Chemically speaking, proteins might be the most sophisticated molecules out there. Millions of different kinds of them live within our cells and work together as a fine-tuned orchestra catalyzing the biochemical reactions that keep us alive.

Few things in the world would function without proteinsnot the cells within your body, and certainly not SARS-CoV-2, the coronavirus responsible for COVID-19.

The proteins in the coronavirus facilitate its remarkable ability to infect human cells without resulting in visible symptoms of COVID-19 for long periods of time. Thats why researchers around the world have been investigating the roles of each of the 29 proteins packed inside SARS-CoV-2.

By learning more about each of those proteins at the molecular level, researchers want to pin down the exact parts of the SARS-CoV-2 proteins that enable it to bind itself to other proteins on the surface of human cells and enable the virus to replicate. The idea is to inhibit those chemical reactions right from the start, and render the coronavirus ineffective.

To analyze those protein interactions, Northeastern researchers are bringing another set of tools to study the coronavirus proteins down to their amino acids, the building blocks of all proteins.

Mary Jo Ondrechen, a professor of chemistry and chemical biology, wants to identify all of the amino acids responsible for the abilities of the coronavirus to infect and thrive at the expense of human cells. Together with Penny Beuning, a professor of chemistry and chemical biology, Ondrechen recently received a grant from the National Science Foundation to use machine learning algorithms and experimental lab work to do just that.

Proteins are long chains of molecules that function through cascading interactions with amino acids form other proteins. But those interactions dont always occur in the same place within the structure of a protein where the protein carries out its chemical reaction. Often, although the interactions happen outside of that site, they still control the reaction. A specific site within a protein can also control the action of different proteins, helping or hindering a specific chemical reaction.

Changes in protein behavior resulting from these networks of interactions, or from preventing interactions, are known as allosteric regulation. Ondrechens algorithm predicts many of these and other types of interactions based on the specific molecular structures of proteins.

Research led by her and Beuning could help researchers gain a better understanding of the biochemistry of SARS-CoV-2, and serve as the basis for developing new drugs to inhibit its infectious abilities.

Researchers around the world have been rushing to develop new chemicals that show promise as compounds that could hinder the coronavirus by interacting with its main active proteins.

Still, scientists are just beginning to understand many of the coronavirus proteins. And, Ondrechen says, there might be sites within those poorly understood proteins that researchers might be failing to notice.

The program, which Ondrechens lab invented in 2009, analyzes the chemical properties of each of the individual amino acids within a protein. It could predict the roles of important but subtle interactions in SARS-CoV-2 involving amino acids that arent directly linked to the main reaction sites, and which would be too difficult to analyze with conventional bioinformatic research.

In the main protease, everybody knows where the catalytic site is, in the RNA transferase, everybody knows where the catalytic site is, Ondrechen says. Our technology is special because we could predict exo-sites, allosteric sites, and other binding sites or interaction sites that can control.

The program will run those predictions against databases that include tens of thousands of compounds with anti-viral properties and compounds found in food, all in a major attempt to find proteins that might hit the predicted sites of protein interaction.

Once the program runs the computational analysis to find candidate proteins to inhibit SARS-CoV-2, it will guide Beunings experimental tests in her lab.

Well be looking at the protein level: Do the compounds actually bind those proteins, and do they modulate the activity of the protein? Beuning says. Ideally, they would inhibit the activity of the protein, and then impair the virus.

For the past 10 years, Ondrechen and Beuning have been combining their computational and experimental power to understand such questions as how proteins control the production of our DNA, and how proteins enable our bodies to carry out some of the most important metabolic functions.

Now, they are planning to move as fast as possible to identify important protein interactions in SARS-CoV-2, test them in the lab, and move on with further tests in live organisms.

Our plans are to finish in six months, Ondrechen says. If we come up with interesting compounds in vitro, hopefully we can find a collaborator that could do in vivo testing.

For media inquiries, please contact media@northeastern.edu.

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The coronavirus might have weak spots. Machine learning could help find them. - News@Northeastern

WORTHINGTON, Ohio--(BUSINESS WIRE)--Prosper Insights & Analytics, in cooperation with the AWS Machine Learning Marketplace, has expanded their suite of China consumer marketing models. The models are created from data derived from the largest continuous survey of Chinese consumers, the Prosper China Quarterly. Prosper has been collecting the China Quarterly since 2006.

