Understanding Deep Learning: How it Works and How to Get Started

June 23, 2023

Deep learning has emerged as a revolutionary area with significant relevance in several sectors in the dynamic technological world. This article intends to give developers, CTOs, and tech enthusiasts a thorough grasp of deep learning, its guiding principles, and step-by-step instructions for getting started.

What is Deep Learning?

Deep learning is a subset of artificial intelligence (AI) that focuses on training artificial neural networks to learn and make intelligent decisions. It involves the development of complex models that can automatically learn representations of data through a hierarchical structure of artificial neurons.

Deep learning models are inspired by the structure and function of the human brain. Neural networks, the fundamental building blocks of deep learning, mimic the interconnectedness of neurons and the way they process information. This biological inspiration allows deep learning models to excel in tasks such as image recognition, natural language processing, and more.

Deep learning models are composed of interconnected layers of artificial neurons. Each layer receives inputs, performs computations, and passes the results to the next layer. The depth of the network refers to the number of layers it contains. Deep neural networks with multiple layers have shown superior performance in capturing complex patterns and representations.

How Does Deep Learning Work?

Deep learning involves two primary processes: training and inference. During training, a deep learning model is exposed to labeled data, allowing it to learn patterns and correlations. Inference occurs when the trained model makes predictions or decisions on new, unseen data. Labeled training data plays a crucial role in deep learning. Supervised learning is a common approach, where the training data is labeled with the corresponding outputs. 

The model learns to associate inputs with the correct outputs, enabling it to make predictions on new, unlabeled data. Deep learning models are optimized through an iterative process known as backpropagation and gradient descent. Backpropagation calculates the gradient of the loss function with respect to each weight in the network, allowing for weight updates that minimize the loss. 

Gradient descent is the optimization algorithm that adjusts the weights based on the calculated gradients, gradually moving towards the optimal values.

Deep Learning Applications

Deep learning has demonstrated remarkable success in various real-world applications including Image recognition, Natural language processing (NLP), and Recommendation system. While deep learning has many advantages, it also has certain limitations. It can be difficult to meet the requirement for large amounts of labeled data, particularly in fields with few datasets already available. Deep learning models need significant hardware resources and are computationally demanding, which might be a hurdle for certain organizations.

Image Recognition and Computer Vision

Computer vision and image recognition applications have been revolutionized by deep learning. Convolutional neural networks (CNNs) are often employed in applications like object identification, picture classification, and autonomous cars because they are better at analyzing visual input.

Natural Language Processing (NLP) and Language Understanding

Deep learning has transformed the NLP, enabling machines to understand and generate human language. Recurrent neural networks (RNNs) and transformers have revolutionized tasks such as machine translation, sentiment analysis, and chatbots. Hence deep learning has vast applications in NLP.

Recommendation Systems and Personalization

Deep learning plays a crucial role in recommendation systems, enabling personalized recommendations based on user behavior and preferences. Collaborative filtering and deep neural networks power recommendation engines in various domains, including e-commerce, entertainment, and content platforms.

Healthcare and Medical Imaging

Deep learning has great potential in the healthcare domain including tumor identification, disease detection from medical images, and supporting precision medicine.

Finance and Fraud Detection

Deep learning is applied for financial services in fraud detection, risk assessment, and algorithmic trading. Deep neural networks analyze large amounts of financial data to identify fraudulent transactions, predict market trends, and optimize investment strategies.

Other Applications in Robotics, Manufacturing, and Genomics

Deep learning finds applications beyond traditional domains. It is used in robotics for perception, control, and manipulation tasks. In manufacturing, deep learning helps optimize processes, improve quality control, and enable predictive maintenance. Genomics benefits from deep learning techniques for DNA sequence analysis, gene expression prediction, and drug discovery.

Getting Started with Deep Learning

It is necessary to learn machine learning fundamentals before learning deep learning. It is necessary to learn mathematical concepts since that would allow you to understand the logic behind the program better. Python programming knowledge is recommended for the implementation of deep learning algorithms.

