What is Deep Learning?

February 1, 2021

In simple terms, Deep learning is a subset of artificial intelligence, focusing on making robots learn what humans naturally do - learn by experiences. In deep learning, machines learn-to-learn with the help of data sets. Deep learning algorithms use artificial neural networks to analyze data as the human brain does independently. Of course, the data training, humongous knowledge base, and pattern recognition techniques are fed to the machine by humans to work on their own later.

Some examples of using deep learning to replace manual work are, Voice commands to phones or laptops, Driverless cars, Face recognition, Text translations, and Sentiment analysis.

Why Deep Learning?

Now that we know the meaning of deep learning, the question arises - why would we want machines to behave like humans? Experts have given several answers to this question, and out of those some are: “for diminishing mundane, repetitive work”, “ To fasten the work speed”, “To achieve accurate results in strict timelines” But the most important reason for exploring branches of advanced concepts of deep learning is “Accuracy.” Deep learning has improved the level of accuracy many times. Multiple tasks like car driving, printing, text recognition, and natural language processing are done more accurately than previously with deep learning. Deep learning has outperformed human minds in computer vision consisting of classifying objects in any image.

Although the term “Deep learning” was introduced by distinguished professor Rina Dechter in 1986 but became a shining term recently due to accelerating demand for less time consuming and accuracy driven services and products, to achieve these demands in a competitive market by businesses, deep learning acted as the magic tool. It became useful by giving solutions when:

  1. Solutions required a large number of data sets, majorly labeled data. For example, To develop driverless cars, the development team would require to process millions of pictures and gigabytes of videos parallel.
  2. Solutions required high throughput or computation power. High-performing deep learning GPUs are connected in parallel for deep learning algorithms as it requires a high amount of data to be processed in less time. These deep learning models are connected with cloud computing clusters to enable developers to process data quicker. This reduces training time for machines with loads of data transferred into their knowledge base. Even though high throughput machines are used for processing, it may take weeks to train a machine due to its complexity.

How Does Deep Learning Work?

Deep learning algorithms, also known as “deep neural networks” use a neural networks to function. Neurons in the neural networks work on similar lines with neurons in the human brain. Neural networks architecture consists of nodes arranged in a layered fashion. More the layers, the more precise and elaborated your model would behave. Deep neural networks contain a very high number of layers which can go up to 150 or more.

In the networks, the node sends out the signal from one node to another and assigns weights to it. The nodes with heavier weight have a greater impact on associated layers. The layers are arranged in a sequence. The final layer compiles weighted inputs and generates output.

To understand the working of deep learning, let us take an example.

Problem statement: Deep learning algorithm receives a Cat’s image as an input and outputs “yes” if there is Cat in that image otherwise “no”.

Solution by deep learning algorithm:

Not all cats look alike. Their different colors, sizes, angles of pictures, light density, image quality, and object shadows add to the complexity of determining cats from the image. Hence, the training set should include multiple images covering the cat’s maximum determining characteristics in an image. Many examples of cat images must be included, which could be considered “cat” by humans and also images that can not be categorized as “cat” images should be included. These example images are fed in the database of neural networks and stored in data sets. This data is mapped into the neural networks; nodes assign weightage to each data element. Output compiles all the disconnected information to conclude. If the algorithm finds out that the object in an image is furry, has four legs, has a tail, then it should be “cat”. There are hundreds of characteristics like this which are particular to cats defined in trained data sets to distinguish them from other objects.

The answer received after all the analysis mentioned in the above paragraph is then compared with the human-generated answer. If these two answers match, then the output is validated and saved as a success case. In case of the answers mismatch, the error is recorded, and weights are changed. This process is repeated several times, and weights are adjusted multiple times until we attain high accuracy. This type of training is known as “supervised learning”. The machine is trained until a point is reached where machines can self learn with previous examples.