The propensity models are developed using AWS SageMaker advanced analytic tools and can be accessed through the AWS Machine Learning Marketplace. All Prosper models are 100% privacy compliant and never use any PII in any part of the process from collection through analysis.

All models are scored with metrics for accuracy, updated regularly and provide marketers with an enhanced targeting opportunity for the China market. Bespoke models available upon request. For more information, click here.

About Prosper Insights & Analytics

Prosper Insights & Analytics is a global leader in consumer intent data serving financial services, marketing technology, retail and marketing industries. We provide global authoritative market information on US and China consumers via curated insights and analytics. By integrating Prosper's unique consumer data with a variety of other data, including behavioral, attitudinal and media, Prosper helps companies accurately predict consumers' future behavior and optimize marketing efforts and improve the effectiveness of demand generation campaigns. http://www.ProsperModelFactory.com

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Prosper Teams up With AWS Machine Learning Marketplace to Expand Access to China Consumer Targeting Models - Business Wire

Over the past year, in collaboration with IEEE and ACM, ValleyML has hosted numerous talks on contemporary topics in data science, machine learning, and artificial intelligence - bringing together technical experts and inquisitive audiences. During these times of unprecedented global lockdowns due to COVID-19 pandemic, now, more than ever, we need to bring people together. To that end, ValleyML will be expanding its outreach with its Virtual and Global events.

SANTA CLARA, Calif., May 14, 2020 /PRNewswire/ --ValleyML, Valley Machine Learning and Articial Intelligence is the most active and important community of ML & AI Companies and Start-ups, Data Practitioners, Executives and Researchers. We have a global outreach to close to 200,000 professionals in AI and Machine Learning. The focus areas of our members are AI Robotics, AI in Enterprise and AI Hardware. We plan to cover the state-of-the-art advancements in AI technology.ValleyML sponsors include UL, MINDBODY Inc., Ambient Scientific Inc., SEMI, Intel, Western Digital, Texas Instruments, Google, Facebook, Cadence andXilinx.

ValleyML Machine Learning and Boot Camp -2020Build a solid foundation of Machine Learning / Deep Learning principles and apply the techniques to real-world problems. Get IEEE PDH Certificate. Virtual Live Boot Camp from July 14th-Sept 10th.Description. Enroll and Learn at ValleyML Live Learning Platform(coupons: valleyml40 Register by June 1st for 40% off. valleyml25 Register by July 1st for 25% off.)

Global Call for Presentations & Sponsors for ValleyML AI Expo 2020 conference series (Global & Virtual).A unified call for proposals from industry for ValleyML's AI Expo events focused on Hardware, Enterprise and Robotics is now open at ValleyML2020. Submit by June 1st to participate in a virtual and global series of 90-minute talks and discussions from Sept 21st to Nov 19th on Mondays-Thursdays. Sponsor AI Expo!Limited sponsorship opportunities available. These highly focused events welcome a community of CTOs, CEOs, Chief Data Scientists, product management executives and delegates from some of the world's top technology companies.

Committee for ValleyML AI Expo 2020:

Program Chair for AI Enterprise and AI Robotics series:

Mr. Marc Mar-Yohana, Vice President at UL.

Program Chair for AI Hardware series:

Mr. George Williams, Director of Data Science at GSI Technology.

General Chair:

Dr. Kiran Gunnam, Distinguished Engineer, Machine Learning and Computer Vision, Western Digital.

View original content:http://www.prnewswire.com/news-releases/valleyml-is-launching-a-machine-learning-and-deep-learning-boot-camp-from-july-14th-to-sept-10th-and-ai-expo-series-from-sept-21st-to-nov-19th-2020-virtual-and-global-live-events-301059663.html

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ValleyML is launching a Machine Learning and Deep Learning Boot Camp from July 14th to Sept 10th and AI Expo Series from Sept 21st to Nov 19th 2020....

Author: Eric Walz

The emerging field of machine learning has important uses in a variety of fields, such as finance, healthcare and business. For self-driving cars, machine learning is an important tool that can be used to predict the behavior of other road users, such as other human drivers, pedestrians and bicyclists.

For Alphabet subsidiary Waymo, machine learning is one of its most important tools in its arsenal to build the world's best autonomous driving system it calls "Waymo Driver" that performs better than a human driver.

