whether the app uses the Jetpack Room library or the Android platform version of SQLite directly. Values can be modified using the database inspector, with changes seen in apps.

Introduced October 12 and accessible from developer.android.com, Android Studio 4.1 also makes it easier to navigate Dagger-related dependency injection code by providing a new gutter action and extending support in the Find Usages Window. For example, clicking on the gutter action next to a method that consumes a given type navigates to where a type is used as a dependency.

Other capabilities in Android Studio 4.1 include templates in the create New Project dialog now use Material Design Components and conform to updated guidance for themes and styles by default. These changes make it easier to recommended material styling patterns and support UI features such as dark themes.

Android Emulator now can also be run directly in Android Studio. This can conserve screen real estate and enable navigation quickly between the emulator and editor window using hotkeys. Also, the emulator now supports foldables, with developers able to configure foldable devices with a variety of designs and configurations.

In addition, symbolification for native crash reports is available; updates to Apply Changes allow for faster builds and the Android Studio Memory Profiler now includes a Native Memory Profiler for apps deployed to physical devices running Android 10 or later.

The Native Memory Profiler tracks allocations and deallocations of objects in native code for a specific time period and offers information about total allocations and remaining heap size.

Rounding off the changes, C/C++ dependencies can be exported from AAR (Android Archive) files; Android Studio Profilers can be accessed in a separate window from the primary Android Studio window, which is useful for game developers; System Trace UI improvements are on offer and 2,370 bugs were fixed and 275 public issues were closed.

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Did you ever wonder what goes on inside the brain of a data scientist?

A few years ago, PerceptiLabs, a deep tech startup, took on an ambitious goal — to visualize what data scientists see when they are building a machine learning model. In doing so, they reinvented the process of model building, making it simpler and faster for experts and beginners alike, to build, train, and analyze their models, so companies could speed up their innovation process.

It’s not news that AI is transforming the world in which we live. Banks are using AI to identify potential fraud, healthcare providers use AI to assist with diagnosis, grocery stores build algorithms to predict consumer behavior, and much more. Today, as businesses rush to accelerate their digital transformations due to COVID-19, AI is becoming more crucial, penetrating more business-critical functions.

To enable AI to do all these great things, the field has generally relied on experts (highly trained data scientists) to build and train complex mathematical models also called machine learning models. This is a complex time-consuming process, involving thousands of lines of code. To see what the models were doing, the experts have to use their imagination to visualize the models in their heads.

As AI and ML took hold and the experience levels of AI practitioners diversified, efforts to democratize ML materialized into a rich set of open source frameworks like TensorFlow and datasets. Advanced knowledge is still required for many of these offerings, and experts are still relied upon to code end-to-end ML solutions. This can have some advantages when building customized solutions, but can require a large investment in resources, infrastructure, and maintenance.

More recently a variety of AutoML tools have launched, promising end-to-end capabilities, where data is input, parameters are adjusted, and a fully-trained, deployable ML model is generated. The simplicity of this sounds inviting — indeed it’s appropriate in certain scenarios — however, ML models created through AutoML often lack transparency into their performance and can be difficult to interpret (i.e., explain why they produce certain results). As well, AutoML solutions often restrict users to only a few ML techniques.

The next generation of ML modeling

PerceptiLabs has developed a next-generation ML tool with our visual modeler that took the best of all worlds: the flexibility of code, some of the automation in connecting components, generating model architectures as well as tuning settings and hyperparameters, combined with the ease of a drag and drop UI.

This makes model building easier, faster, and accessible to a wider spectrum of users, whether you are an expert or beginner. There is also the ability to create custom models like simple linear regression, or something more complex like a GAN.

We designed our tool as a visual API on top of TensorFlow, which has grown to become the most popular ML framework. This gives developers full access to the low-level TensorFlow API and the freedom to pull in other Python modules.

Most importantly, users have full transparency into how their model is architected and a view into how their model performs. The result is a new visual approach that’s almost as good as seeing inside a data scientist’s brain!

ML modeling approaches at a glance

There are a lot of choices when it comes to building machine learning models, and each approach needs to be carefully evaluated against the resources you have available to see it through.

