10 Best Artificial Intelligence books for beginners in 2022

April 29, 2022

Artificial Intelligence (AI) is becoming increasingly popular around the world. Its wide range of uses and applications may be found in practically every industry on earth. Process automation, fraud detection, prediction-based analysis, and improving client experiences are just a few of their services.

Artificial Intelligence will be the hottest and most in-demand field in 2022; most engineers and Gen Z people want to work in AI, Data Science, and Data Analytics. The greatest method to start is to go through the best and most dependable resources. Reading good books is one of the best ways of self-learning for beginners.

Here we've compiled a list of the top 10 artificial intelligence books that can help you clear your basics and develop a good understanding of the use of AI. 

1.) You Look Like a Thing and I Love You: How Artificial Intelligence works and why it’s making the world a weirder place. 

Author: Janelle Shane

Janelle Shane, a scientist, and an AI engineer is a frequent contributor to the New York Times, Slate, and The New Yorker on computer science.

Why should you read this book?

Artificial intelligence (AI) is all over the place. It enables your iPhone's autocorrect feature, assists Google Translate in comprehending the complexities of language, and analyzes your activity to determine which of your friends' Facebook postings you want to see the most. It will do medical diagnoses and drive your automobile in the next few years, and it may even assist our novelists in writing the first lines of their works. But how does it function in practice?

The book illustrates how AI perceives our world and what it gets wrong through her humorous experiments, real-life examples, and informative cartoons. It helps readers develop the skills to be skeptical of claims of a wiser future, and not just a working knowledge of AI. 

YOU LOOK LIKE A THING AND I LOVE YOU is an approachable, funny investigation of the future of technology and civilization. It is a comprehensive study of the cutting-edge technologies that will soon power our planet. 

Price: ₹ 1544.00 on amazon

2.) The Algorithmic Leader

Author: Mike Walsh

Mike Walsh is the founder and CEO of Tomorrow, a global consulting firm specializing in business design for the twenty-first century. He counsels business leaders on how to flourish in the face of rapid technological change. Mike travels approximately 300 days a year across the world, researching trends, collecting case studies, and giving presentations on the future of business. The Algorithmic Leader and The Dictionary of Dangerous Ideas are just a few of Mike's books.

Why should you read this book?

We live in a time of wonder, with self-driving cars, electronics that meet our needs, and robots capable of advanced manufacturing and complex surgery. Every aspect of daily life will be transformed by automation, algorithms, and artificial intelligence (AI), but are we ready for what this implies for the future of work, leadership, and creativity? While many people fear that robots will take their jobs, tremendous advances in machine intelligence raise a far more crucial question: what is human intellect's true potential in the twenty-first century?

The Algorithmic Leader is a hopeful and practical guide to survive and prosper in this unprecedented era of change. Readers will be able to build their journey of personal transformation, harness the power of algorithms, and map a clear way ahead for their organization, their team, and themselves by using Walsh's 10 basic concepts.

Price: ₹ 1569.00 on amazon

3.) Human + Machine

Author: Paul Daugherty & H. James Wilson

Paul Daugherty leads Accenture's Technology Innovation & Ecosystem group and he is the company's chief technology and innovation officer. He also serves on the Global Management Committee of Accenture.

H. James Wilson is Accenture Research's Managing Director of Information Technology and Business Research.

Why should you read this book?

Human + Machine was written to assist executives, workers, students, and others in navigating the changes that AI is bringing to business and the economy. They believe AI will lead to breakthroughs that will significantly change the way the world works and lives. AI, on the other hand, will cause upheaval, and many people will require education, training, and assistance to prepare for the new jobs that will be generated. The authors are contributing the revenues from the sale of this book to sponsor education and retraining programs aimed at acquiring fusion skills for the age of artificial intelligence to help meet this requirement.

The book explains how firms are leveraging the new laws of AI to leap ahead in innovation and profitability, as well as what you can do to achieve comparable results, based on the author’s experience and study with 1,500 enterprises. It outlines six whole new types of hybrid human-machine positions that every firm should create, as well as a "leader's handbook" that outlines the five key principles needed to become an AI-powered organization.