However, unlike a computer, human drivers have the ability to anticipate and predict what others on the road might do and can learn from past experiences, something that's difficult to train a computer to do and requires lots of processing power. So Waymo developed is own machine learning model that can do the same job and with less compute.

For example, when approaching an intersection a human driver might anticipate that another driver traveling in the opposite direction will make a left turn in their path or a pedestrian may enter the roadway. By anticipating this behavior the driver can mentally prepare to brake if needed. Predicting these types of behaviors for a computer is challenging for engineers working on self-driving vehicles.

That's where machine learning comes into play, allowing Waymo's autonomous vehicles to make better decisions. Machine learning is commonly used to model and reduce some of this complexity, thereby enabling the self-driving system to learn new types of behavior.

Waymo collects data from the real world from driving millions of miles and billions more miles in computer simulation built from data collected from its fleet. To navigate, Waymo's autonomous vehicles rely on highly complex, high-definition maps and vehicle sensor data. However, this data alone is not enough to make predictions, according to Waymo.

Simplifying a Complex Scene

The behavior of other road users is often complicated and difficult to capture with just map-derived traffic rules because driving patterns vary and human drivers often break the rules they're supposed to follow.

The most popular way to incorporate highly detailed, centimeter-level maps into behavior prediction models is by rendering the map into pixels and encoding all of the scene information, such as traffic signs, crosswalks, road lanes, and road boundaries, with a convolutional neural network (CNN).

However, this method requires a tremendous amount of processor power and takes time (latency), which is not ideal for a self-driving vehicle that needs to make decisions in a fraction of a second.

To address these issues and make better predictions Waymo developed a new model it calls "VectorNet", that provides more accurate behavior predictions while using less compute than CNNs, the company says. VectorNet essentially takes a highly complex scene and simplifies it using vectors so it can be processed with less computing power.

Map features can be simplified into vectors, which are easier for machine learning models to process.

This complex scene can be broken down into vectors to make is easier to process.

For example, an intersection crosswalk can be represented as a polygon defined by several points and a stop sign can be represented by a single point. Road curves can be approximately represented as polylines by "connected the dots." These polylines are then further split into vector fragments.

In this way, Waymo's engineers are able to represent all the road features and the trajectories of the other objects as a set of simplified vectors instead of a highly complex scene, which is much more difficult to work with. With this simplified view, Waymo designed VectorNet to effectively process its vehicle sensor data and map inputs.

The neural network is implemented to capture the relationships between various vectors. These relationships occur when, for example, a car enters an intersection or a pedestrian approaches a crosswalk. Through learning such interactions between road features and object trajectories, VectorNet's data-driven, machine learning-based approach allows Waymo to better predict other agents' behavior by learning from different behavior patterns.

Waymo proposed a novel hierarchical graph neural network. The first level is composed of polyline subgraphs. Then VectorNet gathers information within each polyline. In the second level called "global interaction graph", VectorNet exchanges information among polylines.

Here is the simplfied intersection with the input vectors that are converted to polyline subgraphs.

To further boost VectorNet's capabilities and understanding of the real world, Waymo trained the system to learn from context clues to make inferences about what could happen next around the vehicle to make improved behavior predictions.

For example, important scene information can often be obscured while driving, such as a tree branch blocking a stop sign. When this happens to a human driver, they can draw upon past experiences about the possibility of the stop sign being there, although they cannot see it. Machine learning makes these types of predictions using inference.

To further improve the accuracy of VectorNet, Waymo randomly masks out map features during training, such as a stop sign at a four-way intersection and requiring the CNN to complete it.

In this way, VectorNet can further improve the Waymo Driver's understanding of the world around it and be better prepared for any unexpected situations.

The intersection is broken down to create a global interaction graph.

Waymo validated the performance of VectorNet with the task of trajectory prediction, an important task for a self-driving vehicle that interacts with human drivers on the road. Compared with ResNet-18, one of the most advanced and widely used CNNs, VectorNet achieves up to 18% better performance while using only 29% of the parameters and consuming just 20% of the computation when there are 50 agents (other vehicles, pedestrians) per scene, Waymo reported.

Also this week, Waymo announced its latest funding round of $750 million. With the new funding, Waymo has raised $3 billion since March. The company is working on self-driving cars, commercial robotaxis and self-driving trucks that will all be powered by its advanced AI software.