That’s why here at PerceptiLabs, we think that our new visual way to build machine learning models, strikes just the right balance across a wide spectrum of ML users while offering better explainability, sophistication, and usability. It’s a flexible but comprehensive approach, that lets you choose the way you want to work, depending on your experience and project needs.

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Google recently released TensorFlow Quantum, a toolset for combining state-of-the-art machine learning techniques with quantum algorithm design. This is an essential step to build tools for developers working on quantum applications.

Simultaneously, they have focused on improving quantum computing hardware performance by integrating a set of quantum firmware techniques and building a TensorFlow-based toolset working from the hardware level up – from the bottom of the stack.

The fundamental driver for this work is tackling the noise and error in quantum computers. Here’s a small overview of the above and how the impact of noise and imperfections (critical challenges) is suppressed in quantum hardware. 

Noise And Error: The Chinks In Armor When It Comes To Quantum Computers

Quantum computing combines information processing and quantum physics to solve challenging computer problems. However, a significant issue in quantum computers is susceptibility to noise and error, limiting quantum computing hardware efficiency. Noise refers to all sorts of things that can cause interference, like the electromagnetic signals from the WiFi or disturbances in the Earth’s magnetic field. Most quantum computing hardware can run just a few dozen calculations over much less than 1 ms before requiring a reset due to the noise’s influence. That is about 1024 times worse than the hardware in a laptop.

Many teams have been working to make the hardware resistant to the noise to overcome these weaknesses. Many theorists have also designed a smart algorithm called Quantum Error Correction. QEA can identify and fix errors in the hardware, but it is very slow or incapable of practice. Because the information is to be spread in one qubit over lots of qubits, it may take a thousand or more physical qubits to realize just one error-corrected “logical qubit.”

To overcome this, Q-CTRL’s “quantum firmware” can stabilize the qubits against noise and decoherence without the need for extra resources. This is done by adding the new solutions that improve the hardware’s robustness to the error at the lowest layer of the quantum computing stack.

The protocols described by the Quantum firmware are there to deliver the quantum hardware with augmented performance to higher levels of the abstraction in the quantum computing stack.

In general, quantum computing hardware relies on light-matter interaction, which is made to enact quantum logic operations.

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While deep learning is viewed as a small part of the field of artificial intelligence, it’s now a field that is by all accounts growing out of the AI space itself. Deep learning is the advancement of ‘thinking’ computer systems, called neural networks, and using it requires coding procedures unfamiliar to old-school developers. With the assistance of deep learning, we can show our computers to learn for themselves such that it gives us noteworthy outcomes. Furthermore, you get the opportunity to be at the front line, as experts in profound learning are required now like never before.

If you want to learn deep learning and don’t know where to start, we’ve compiled a list of free online courses that can help you learn deep learning.

Creative Applications of Deep Learning with TensorFlow

This course covers the essential segments of deep learning. What it implies, how it works, and creating necessary code to build different algorithms, for example, deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks. A significant offering of this course will be to not just see how to build the fundamental segments of these algorithms, yet in addition how to apply them for exploring creative applications. Free and paid choices are available.

Best Deep Learning Course (deepLearning.ai)

This is without a doubt one of the best deep learning affirmations with Andrew Ng himself teaching the subject. The Co-Founder of Global Learning Platform Coursera, Andrew has been the head of Google Brain and Baidu AI group before. Going along with him are teachers from leading institutes like Stanford. In this certification course, you will find out about the foundations of Deep Learning, realize how to build neural networks and understand about machine learning ventures. There will be real time case studies including sign language reading, music generation and natural language processing among others. Alongside all the theory, you will be educated to implement these ideas in Python and TensorFlow.

Deep Learning for Business by Yonsei University

Offered by Yonsei University, Deep Learning for Business shows the nuts and bolts of deep learning and how to implement it in your organization to accelerate outcomes. You will learn how to make business strategies that encourage technical planning on new machine learning and deep learning products.

The course starts with an overview of deep learning products and services, trailed by a module on business with deep learning and machine learning. It additionally covers deep learning Computing Systems and Software, deep learning Neural Networks and deep learning with CNN and RNN. The course finishes with a module on TensorFlow Playgrounds. There’s no expense to learn and you can finish the course in 8 hours.