Challenges in the future of Deep Learning

  • Massive datasets: Massive amount of datasets is a challenge in deep learning with increasing data day by day. Here we are talking about training data. Humongous data is scattered in the market with no detectable pattern as an input data set, but the same data set should be arranged while used for training purposes. It becomes a tough task to find different types of example datasets to maximize example training data coverage. New concepts of generative adversarial learning and transfer learning are used to overcome this challenge.
  • Overfitting: “The greatest danger in art is too much knowledge”. Overfitting is a common problem in deep learning when data analysis focuses too closely on the dataset. In this situation, even the mundane and non-important parameters are recorded, which results in skewed results. Due to the highly receptive nature of neural networks, accurate results depend upon correct characteristic determination. Similarly, the chances of deviating from results are also there if the algorithm focuses on the wrong characteristics. But this issue can be overcome by adhering to the right data sets. One of the famous research papers “Dropout: a simple way to prevent neural networks from overfitting” is a great knowledge source for researchers and scientists to reduce overfitting related errors.
  • Privacy breach: It is noted that data privacy has gained notion in recent years. Recently, the Federal trade commission (FTC) snapped a $5 billion penalty on Facebook for mishandling the data and privacy of application users. Deep learning is a data-dependent technique where data is recorded on a wide-scale to achieve accuracy in results. But due to several privacy laws and restrictions, it has become challenging to gain access to critical data which prevents deep learning from reaching accurate results.
  • Butterfly effect: Deep learning is vulnerable to produce outrightly inaccurate results even if there is the slightest change in the input data. It makes any algorithm unstable and unreliable for mission-critical or decision-making applications. The instances have been recorded where hackers can add the unnoticeable amount of “noise” in the data set to completely corrupt the result.

How is Deep Learning Beneficial in the Future?

  • Self-learning or ubiquitous deep learning: As of now, most robots still require human assistance to learn new situations and reactions. But deep learning can help in designing models via which robots can learn themselves. This would help various businesses that are not experts in AI but still can take advantage of self-learning bots to reduce the number of human errors and increase the speed of transactions.
  • Deep learning for cybersecurity: Security incidents have also risen parallelly with advancements in technology. The list of attacks is pretty long with some famous ones like WannaCry, Capitol One breach, NotPetya. It has become a necessity for businesses to act fast and proactive to prevent losses from these. More Cyberdefense agencies would subscribe to deep machine learning algorithms to respond faster than humans and detect patches in IT Infrastructure to Reduce the Impact of attacks.
  • Automation of repetitive tasks: When did you visit the car garage last time? Did you try to observe several mundane tasks that could have been automated? Deep-learning robots mounted with deep learning abilities can complete tasks with input received from different AI sources and sensors. Like the way humans act based on the environment and experiences, robots can also act according to data sets containing previous examples and input data from sensors.
  • Machine vision: Deep learning has brought revolutionary changes in the way images are perceived and classified. Different actions like object detection in images, image restoration or recreation, image classification, and even detention of messages from the handwritten text can be performed using deep learning. This functionality, driven by deep learning provides an analytical vision to machines, which helps get into details that would have been upheaval tasks for humans.
  • Deep learning to enhance customer experience: Deep learning is used to create a useful application to improve your business experience. One common example which you find in almost all consumer-centric websites is Chatbot. Chatbots uses deep learning to analyze customer text and generates responses accordingly. Other examples are image captions (The complexity of image captions reduces if you cannot identify one after each attempt).
  • Deep learning in marketing: The use of websites for commercial purposes has gained traction in the COVID-19 era. Consumers are becoming smarter day by day by ordering their required products online with a click’s comfort. Similarly, businesses are also becoming smart by subscribing to smart marketing with the help of deep learning. Deep learning is outperforming humans in SEO. The real-time web content generators are used to tweak content and optimize the website for SEO. This helps websites improve their SEO ranking without the interference of human SEOs. Google has pioneered digital marketing with the help of the best GPUs for deep learning.

Deep Learning has a Bright Future!

It is predicted that deep learning would become widespread and embedded in the system to achieve faster and accurate outputs. GPU Cloud instances offered by E2E makes it easy and affordable to build and deploy deep learning systems.

As per the article by Big Data Evangelist James Kobielus in “6 Predictions for the future of deep learning”: The deep learning industry will adopt a core set of standard tools, and Deep learning will gain native support within various platforms like a spark, open analytics ecosystem with reusable components and framework libraries. The same has been indicated by “Ray Kurzweil”. He became famous for his prediction that Artificial Intelligence would outsmart humans in computational capabilities by 2029.