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Waymo Develops a Machine Learning Model to Predict the Behavior of Other Road Users for its Self-Driving Vehicles - FutureCar

From navigating to a new place to picking out new music, algorithms have laid the foundation for large parts of modern life. Similarly, artificial Intelligence is booming because it automates and backs so many products and applications. Recently, I addressed some analytical applications for TensorFlow. In this article, Im going to lay out a higher-level view of Googles TensorFlow deep learning framework, with the ultimate goal of helping you to understand and build deep learning algorithms from scratch.

Over the past couple of decades, deep learning has evolved rapidly, leading to massive disruption in a range of industries and organizations. The term was coined in 1943 when Warren McCulloch and Walter Pitts created a computer model based on neural networks of a human brain, creating the first artificial neural networks (or ANNs). Deep learning now denotes a branch of machine learning that deploys data-centric algorithms in real-time.

Backpropagation is a popular algorithm that has had a huge impact in the field of deep learning. It allows ANNs to learn by themselves based on the errors they generate while learning. To further enhance the scope of an ANN, architectures like Convolutional Neural Networks, Recurrent Neural Networks, and Generative Networks have come into the picture. Before we delve into them, lets first understand the basic components of a neural network.

Neurons and Artificial Neural Networks

An artificial neural network is a representational framework that extracts features from the data its given. The basic computational unit of an ANN is the neuron. Neurons are connected using artificial layers through which the information passes. As the information flows through these layers, the neural network identifies patterns between the data. This type of processing makes ANNs useful for several applications, such as for prediction and classification.

Now lets take a look at the basic structure of an ANN. It consists of three layers: the input layer, the output layer, which is always fixed or constant, and the hidden layer. Inputs initially pass through an input layer. This layer always accepts a constant set of dimensions. For instance, if we wanted to train a classifier that differentiates between dogs and cats, the inputs (in this case, images) should be of the same size. The input then passes through the hidden layers and the network updates the weights and recognizes the patterns. In the final step, we classify the data at the output layer.

Weights and Biases

Every neuron inside a neural network is associated with parameters, weight and bias. The weight is an integer that controls the signals between any two neurons. If the output is desirable, meaning that the output is in proximity to the one that we expected it to produce, then the weights are ideal. If the same network is generating an erroneous output thats far away from the actual one, then the network alters the weights to improve the subsequent results.

Bias, the other parameter, is the algorithms tendency to consistently learn the wrong thing by not taking into account all the information in the data. For the model to be accurate, bias needs to be low. If there are inconsistencies in the dataset, like missing values, fewer data tuples, or erroneous input data, the bias would be high and the predicted values could be wrong.

Working of a Neural Network

Before we get started with TensorFlow, lets examine how a neural network produces an output with weights, biases, and input by taking a look at the first neural network, called Perceptron, which dates back to 1958. The Perceptron network is a simple binary classifier. Understanding how this works will allow us to comprehend the workings of a modern neuron.

The Perceptron network is a supervised machine learning technique that uses a binary classifier function by mapping a vector of binary variables to a single binary output. It works as follows:

Multiply the inputs (x1, x2, x3) of the network to their corresponding weights (w1, w2, w3).

Add the multiplied weights and inputs together. This is called the weighted sum, denoted by, x1*w1 + x2*w2 +x3*w3

Apply the activation function. Determine whether the weighted sum is greater than a threshold (say, 0.5), if yes, assign 1 as the output, otherwise assign 0. This is a simple step function.

Of course, Perceptron is a simple neural network that doesnt wholly consider all the concepts necessary for an end-to-end neural network. Therefore, lets go over all the phases that a neural network has to go through to build a sophisticated ANN.

Input

A neural network has to be defined with the number of input dimensions, output features, and hidden units. All these metrics fall in a common basket called hyperparameters. Hyperparameters are numeric values that determine and define the neural network structure.

Weights and biases are set randomly for all neurons in the hidden layers.

Feed Forward

The data is sent into the input and hidden layers, where the weights get updated for every iteration. This creates a function that maps the input with the output data. Mathematically, it is defined asy=f(x), where y is the output, x is the input, and f is the activation function.

For every forward pass (when the data travels from the input to the output layer), the loss is calculated (actual value minus predicted value). The loss is again sent back (backpropagation) and the network is retrained using a loss function.

Output error

The loss is gradually reduced using gradient descent and loss function.

The gradient descent can be calculated with respect to any weight and bias.