Deep Learning Certification by IBM (edX)

All through this expert certificate program, you will learn and gain pro Deep Learning abilities through a series of hands-on tasks and projects. Accessible on famous elearning stage edX, the course will come full circle into a Deep Learning capstone project that will help you grandstand your applied abilities to prospective employers. In addition to other things, you will learn central ideas of Deep Learning, including different Neural Networks for both supervised and unsupervised learning. You will likewise figure out how to build and send various types of Deep Architectures including Convolutional Networks, Recurrent Networks just as Autoencoders.

Introduction to Deep Learning by the National Research University Higher School of Economics

Prologue to Deep Learning is the initial segment of the Advanced Machine Learning Specialization from the National Research University Higher School of Economics. It’s intended to assist you with understanding the basics of current neural networks and their application in computer vision and natural language.

The class incorporates video talks, readings and tests. It comprises of 5 modules:

• Introduction to optimization
• Deep learning for images
• Introduction to neural networks
• Deep learning for sequences
• Unsupervised representation learning

You will likewise be entrusted with finishing a task to showcase what you learned in the course.

Complete Guide to TensorFlow for Deep Learning Training with Python (Udemy)

Jose Marcial Portilla has a MS from Santa Clara University and has been teaching Data Science and programming for various years now. His Tensorflow Certification will assist you with figuring out how to utilize Google’s Deep Learning Framework – TensorFlow with Python. He will likewise show you how you can utilize TensorFlow for Image Classification with Convolutional Neural Networks, how to do time series analysis with Recurrent Neural Networks and instruct you to take care of unsupervised learning problems with AutoEncoders. This training has been visited by nearly 20,000 students and has exceptional reviews and ratings.

Applied AI with Deep Learning

Applied AI with Deep Learning is the third course in Advanced Data Science with IBM Specialization. Data Scientist Romeo Kienzler imparts his experience in the field to offer essential insights into deep learning. He covers a few core subjects, including deep learning frameworks, applications, scaling and deployment.

Past the video lectures used to present the material, you’ll be needed to finish readings and tests. The readings supplement the guidance given in the exercises and tests help recognize areas where you may require more work.

Register for free, and you should expect to go through 18 hours working through the course material.

Deep Learning – Google via Udacity

In this course, you’ll build up a clear understanding of profound learning, and build intelligent systems that learn from complex and additionally huge datasets. You will figure out how to tackle new classes of issues that were once thought restrictively challenging, and come to better appreciate the complex nature of human insight as you take care of these equivalent issues easily utilizing deep learning techniques.

Deep Learning Nano Degree Program (Udacity)

People who need to study how to build and apply their own deep neural networks to different issues like image classification and generation, time-series prediction, and model deployment can take help from this nano degree program. This program is exceptionally made for students who are keen on making a profession in machine learning, artificial intelligence, or deep learning. Taking a crack at this program will acquaint you with Deep Learning algorithms, neural networks, and deploying a sentiment analysis model. In the wake of completing the program with given tasks and projects, you will get a certificate of completion that can be shared with your resume and employers.

Natural Language Processing with Deep Learning – Stanford University

The course gives a careful prologue to forefront research in deep learning applied to NLP. On the model side we will cover representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some ongoing models including a memory part. Through video lectures and programming tasks students will gain proficiency with the essential designing stunts for making networks work on practical problems

The post 10 BEST ONLINE AND FREE DEEP LEARNING COURSES appeared first on Artificial Intelligence.

Himax Technologies, Inc., a leading supplier and fabless manufacturer of display drivers and other semiconductor products, announced the launch of WiseEye WE-I Plus HX6537-A solution that supports Google’s TensorFlow Lite for Microcontrollers. In this collaboration, Himax is providing the HX6537-A processor with NN (neural network) based SDK (Software Development Kit) for developers to generate deep learning inferences running on TensorFlow Lite for Microcontrollers kernel to boost overall system AI performance.

The Himax WiseEye solution is composed of the Himax HX6537-A processor and Himax AoS sensor. With support to TensorFlow Lite for Microcontrollers, developers are able to take advantage of the WE-I Plus platform as well as the integrated ecosystem from TensorFlow Lite for Microcontrollers to develop their NN based edge AI applications targeted for Notebook, TV, Home Appliance, Battery Camera and IP Surveillance edge computing markets. The benefits of the Himax HX6537-A processor are driven by three unique features.