In a nutshell,

Deep learning models are expected to exponentially grow in the future to create innovative applications freeing up human brains from manual repetitive tasks. A few trends which are observed about the future of deep learning are:

  1. Support and growth of commercial activities over the networks. NLP and digital marketing have increased the use of deep learning algorithms and gained valuable attention from consumers.
  2. An urge to automate repetitive tasks requiring more physical labor than mental involvement will encourage data scientists and engineers to innovate in AI continuously.
  3. A tussle between data protection organizations and deep learning research agencies will prevail in the future too.
  4. The limitation of deep learning is the “ability to reason” is a bottleneck to create independent decision-making machines.

E2E Networks hopes that this article has shed light on the bright future of deep learning. For more blogs, check out the E2E Networks website.

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This is a decorative image for Clustering in deep learning- A acknowledged tool.
June 27, 2022

Clustering in deep learning- A acknowledged tool

When learning something new about anything, such as music, one strategy may be to seek relevant groupings or collections. You may organize your music by genre, but your friend may organize it by the singer. The way you combine items allows you to learn more about them as distinct pieces of music, somewhat similar to what clustering algorithms do. 

Let’s discuss a detailed brief on Clustering algorithms, their applications, and how GPUs can be used to accelerate the potential of clustering models. 

Table of content-

  1. What is Clustering?
  2. How to do Clustering?
  3. Methods of Clustering. 
  4. DNN in Clustering. 
  5. Accelerating analysis with clustering and GPU. 
  6. Applications of Clustering.
  7. Conclusion. 

What is Clustering?

In machine learning, we typically group instances as a first step in interpreting a data set in a machine learning system. The technique of grouping unlabeled occurrences is known as clustering. Clustering is based on unsupervised machine learning since the samples are unlabeled. When the instances are tagged, clustering transforms into classification.

Clustering divides a set of data points or populations into groups so that data points in the same group are more similar to one another and dissimilar from data points in other groups. It is simply a collection of elements classified according to their similarity and dissimilarity.

How to do Clustering?

Clustering is critical because it determines the intrinsic grouping of the unlabeled data provided. There are no requirements for good clustering. It is up to the user to decide which criteria will be used to satisfy their demands. For example, we might be interested in locating representatives for homogenous groups (data reduction), locating "natural clusters" and defining their unknown qualities ("natural" data types), locating useful and appropriate groupings ("useful" data classes), or locating odd data items (outlier detection). This method must make assumptions about point resemblance, and each assumption results in a unique but equally acceptable cluster.

Methods of Clustering: 

  1. Density-Based Approaches: These methods assume clusters to be the dense region of the space, with some similarities and differences to the lower dense region. These algorithms have high accuracy and can combine two clusters. DBSCAN (Density-Based Spatial Clustering of Applications with Noise), OPTICS (Ordering Points to Identify Clustering Structure), and other algorithms are examples. 

  1. Methods Based on Hierarchy: The clusters created in this approach form a tree-like structure based on the hierarchy. The previously established cluster is used to generate new clusters. It is classified into two types. aggregative (bottom-up approach) dividing (top-down approach)

  1. Partitioning Methods: These methods divide the items into “k” clusters, with each split becoming a separate cluster. This approach is used to optimize an objective criterion similarity function, such as K-means, CLARANS (Clustering Large Applications Based on Randomized Search), and so on. 

  1. Grid-based Methods: The data space is divided into a finite number of cells that create a grid-like structure in this approach. STING (Statistical Information Grid), wave cluster, CLIQUE (Clustering In Quest), and other clustering processes performed on these grids are rapid and independent of the number of data items.

DNN in clustering-

In Deep Learning, DNNs serve as mappings to better representations for clustering. The properties of these representations might be drawn from different layers of the network, or even from many. This choice may be divided into two categories:

  • One layer: Refers to the general scenario in which just the output of the network's last layer is used. This method makes use of the representation's low dimensionality. 

  • Several layers: This representation is a composite of the outputs of several layers. As a result, the representation is more detailed and allows for embedded space to convey more sophisticated semantic representations, potentially improving the separation process and aiding in the computation of similarity.

Accelerating analysis with clustering and GPU-

Clustering is essential in a wide range of applications and analyses, but it is now facing a computational problem as data volumes continue to grow. One of the most promising options for tackling the computational barrier is parallel computing using GPUs. Because of their huge parallelism and memory access-bandwidth benefits, GPUs are an excellent approach to speed data-intensive analytics, particularly graph analytics. The massively parallel architecture of a GPU, which consists of thousands of tiny cores built to handle numerous tasks concurrently, is ideally suited for the computing job. This may be used for groups of vertices or edges in a big graph.