Backpropagation

We backpropagate the error that traverses through each and every layer using the backpropagation algorithm.

Output

By minimizing the loss, the network re-updates the weights for every iteration (One Forward Pass plus One Backward Pass) and increases its accuracy.

As we havent yet talked about what an activation function is, Ill expand that a bit in the next section.

Activation Functions

An activation function is a core component of any neural network. It learns a non-linear, complex functional mapping between the input and the response variables or output. Its main purpose is to convert an input signal of a node in an ANN to an output signal. That output signal is the input to the subsequent layer in the stack. There are several types of activation functions available that could be used for different use cases. You can find a list comprising the most popular activation functions along with their respective mathematical formulae here.

Now that we understand what a feed forward pass looks like, lets also explore the backward propagation of errors.

Loss Function and Backpropagation

During training of a neural network, there are too many unknowns to be deciphered. As a result, calculating the ideal weights for all the nodes in a neural network is difficult. Therefore, we use an optimization function through which we could navigate the space of possible ideal weights to make good predictions with a trained neural network.

We use a gradient descent optimization algorithm wherein the weights are updated using the backpropagation of error. The term gradient in gradient descent refers to an error gradient, where the model with a given set of weights is used to make predictions and the error for those predictions is calculated. The gradient descent optimization algorithm is used to calculate the partial derivatives of the loss function (errors) with respect to any weight w and bias b. In practice, this means that the error vectors would be calculated commencing from the final layer, and then moving towards the input layer by updating the weights and biases, i.e., backpropagation. This is based on differentiations of the respective error terms along each layer. To make our lives easier, however, these loss functions and backpropagation algorithms are readily available in neural network frameworks such as TensorFlow and PyTorch.

Moreover, a hyperparameter called learning rate controls the rate of adjustment of weights of a network with respect to the gradient descent. The lower the learning rate, the slower we travel down the slope (to reach the optimum, or so-called ideal case) while calculating the loss.

TensorFlow is a powerful neural network framework that can be used to deploy high-level machine learning models into production. It was open-sourced by Google in 2015. Since then, its popularity has increased, making it a common choice for building deep learning models. On October 1st, a new, stable version got released, called TensorFlow 2.0, with a few major changes:

Eager Execution by Default - Instead of creating tf.session(), we can directly execute the code as usual Python code. In TensorFlow 1.x, we had to create a TensorFlow graph before computing any operation. In TensorFlow 2.0, however, we can build neural networks on the fly.

Keras Included - Keras is a high-level neural network built on top of TensorFlow. It is now integrated into TensorFlow 2.0 and we can directly import Keras as tf.keras, and thereby define our neural network.

TF Datasets - A lot of new datasets have been added to work and play with in a new module called tf.data.

1.0 Support: All the existing TensorFlow 1.x code can be executed using TensorFlow 2.0; we need not modify any of our previous code.

Major Documentation and API cleanup changes have also been introduced.

The TensorFlow library was built based on computational graphs a runtime for executing such computational graphs. Now, lets perform a simple operation in TensorFlow.

Here, we declared two variables a and b. We calculated the product of those two variables using a multiplication operation in Python (*) and stored the result in a variable called prod. Next, we calculated the sum of a and b and stored them in a variable named sum. Lastly, we declared the result variable that would divide the product by the sum and then would print it.

This explanation is just a Pythonic way of understanding the operation. In TensorFlow, each operation is considered as a computational graph. This is a more abstract way of describing a computer program and its computations. It helps in understanding the primitive operations and the order in which they are executed. In this case, we first multiply a and b, and only when this expression is evaluated, we take their sum. Later, we take prod and sum, and divide them to output the result.

TensorFlow Basics

To get started with TensorFlow, we should be aware of a few essentials related to computational graphs. Lets discuss them in brief:

Variables and Placeholders: TensorFlow uses the usual variables, which can be updated at any point of time, except that these need to be initialized before the graph is executed. Placeholders, on the other hand, are used to feed data into the graph from outside. Unlike variables, they dont need to be initialized.Consider a Regression equation, y = mx+c, where x and y are placeholders, and m and c are variables.

Constants and Operations: Constants are the numbers that cannot be updated. Operations represent nodes in the graph that perform computations on data.

Graph is the backbone that connects all the variables, placeholders, constants, and operators.

Prior to installing TensorFlow 2.0, its essential that you have Python on your machine. Lets look at its installation procedure.