Ultra low power and high-performance AI processor

The HX6537-A processor adopts a programmable DSP that runs at 400MHz with power-efficient and multi-level power schemes that incorporate CDM, HOG and JPEG hardware accelerators for real-time motion detection, object detection and image processing. The processor remains in low power mode until a movement/object is identified by accelerators. Afterwards, DSP coped with the running NN inference on TensorFlow Lite for Microcontrollers kernel will be able to perform the needed CV operation to send out the metadata results over TLS (Transport Level Security) protocol to main SOC and/or cloud service for application level operation. The average power consumption for Google Person Detection example inference could be under 5mW.

Support Google TensorFlow Lite for Microcontrollers

As a result of Himax support to TensorFlow Lite for Microcontrollers, it is now possible to leverage the TensorFlow ecosystem to train and deploy TensorFlow models onto Himax’s ultra low power hardware. For software development, Himax has ported WE-I Plus SDK.AI with optimized Machine Learning Inference (MLI) library dedicated to TensorFlow Lite for Microcontrollers. The MLI software library could bolster inference running on TensorFlow Lite for Microcontrollers MLI kernel 5 times faster than the reference kernel. This will enable developers to get the benefit of performance and comprehensive NN based machine learning models and kernel of TensorFlow Lite for Microcontrollers to easily deploy their video and voice-oriented AI applications.

Comprehensive WiseEye Computer Vision solution with Himax AoS Sensor

To address overall system low power needs, HX6537-A provides a proprietary sensor interface that works with Himax’s ultra low power AoS (Always On Sensor) sensor solutions to support up to VGA@60fps image input and fast wake-up for speedy sensor image capture. Additionally, average Himax AoS sensor power consumption can be less than 1mW.

“Himax WE-I Plus, coupled with Himax AoS image sensors, broadens TensorFlow Lite ecosystem offering and provides developers with possibilities of high performance and ultra low power,” said Pete Warden, Technical Lead of TensorFlow Lite for Microcontrollers at Google.

“Himax continues to demonstrate its expertise in developing innovative technologies that enable the company to partner with leaders such as Google in the AIoT industry. The unique design of HX6537-A by Himax provides a state-of-the-art CNN based deep learning SDK.AI with TensorFlow Lite for Microcontrollers which will enable developers and customers to accelerate the development of CV applications to drive ultra low power system goals for edge AI applications,” said Jordan Wu, President and Chief Executive Officer of Himax Technologies.

The post Himax Launches WiseEye WE-I Plus HX6537-A To Support AI Deep Learning With Google’s TensorFlow Lite appeared first on Artificial Intelligence.

Google has announced a new TensorFlow runtime designed to make it easier to build and deploy machine learning models across many different devices. 

The company explained that ML ecosystems are vastly different than they were 4 or 5 years ago. Today, innovation in ML has led to more complex models and deployment scenarios that require increasing compute needs.

To address these new needs, Google decided to take a new approach towards a high-performance low-level runtime and replace the current TensorFlow stack that is optimized for graph execution, and incurs non-trivial overhead when dispatching a single op.

The new TFRT provides efficient use of multithreaded host CPUs, supports fully asynchronous programming models, and focuses on low-level efficiency and is aimed at a broad range of users such as:

• researchers looking for faster iteration time and better error reporting,
• application developers looking for improved performance,
• and hardware makers looking to integrate edge and datacenter devices into TensorFlow in a modular way. 

It is also responsible for the efficient execution of kernels – low-level device-specific primitives – on targeted hardware, and playing a critical part in both eager and graph execution.

“Whereas the existing TensorFlow runtime was initially built for graph execution and training workloads, the new runtime will make eager execution and inference first-class citizens, while putting special emphasis on architecture extensibility and modularity,” Eric Johnson, TRFT product manager, and Mingsheng Hong, TFRT tech lead, wrote in a post.

To achieve higher performance, TFRT has a lock-free graph executor that supports concurrent op execution with low synchronization overhead and has decoupled device runtimes from the host runtime, the core TFRT component that drives host CPU and I/O work.

The runtime is also tightly integrated with MLIR’s compiler infrastructure to generate and optimized, target-specific representation of the computational graph that the runtime executes. 

“Together, TFRT and MLIR will improve TensorFlow’s unification, flexibility, and extensibility,” Johnson. and Hong wrote.