In an analysis using data, clustering is a task with high parallelism that can be expedited using GPUs. In the future, GPUs will include spectral and hierarchical clustering/partitioning approaches based on the minimal balanced cut metric. 

Applications of Clustering-

The clustering approach may be applied to a wide range of areas. The following are some of the most popular applications of this technique: 

  • Segmentation of the Market: Cluster analysis, in the context of market segmentation, is the application of a mathematical model to uncover groups of similar consumers based on the smallest variances among customers within each group. In market segmentation, the purpose of cluster analysis is to precisely categorize customers in order to create more successful customer marketing through personalization. 

  • Recommendation engine: Clustering may be used to solve a number of well-known difficulties in recommendation systems, such as boosting the variety, consistency, and reliability of suggestions; the data sparsity of user-preference matrices; and changes in user preferences over time.

  • Analysis of social networks: Clustering in social network analysis is not the same as traditional clustering. It necessitates classifying items based on their relationships as well as their properties. Traditional clustering algorithms group items only on their similarity and cannot be used for social network research. A social network clustering analysis technique, unlike typical clustering algorithms, can classify items in a social network based on their linkages and detect relationships between classes. 

  • Segmentation of images: Segmentation of images using clustering algorithms is a method for doing pixel-wise image segmentation. The clustering algorithm here aims to cluster the pixels that are close together in this form of segmentation. There are two ways to conduct segmentation via clustering - Merging Clustering and Divisive Clustering

  • Detecting Anomaly: Clustering may be used to train the normalcy model by grouping comparable data points together into clusters using a distance function. Clustering is appropriate for anomaly detection since no knowledge of the attack classes is required during training. Outliers in a dataset can be found using clustering and related approaches.


Clustering is an excellent method for learning new things from old data. Sometimes the resultant clusters will surprise you, and it may help you make sense of an issue. One of the most interesting aspects of employing clustering for unsupervised learning is that the findings may be used in a supervised learning issue. 

Clusters might be the new features that you employ on a different data set! Clustering may be used on almost every unsupervised machine learning issue, but make sure you understand how to examine the results for accuracy.

Clustering is also simple to apply; however, several essential considerations must be made, such as dealing with outliers in your data and ensuring that each cluster has a sufficient population.

This is a decorative image for How GPUs are affecting Deep Learning inference?
June 27, 2022

How GPUs are affecting Deep Learning inference?

The training step of most deep learning systems is the most time-consuming and resource-intensive. This phase may be completed in a fair period of time for models with fewer parameters, but as the number of parameters rises, so does the training time. This has a two-fold cost: your resources will be engaged for longer, and your staff will be left waiting, squandering time. 

We'll go through how GPUs manage such issues and increase the performance of deep learning inferences like multiclass classification and other inferences. 

Table of Content:

  1. Graphical Processing Unit (GPU)
  2. Why GPUs?
  3. How GPUs improved the performance of Deep Learning Inferences?
  4. Critical Decision Criteria for Inference 
  5. Which hardware should you use for DL inferences? 
  6. Conclusion

Graphical Processing Units (GPU)

A graphics processing unit (GPU) is a specialized hardware component capable of performing many fundamental tasks at once. GPUs were created to accelerate graphics rendering for real-time computer graphics, especially gaming applications. The general structure of the GPU is similar to that of the CPU; both are spatial architectures. Unlike CPUs, which have a few ALUs optimized for sequential serial processing, the GPU contains thousands of ALUs that can do a huge number of fundamental operations at the same time. Because of this exceptional feature, GPUs are a strong competitor for deep learning execution.

Why GPUs?

Graphics processing units (GPUs) can help you save time on model training by allowing you to execute models with a large number of parameters rapidly and efficiently. This is because GPUs allow you to parallelize your training activities, divide them across many processor clusters, and perform multiple computing operations at the same time.

GPUs are also tuned to execute certain jobs, allowing them to complete calculations quicker than non-specialized technology. These processors allow you to complete jobs faster while freeing up your CPUs for other duties. As a result, bottlenecks caused by computational restrictions are no longer an issue.