Deep learning frameworks simplify the implementation and deployment of deep learning models. TensorFlow and PyTorch are widely adopted frameworks known for their flexibility, scalability, and rich ecosystem of tools. They provide high-level abstractions and efficient computation on both CPUs and GPUs.

Numerous resources are available for learning deep learning concepts and techniques. Online courses, tutorials, and books offer structured learning paths for beginners and advanced practitioners. Platforms like Coursera, Udacity, and Fast.ai provide specialized deep learning courses from esteemed instructors. Community forums and open-source repositories offer a wealth of code examples, projects, and discussions to deepen your understanding.

Challenges and Future Directions

Despite its accomplishments, deep learning faces several challenges. Gathering labeled training data can be time-consuming and expensive. The quest for interpretability in deep learning models remains an ongoing area of research, as complex neural networks often lack transparency. As the field progresses, emerging trends like transfer learning, which enables leveraging pre-trained models for new tasks, and Explainable AI, which aims to provide insights into the decision-making process of deep learning models, hold significant promise.

Data Challenges and the Need for Labeled Datasets

Deep learning models often require large labeled datasets for effective training. Acquiring and annotating such datasets can be time-consuming and costly, particularly in domains with limited available data. Researchers are exploring techniques like semi-supervised learning and transfer learning to overcome data limitations.

Computational Resources and Hardware Limitations

Deep learning models are computationally intensive and demand significant hardware resources. Training deep neural networks often requires powerful GPUs or specialized hardware accelerators. Cloud computing and parallel processing frameworks have made deep learning more accessible, but resource constraints remain a challenge.

Emerging Trends: Transfer Learning and Explainable AI

Transfer learning, a technique that allows models to transfer knowledge from one domain to another, has gained significant attention in deep learning. It enables leveraging pre-trained models and fine-tuning them for specific tasks, reducing the need for large amounts of labeled data. Explainable AI, a growing area of research, aims to provide insights and explanations for the decisions made by deep learning models, increasing their transparency and trustworthiness.


Deep learning has transformed the AI domain by allowing computers to learn from complicated data and make intelligent choices. Deep learning applications may be found in a variety of industries, including robotics, healthcare, finance, natural language processing, image identification, and more. Developers and technology enthusiasts can use the full potential of deep learning for better futuristic innovation using tools that are already accessible.

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A Complete Guide To Customer Acquisition For Startups

Any business is enlivened by its customers. Therefore, a strategy to constantly bring in new clients is an ongoing requirement. In this regard, having a proper customer acquisition strategy can be of great importance.

So, if you are just starting your business, or planning to expand it, read on to learn more about this concept.

The problem with customer acquisition

As an organization, when working in a diverse and competitive market like India, you need to have a well-defined customer acquisition strategy to attain success. However, this is where most startups struggle. Now, you may have a great product or service, but if you are not in the right place targeting the right demographic, you are not likely to get the results you want.

To resolve this, typically, companies invest, but if that is not channelized properly, it will be futile.

So, the best way out of this dilemma is to have a clear customer acquisition strategy in place.

How can you create the ideal customer acquisition strategy for your business?

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You need to define your goals so that you can meet the revenue expectations you have for the current fiscal year. You need to find a value for the metrics –

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All these metrics tell you how well you will be able to grow your business and revenue.

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You need to understand who your current customers are and who your target customers are. Once you are aware of your customer base, you can focus your energies in that direction and get the maximum sale of your products or services. You can also understand what your customers require through various analytics and markers and address them to leverage your products/services towards them.

  • Choose your channels for customer acquisition

How will you acquire customers who will eventually tell at what scale and at what rate you need to expand your business? You could market and sell your products on social media channels like Instagram, Facebook and YouTube, or invest in paid marketing like Google Ads. You need to develop a unique strategy for each of these channels. 

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Combining these strategies with your long-term business plan will bring results. However, there will be challenges on the way, where you need to adapt as per the requirements to make the most of it. At the same time, introducing new technologies like AI and ML can also solve such issues easily. To learn more about the use of AI and ML and how they are transforming businesses, keep referring to the blog section of E2E Networks.