TFRT will be integrated into TensorFlow, and will be enabled initially through an opt-in flag, giving the team time to fix any bugs and fine-tune performance. Eventually, it will become TensorFlow’s default runtime. 

The post Google to replace TensorFlow’s runtime with TFRT appeared first on Artificial Intelligence.

Google has launched TensorFlow RunTime (TFRT), which is a new runtime for its TensorFlow machine learning framework. 

According to a recent blog post by Eric Johnson, TFRT Product Manager and Mingsheng Hong, TFRT Tech Lead/Manager, “TensorFlow RunTime aims to provide a unified, extensible infrastructure layer with best-in-class performance across a wide variety of domain-specific hardware. It provides efficient use of multithreaded host CPUs, supports fully asynchronous programming models, and focuses on low-level efficiency.”

The company has made TFRT available on GitHub. According to the company, as part of a benchmarking study for TensorFlow Dev Summit 2020 — while comparing the performance of GPU inference over TFRT to the current runtime, we saw an improvement of 28% in average inference time. These early results are strong validation for TFRT to provide a significant boost to performance.

The blog further stated how TFRT could benefit a broad range of users — including the researchers who are looking for faster iteration time and better error reporting when developing complex new models in eager mode; application developers who are looking for improved performance when training and serving models in production; and hardware makers looking to integrate edge and datacenter devices into TensorFlow in a modular way.

Explaining further, Johnson stated that TFRT is responsible for efficient execution of kernels – low-level device-specific primitives – on targeted hardware. Alongside, it also plays a critical part in both eager and graph execution.

TensorFlow training stack

“In eager execution, TensorFlow APIs call directly into the new runtime. In graph execution, your program’s computational graph is lowered to an optimised target-specific program and dispatched to TFRT. In both execution paths, the new runtime invokes a set of kernels that call into the underlying hardware devices to complete the model execution, as shown by the black arrows,” wrote Johnson.

Comparing it with TensorFlow runtime, which was initially built for graph execution and training workloads, TFRT will make eager execution and inference first-class citizens, while putting special emphasis on architecture extensibility and modularity. Besides, TFRT has the following selected design highlights:

• TFRT has a lock-free graph executor that supports concurrent op execution with low synchronisation overhead, and a thin, eager op dispatch stack so that eager API calls will be asynchronous and more efficient. This will help in achieving higher performance.
• The company decoupled device runtimes from the host runtime, the core TFRT component that drives host CPU and I/O work, in order to make extending the TF stack easier.
• To get consistent behaviour, TFRT leverages common abstractions, such as shape functions and kernels, across both eager and graph.

According to the blog, “A high-performance low-level runtime is a key to enable the trends of today and empower the innovations of tomorrow.”

The company has limited the contributions, to begin with, but is encouraging participation in the form of requirements and design discussions.

The post GOOGLE LAUNCHES TENSORFLOW RUNTIME FOR ITS TENSORFLOW ML FRAMEWORK appeared first on Artificial Intelligence.

Google has announced TensorFlow Lite Model Maker, a tool for converting an existing TensorFlow model to the TensorFlow Lite format used to serve predictions on lightweight hardware such as mobile devices.

TensorFlow models can be quite large, and serving predictions remotely from beefy hardware capable of handling them isn’t always possible.

Google created the TensorFlow Lite model format to make it more efficient to serve predictions locally, but creating a TensorFlow Lite version of a model previously required some work.

In a blog post, Google described how TensorFlow Lite Model Maker adapts existing TensorFlow models to the Lite format with only a few lines of code.

The adaptation process uses one of a small number of task types to evaluate the model and generate a Lite version. The downside is that only a couple of task types are available for use right now — i.e., image and text classification — so models for other tasks (e.g., machine vision) aren’t yet supported.

Other TensorFlow Lite tools announced in the same post include a tool to automatically generate platform-specific wrapper code to work with a given model.

Because hand-coding wrappers for models can be error-prone, the tool automatically generates the wrapper from metadata in the model autogenerated by Model Maker. The tool is currently available in a pre-release beta version, and supports only Android right now, with plans to eventually integrate it into Android Studio.

The post Google unveils TensorFlow tool for making mobile-ready models appeared first on Artificial Intelligence.