GPUs are capable of doing several calculations at the same time. This allows training procedures to be distributed and can considerably speed up deep learning operations. You can have a lot of cores with GPUs and consume fewer resources without compromising efficiency or power. The decision to integrate GPUs in your deep learning architecture is based on various factors: Memory bandwidth—GPUs, for example, can offer the necessary bandwidth to support big datasets. This is due to the fact that GPUs have specialized video RAM (VRAM), which allows you to save CPU memory for other operations. Dataset size—GPUs can scale more readily than CPUs, allowing you to analyze large datasets more quickly. The more data you have, the more advantage you may get from GPUs. Optimization—one disadvantage of GPUs is that it might be more difficult to optimize long-running individual activities than it is with CPUs.

How GPUs improved the performance of Deep Learning Inferences?

Multiple matrix multiplications make up the computational costly element of the neural network. So, what can we do to make things go faster? We may easily do this by performing all of the processes at the same time rather than one after the other. In a nutshell, this is why, when training a neural network, we utilize GPUs (graphics processing units) rather than CPUs (central processing units). 

Critical Decision Criteria for Inference-


The speed, efficiency, and accuracy of these projections are some of the most important decision factors in this phase of development. If a model can't analyze data quickly enough, it becomes a theoretical exercise that can't be used in practice. It becomes too expensive to run in manufacturing if it consumes too much energy. Finally, if the model's accuracy is inadequate, a data science team will be unable to justify its continuous usage. Inference speed, in particular, can be a bottleneck in some scenarios and instances, such as Image Classification, which is utilized in a variety of applications such as social media and image search engines. Even though the tasks are basic, timeliness is crucial, especially when it comes to public safety or platform infractions. 

Self-driving vehicles, commerce site suggestions, and real-time internet traffic routing are all instances of edge computing or real-time computing. Object recognition inside 24x7 video feeds, as well as large volumes of images and videos. Pathology and medical imaging are examples of complex images or tasks. These are some of the most difficult photos to decipher. To achieve incremental speed or accuracy benefits from a GPU, data scientists must now partition pictures into smaller tiles. These cases necessitate a decrease in inference speed while also increasing accuracy. Because inference is often not as resource-intensive as training, many data scientists working in these contexts may start with CPUs. Some may resort to leveraging GPUs or other special hardware to obtain the performance or accuracy enhancements they seek as inference speed becomes a bottleneck.

Which hardware should you use for DL inferences? 

There are several online recommendations on how to select DL hardware for training, however, there are fewer on which gear to select for inference. In terms of hardware, inference and training may be very distinct jobs. When faced with the decision of which hardware to use for inference, you should consider the following factors: How critical is it that my inference performance (latency/throughput) be good? Is it more important for me to maximize latency or throughput? Is the typical batch size for my company modest or large? How much of a financial sacrifice am I ready to make in exchange for better results? Which network am I connected to? 

You know how we choose inference hardware? We start by assessing throughput performance. The V100 clearly outperforms the competition in terms of throughput, especially when employing a big batch size (8 images in this case). Furthermore, because the YOLO model has a significant parallelization potential, the CPU outperforms the GPU in this metric.


We looked at the various hardware and software techniques that have been utilized to speed up deep learning inference. We began by explaining what GPUs are, why they are needed, how GPUs increased the performance of Deep Learning Inferences, the essential choice criteria for the deep learning model and the hardware that should be employed. 

There is little question that the area of deep learning hardware will grow in the future years, particularly when it comes to specialized AI processors or GPUs. 

How do you feel about it? 

This is a decorative image for Understanding PyTorch
June 27, 2022

Understanding PyTorch

Every now and then, a library or framework emerges that completely changes the way we think about deep learning and aids in the advancement of deep learning studies by making them computationally quicker and less costly. Here we will be discussing one such library: PyTorch.


PyTorch is the library or framework for Python scripts that make deep learning projects easier to create. PyTorch's approachability and ease of use drew a large number of early adopters from the academic, research, and development communities. And it has developed into one of the most popular deep learning tools across a wide range of applications in the years after its first release.

PyTorch has two primary characteristics or features at its core: An n-dimensional Tensor that works similarly to NumPy but on GPUs and the other is the construction and training of neural networks using automatic differentiation. Apart from these primary features, PyTorch includes a number of other features, which are detailed below in this blog.

PyTorch Tensor-

Numpy is a fantastic framework, however, it is unable to use GPUs to speed up numerical operations. GPUs can frequently deliver speedups of 50x or more for contemporary deep neural networks and today's parallel computing methods may take advantage of GPUs much more. 