Price: ₹ 909.00 on amazon

4.) Human Compatible

Author: Stuart Russell

Stuart Russell is a professor of computer science at the University of California, where he also runs the Center for Human Compatible Artificial Intelligence and holds the Smith-Zadeh Chair in Engineering.

Why should you read this book?

Superhuman artificial intelligence, in the public imagination, is a looming tidal wave that threatens not only jobs and human relationships but civilization itself. Human-machine conflict is considered unavoidable, with an all-too-predictable result. Stuart Russell, a renowned AI researcher, argues in this ground-breaking book that this nightmare can be avoided, but only if we rethink AI from the ground up. Russell begins by exploring the topic of intelligence in humans and machines. He highlights the near-term benefits we might expect, which include intelligent personal assistants and much higher processing speeds to fasten scientific research, as well as the AI breakthroughs that must occur before we achieve superhuman AI.

Price: ₹ 1144.00 on amazon

5.) Super Intelligence

Author: Nick Bostrom 

Nick Bostrom is a polymath and philosopher from Sweden who has studied theoretical physics, computational neuroscience, logic, and artificial intelligence. He is the founding director of the Future of Humanity Institute and a professor at Oxford University.

Why should you read this book?

Human brains have abilities that other animals' brains lack. These unique abilities are responsible for our species' dominance. Other animals may have more powerful muscles or sharper claws, but we have more intelligent minds. If machine brains eventually outperform human brains in general intelligence, this new superintelligence could be extremely powerful.

Learn about oracles, genies, and singletons; boxing methods, tripwires, and mind crime; humanity's cosmic endowment and differential technological development; indirect normativity, instrumental convergence, whole brain emulation, and technology couplings; biological cognitive enhancement and artificial intelligence; dystopian evolution and Malthusian economics; and collective intelligence as you read the book.

Price: ₹ 460.00 on amazon

6.) The Master Algorithm

Author: Pedro Domingos

Pedro Domingos is a well-known AI expert. He's a computer science professor at the University of Washington in Seattle. He has received the SIGKDD Innovation Award and the IJCAI John McCarthy Award, two of the greatest awards in data science and AI.

Why should you read this book?

For a non-technical or non-expert reader, The Master Algorithm provides a wonderful story and summary of machine learning and AI. The reader will gain a thorough understanding of the various types of algorithmic approaches to data and computer-based learning and intelligence, as well as the benefits and drawbacks of each algorithmic technique. It also aids in the development of ideas and concepts related to what is required for computers to demonstrate human-like intelligence, which is far more difficult than most people assume.

Price: ₹599.00 on amazon

7.) Robot Rules: Regulating Artificial Intelligence  

Author: Jacob Turner

Jacob Turner is a lawyer as well as a published author. He is on the Conflict Analytics Lab advisory board, a research-based consortium focused on artificial intelligence and data analytics in conflict resolution and negotiation.

Why should you read this book?

By no means a technical book, but one that deserves to be on the list. Who is responsible if a computer algorithm causes harm? Is it possible for an AI or a robot to have legal protection and rights? How can we tell what's right and incorrect in terms of ethical decision-making? These are only a few of the issues that the field of AI is dealing with, and this book will help you think about the future of technology.

This book outlines why AI is different, what legal and ethical issues it may pose, and how to deal with them. It claims that AI is unlike any other technology because of its ability to make decisions on its own and in an unpredictable manner. According to the book, to address these issues, we need to create new cross-industry and international institutions and rules.

Robot Rules will appeal to those interested in law, politics, and philosophy, as well as those interested in computer programming, neuroscience, and engineering.

Price: ₹2630.00 on amazon

8.) Life 3.0: Being Human in the Age of Artificial Intelligence

Author: Max Tegmark 

Max Tegmark is an MIT professor who enjoys pondering life's fundamental problems. He has written two books and over 200 technical papers on subjects ranging from cosmology to artificial intelligence. For his unconventional views and love of adventure, he is known as "Mad Max." He's also the president and founder of the Future of Life Institute, which works to ensure that we not only develop technology, but also the wisdom needed to put it to good use.

Why should you read this book?