Python for Windows

You can download it here.

Click on the Latest Python 3 release - Python x.x.x. Select the option that suits your system (32-bit - Windows x86 executable installer, or 64-bit - Windows x86-64 executable installer). After downloading the installer, follow the instructions that are displayed on the setup wizard. Make sure to add Python to your PATH using environment variables.

Python for OSX

You can download it here.

Click on the Latest Python 3 release - Python x.x.x. Select macOS 64-bit installer,and run the file.

Python on OSX can also be installed using Homebrew (package manager).

To do so, type the following commands:

Python for Debian/Ubuntu

Invoke the following commands:

This installs the latest version of Python and pip in your system.

Python for Fedora

Invoke the following commands:

This installs the latest version of Python and pip in your system.

After youve got Python, its time to install TensorFlow in your workspace.

To fetch the latest version, pip3 needs to be updated. To do so, type the command

Now, install TensorFlow 2.0.

This automatically installs the latest version of TensorFlow onto your system. The same command is also applicable to update the older version of TensorFlow.

The argument tensorflow in the above command could be any of these:

tensorflow Latest stable release (2.x) for CPU-only.

tensorflow-gpu Latest stable release with GPU support (Ubuntu and Windows).

tf-nightly Preview build (unstable). Ubuntu and Windows include GPU support.

tensorflow==1.15 The final version of TensorFlow 1.x.

To verify your install, execute the code:

Now that you have TensorFlow on your local machine, Jupyter notebooks are a handy tool for setting up the coding space. Execute the following command to install Jupyter on your system:

Now that everything is set up, lets explore the basic fundamentals of TensorFlow.

Tensors have previously been used largely in math and physics. In math, a tensor is an algebraic object that obeys certain transformation rules. It defines a mapping between objects and is similar to a matrix, although a tensor has no specific limit to its possible number of indices. In physics, a tensor has the same definition as in math, and is used to formulate and solve problems in areas like fluid mechanics and elasticity.

Although tensors were not deeply used in computer science, after the machine learning and deep learning boom, they have become heavily involved in solving data crunching problems.

Scalars

The simplest tensor is a scalar, which is a single number and is denoted as a rank-0 tensor or a 0th order tensor. A scalar has magnitude but no direction.

Vectors

A vector is an array of numbers and is denoted as a rank-1 tensor or a 1st order tensor. Vectors can be represented as either column vectors or row vectors.

A vector has both magnitude and direction. Each value in the vector gives the coordinate along a different axis, thus establishing direction. It can be depicted as an arrow; the length of the arrow represents the magnitude, and the orientation represents the direction.

Matrices

A matrix is a 2D array of numbers where each element is identified by a set of two numbers, row and column. A matrix is denoted as a rank-2 tensor or a 2nd order tensor. In simple terms, a matrix is a table of numbers.

Tensors

A tensor is a multi-dimensional array with any number of indices. Imagine a 3D array of numbers, where the data is arranged as a cube: thats a tensor. When its an nD array of numbers, that's a tensor as well. Tensors are usually used to represent complex data. When the data has many dimensions (>=3), a tensor is helpful in organizing it neatly. After initializing, a tensor of any number of dimensions can be processed to generate the desired outcomes.

TensorFlow represents tensors with ease using simple functionalities defined by the framework. Further, the mathematical operations that are usually carried out with numbers are implemented using the functions defined by TensorFlow.

Firstly, lets import TensorFlow into our workspace. To do so, invoke the following command:

This enables us to use the variable tf thereafter.

Now, lets take a quick overview of the basic operations and math, and you can simultaneously execute the code in the Jupyter playground for a better understanding of the concepts.

tf.Tensor

The primary object in TensorFlow that you play with is tf.Tensor. This is a tensor object that is associated with a value. It has two properties bound to it: data type and shape. The data type defines the type and size of data that will be consumed by a tensor. Possible types include float32, int32, string, et cetera. Shape defines the number of dimensions.

tf.Variable()

The variable constructor requires an argument which could be a tensor of any shape and type. After creating the instance, this variable is added to the TensorFlow graph and can be modified using any of the assign methods. It is declared as follows:

Output:

tf.constant()

The tensor is populated with a value, dtype, and, optionally, a shape. This value remains constant and cannot be modified further.

See the rest here:
A Lightning-Fast Introduction to Deep Learning and TensorFlow 2.0 - Built In