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Image-based 3D Object Reconstruction State-of-the-Art and trends in the Deep Learning Era

3D reconstruction is one of the most complex issues of deep learning systems. There have been multiple types of research in this field, and almost everything has been tried on it — computer vision, computer graphics and machine learning, but to no avail. However, that has resulted in CNN or convolutional neural networks foraying into this field, which has yielded some success.

The Main Objective of the 3D Object Reconstruction

Developing this deep learning technology aims to infer the shape of 3D objects from 2D images. So, to conduct the experiment, you need the following:

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By using the apparatus and datasets, you will be able to proceed with the 3D reconstruction from 2D datasets.

State-of-the-art Technology Used by the Datasets for the Reconstruction of 3D Objects

The technology used for this purpose needs to stick to the following parameters:

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Training with the help of one or multiple RGB images, where the segmentation of the 3D ground truth needs to be done. It could be one image, multiple images or even a video stream.

The testing will also be done on the same parameters, which will also help to create a uniform, cluttered background, or both.

  • Output

The volumetric output will be done in both high and low resolution, and the surface output will be generated through parameterisation, template deformation and point cloud. Moreover, the direct and intermediate outputs will be calculated this way.

  • Network architecture used

The architecture used in training is 3D-VAE-GAN, which has an encoder and a decoder, with TL-Net and conditional GAN. At the same time, the testing architecture is 3D-VAE, which has an encoder and a decoder.

  • Training used

The degree of supervision used in 2D vs 3D supervision, weak supervision along with loss functions have to be included in this system. The training procedure is adversarial training with joint 2D and 3D embeddings. Also, the network architecture is extremely important for the speed and processing quality of the output images.

  • Practical applications and use cases

Volumetric representations and surface representations can do the reconstruction. Powerful computer systems need to be used for reconstruction.

Given below are some of the places where 3D Object Reconstruction Deep Learning Systems are used:

  • 3D reconstruction technology can be used in the Police Department for drawing the faces of criminals whose images have been procured from a crime site where their faces are not completely revealed.
  • It can be used for re-modelling ruins at ancient architectural sites. The rubble or the debris stubs of structures can be used to recreate the entire building structure and get an idea of how it looked in the past.
  • They can be used in plastic surgery where the organs, face, limbs or any other portion of the body has been damaged and needs to be rebuilt.
  • It can be used in airport security, where concealed shapes can be used for guessing whether a person is armed or is carrying explosives or not.
  • It can also help in completing DNA sequences.

So, if you are planning to implement this technology, then you can rent the required infrastructure from E2E Networks and avoid investing in it. And if you plan to learn more about such topics, then keep a tab on the blog section of the website

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A Comprehensive Guide To Deep Q-Learning For Data Science Enthusiasts

For all data science enthusiasts who would love to dig deep, we have composed a write-up about Q-Learning specifically for you all. Deep Q-Learning and Reinforcement learning (RL) are extremely popular these days. These two data science methodologies use Python libraries like TensorFlow 2 and openAI’s Gym environment.

So, read on to know more.

What is Deep Q-Learning?

Deep Q-Learning utilizes the principles of Q-learning, but instead of using the Q-table, it uses the neural network. The algorithm of deep Q-Learning uses the states as input and the optimal Q-value of every action possible as the output. The agent gathers and stores all the previous experiences in the memory of the trained tuple in the following order:

State> Next state> Action> Reward

The neural network training stability increases using a random batch of previous data by using the experience replay. Experience replay also means the previous experiences stocking, and the target network uses it for training and calculation of the Q-network and the predicted Q-Value. This neural network uses openAI Gym, which is provided by taxi-v3 environments.

Now, any understanding of Deep Q-Learning   is incomplete without talking about Reinforcement Learning.

What is Reinforcement Learning?