How can we increase our prosperity through automation without robbing people of their livelihood or sense of purpose? What career guidance can we give today's youth? How can we make future AI systems more durable so that they can achieve our goals without crashing, failing, or being hacked? Should we be concerned about a fatal arms race between autonomous weapons? Will machines one day outsmart us in every endeavor, removing humans from the workforce and possibly putting them out of business? Will AI make life better than it has ever been, or will it empower humans beyond our capacity? - and there’s nobody better situated or qualified to answer these questions than Max Tegmark. 

Price: ₹2500.00 on amazon

9.) Genius Makers: The Mavericks Who Brought AI to Google, Facebook, and the World

Author: Cade Metz

Cade Metz is a New York Times reporter who focuses on artificial intelligence, self-driving cars, robots, virtual reality, and other emerging technologies. He was previously a senior staff writer for Wired magazine and the US editor for The Register, one of the UK's most popular science and technology news sites.

Why should you read this book?

This brightly, colored page-turner places artificial intelligence in a human context. Metz describes this breakthrough technology and makes the hunt interesting via the lives of Geoff Hinton and other significant characters. 

What exactly does it mean to be intelligent? What does it mean to be human? What do we truly desire from life and the intelligence we possess or may develop? This book takes you inside the rooms where these issues are being answered with in-depth and exclusive reporting based on hundreds of interviews. Where our most powerful companies, our social discourse, and our daily lives have all been infiltrated by an extremely powerful new artificial intelligence.

Price: ₹ 1756.00 on amazon

10.) Basics of Artificial Intelligence & Machine Learning

Author: Dr. Dheeraj Mehrotra 

Dheeraj Mehrotra is a 2005 National Awardee, a Certified NLP Business Diploma holder, an Educational Innovator, and Author with expertise in Six Sigma in Education, Academic Audits, and Neuro-Linguistic Programming (NLP)

Why should you read this book?

With the advancement of technology, the concepts of Artificial Intelligence (AI) and Machine Learning (ML) have been in use for many years. It has gradually merged our lives through practically every narration of learning, teaching, entertainment, ordinary activities, and so on. 

This book provides a common grasp of the themes about the impact of AI and ML on our lives, as well as deepens our awareness of how technology affects our lives in particular. The author helps the readers to look towards science as a way to make a difference in their lives. To raise awareness about making technology more accessible to people in general. And those in charge must be informed about the use and misuse of the same.

Price: ₹350.00 on amazon


The AI industry is continuing to grow and expand, with the potential to impact every area of our lives and these books are your first step if you want to establish a foothold in the field of artificial intelligence and learn everything you need to know to get started quickly.

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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.

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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?

  • Define what your goals are

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 –

  • MRR – Monthly recurring revenue, which tells you all the income that can be generated from all your income channels.
  • CLV – Customer lifetime value tells you how much a customer is willing to spend on your business during your mutual relationship duration.  
  • CAC – Customer acquisition costs, which tells how much your organization needs to spend to acquire customers constantly.
  • Churn rate – It tells you the rate at which customers stop doing business.

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

  • Identify your ideal customers

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. 

  • Communicate with your customers

If you know exactly what your customers have in mind, then you will be able to develop your customer strategy with a clear perspective in mind. You can do it through surveys or customer opinion forms, email contact forms, blog posts and social media posts. After that, you just need to measure the analytics, clearly understand the insights, and improve your strategy accordingly.

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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This is a decorative image for: Constructing 3D objects through Deep Learning
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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:

  • Input

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

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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?

Reinforcement is a subsection of ML. This part of ML is related to the action in which an environmental agent participates in a reward-based system and uses Reinforcement Learning to maximize the rewards. Reinforcement Learning is a different technique from unsupervised learning or supervised learning because it does not require a supervised input/output pair. The number of corrections is also less, so it is a highly efficient technique.

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.
  3. Choosing the optimal action set and gaining the relevant experience – A Q-table is generated from the data with a set of specific states and actions, and the weight of this data is calculated for updating the Q-Table to the following step.
  4. Updating Q-table rewards and next state determination – After the relevant experience is gained and agents start getting environmental records. The reward amplitude helps to present the subsequent step.  

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.

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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.
  • GAUDI also removes GANs pathologies like mode collapse and improved GAN.
  • 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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