To train many models at once PyTorch offers distributed training, allowing academic practitioners and developers to parallelize their work. Using many GPUs to process bigger batches of input data the training of models can be made feasible with distributed training, as a result, the computation time is reduced.

The Tensor, the most fundamental PyTorch concept, is capable to do so. A PyTorch Tensor is basically the same as a NumPy array: a Tensor is an n-dimensional array, and PyTorch has several methods for working with them. Tensors may maintain track of a computational graph and gradients behind the scenes, but they can also be used as a general tool for scientific computing. PyTorch Tensors, unlike NumPy, may use GPUs to speed up their numeric operations. You just need to provide the suitable device to execute a PyTorch Tensor on GPU.

Automatic Differentiation-

Automatic differentiation is a method used by PyTorch to record all of our operations and then compute gradients by replaying them backward. Generally while training neural networks, developers have to manually implement both forward and backward passes. While manually implementing backward pass is easy but doing the same for forward pass might get a bit tricky or exhausting task. This is exactly what the autograd package in PyTorch does. 

When you use autograd, your network's forward pass will construct a computational graph, with nodes being Tensors and edges being functions that produce output Tensors from input Tensors. Because we calculate the gradients on the forward pass, this approach allows us to save time on each epoch.  You may also simply compute gradients by back propagating across this graph.

Flow control and weight sharing-


PyTorch implements a weird model as an example of dynamic graphs and weight sharing: a third-fifth order polynomial that selects a random integer between 3 and 5 and utilizes that many orders on each forward pass, recycling the same weights several times to calculate the fourth and fifth order. We can construct the loop in this model using standard Python flow control, and we can achieve weight sharing by simply repeating the same argument many times.


TorchScript allows you to turn PyTorch code into serializable and optimizable models. Any TorchScript application may be saved from a Python process and loaded into another process that doesn't require or doesn’t have a Python environment.

Pytorch has tools for converting a model from a pure Python program to a TorchScript program that can be executed in any standalone application such as of C++. This allows users to train models in PyTorch using familiar Python tools before exporting the model to a production environment where Python applications may be inefficient due to performance and multi-threading issues.

Dynamic Computation Graphs-

In frameworks like PyTorch, you usually have a set up of the computational network and a distinct execution mechanism than the host language. This unusual design is largely motivated by the need for efficiency and optimization. DL frameworks keep track of a computational graph that specifies the sequence in which calculations must be completed in a model. Researchers have found it difficult to test out more creative ideas because of this inconvenient arrangement.

There are two such types of computational graphs, one is static and the other is dynamic. Variable sizes must be established at the start with a static network i.e. when the graph is Static all the variables are to be created and connected in the beginning, and then later is settled up in a static (non-changing) session which might be inconvenient for some applications, such as NLP as for NLP Dynamic computational graphs are critical since language or input can arrive in a variety of expression lengths.

PyTorch, on the other hand, employs a dynamic graph. That is, the computational graph is constructed dynamically once variables are declared. As a result, after each training cycle, this graph is regenerated. Dynamic graphs are adaptable, allowing us to change and analyze the graph's internals at any moment. 

When all you had before were "goto" commands, introducing dynamic computational graphs is like introducing the idea of a process. We may write our programs in a composable manner thanks to the idea of the procedure. Of course, one may argue that DL designs do not require a stack. Recent research on Stretcher networks and Hyper networks, on the other hand, demonstrates this. Context switching, such as a stack, appears to be beneficial in some networks in studies.

nn Module

Autograd and computational graphs are a powerful paradigm for automatically generating sophisticated operators and computing derivatives; nevertheless, raw autograd may be too low-level for huge neural networks. We often consider stacking the computation when developing neural networks, with some layers containing learnable parameters that will be tweaked throughout the learning process. 

In such cases, we can make use of PyTorch’s nn module. The nn package defines modules, which are fundamentally equivalent to neural network layers. A Module can contain internal data such as Tensors with learnable parameters in addition to taking input Tensors and computing output Tensors.


In this blog, we understood how PyTorch is different from other libraries like NumPy, What are the special features that it offers including Tensor computing with substantial GPU acceleration and a tape-based autograd system used to build deep neural networks. 

We also studied other features like flow control and weight sharing, torch scripts, computation graphs, and nn module. 

This description was adequate to gain a general notion of what PyTorch is and how academicians, researchers, and developers may utilize it to construct better projects.

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