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Now, the understanding of reinforcement learning is incomplete without knowing about Markov Decision Process (MDP). MDP is involved with each state that has been presented in the results of the environment, derived from the state previously there. The information which composes both states is gathered and transferred to the decision process. The task of the chosen agent is to maximize the awards. The MDP optimizes the actions and helps construct the optimal policy.

For developing the MDP, you need to follow the Q-Learning Algorithm, which is an extremely important part of data science and machine learning.

What is Q-Learning Algorithm?

The process of Q-Learning is important for understanding the data from scratch. It involves defining the parameters, choosing the actions from the current state and also choosing the actions from the previous state and then developing a Q-table for maximizing the results or output rewards.

The 4 steps that are involved in Q-Learning:

  1. Initializing parameters – The RL (reinforcement learning) model learns the set of actions that the agent requires in the state, environment and time.
  2. Identifying current state – The model stores the prior records for optimal action definition for maximizing the results. For acting in the present state, the state needs to be identified and perform an action combination for it.
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In case the Q-table size is huge, then the generation of the model is a time-consuming process. This situation requires Deep Q-learning.

Hopefully, this write-up has provided an outline of Deep Q-Learning and its related concepts. If you wish to learn more about such topics, then keep a tab on the blog section of the E2E Networks website.

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GAUDI: A Neural Architect for Immersive 3D Scene Generation

The evolution of artificial intelligence in the past decade has been staggering, and now the focus is shifting towards AI and ML systems to understand and generate 3D spaces. As a result, there has been extensive research on manipulating 3D generative models. In this regard, Apple’s AI and ML scientists have developed GAUDI, a method specifically for this job.

An introduction to GAUDI

The GAUDI 3D immersive technique founders named it after the famous architect Antoni Gaudi. This AI model takes the help of a camera pose decoder, which enables it to guess the possible camera angles of a scene. Hence, the decoder then makes it possible to predict the 3D canvas from almost every angle.

What does GAUDI do?

GAUDI can perform multiple functions –

  • The extensions of these generative models have a tremendous effect on ML and computer vision. Pragmatically, such models are highly useful. They are applied in model-based reinforcement learning and planning world models, SLAM is s, or 3D content creation.
  • Generative modelling for 3D objects has been used for generating scenes using graf, pigan, and gsn, which incorporate a GAN (Generative Adversarial Network). The generator codes radiance fields exclusively. Using the 3D space in the scene along with the camera pose generates the 3D image from that point. This point has a density scalar and RGB value for that specific point in 3D space. This can be done from a 2D camera view. It does this by imposing 3D datasets on those 2D shots. It isolates various objects and scenes and combines them to render a new scene altogether.
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  • GAUDI also uses this to train data on a canonical coordinate system. You can compare it by looking at the trajectory of the scenes.

How is GAUDI applied to the content?

The steps of application for GAUDI have been given below:

  • Each trajectory is created, which consists of a sequence of posed images (These images are from a 3D scene) encoded into a latent representation. This representation which has a radiance field or what we refer to as the 3D scene and the camera path is created in a disentangled way. The results are interpreted as free parameters. The problem is optimized by and formulation of a reconstruction objective.
  • This simple training process is then scaled to trajectories, thousands of them creating a large number of views. The model samples the radiance fields totally from the previous distribution that the model has learned.
  • The scenes are thus synthesized by interpolation within the hidden space.
  • The scaling of 3D scenes generates many scenes that contain thousands of images. During training, there is no issue related to canonical orientation or mode collapse.
  • A novel de-noising optimization technique is used to find hidden representations that collaborate in modelling the camera poses and the radiance field to create multiple datasets with state-of-the-art performance in generating 3D scenes by building a setup that uses images and text.

To conclude, GAUDI has more capabilities and can also be used for sampling various images and video datasets. Furthermore, this will make a foray into AR (augmented reality) and VR (virtual reality). With GAUDI in hand, the sky is only the limit in the field of media creation. So, if you enjoy reading about the latest development in the field of AI and ML, then keep a tab on the blog section of the E2E Networks website.

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