Tesla, a multinational automotive company, has been facing severe scrutiny over its automobiles’ controversial autopilot and full self-driving features that have allegedly killed many. Several state and federal regulators are seeking action against the electric car maker.
The National Highway Traffic Safety Administration (NHTSA) has upgraded its investigation from a mere preliminary analysis to an extensive engineering evaluation. NHTSA has asked Tesla to give clarification on its cabin digital camera. It is a part of the probe into 830,000 Tesla automobiles that have autopilot.
The US regulator, in a letter, asked Tesla to describe the function that the cabin digital camera performs in enforcing driver engagement and attentiveness. It also asked for clarification on how the camera’s inputs are factored into the topic operation of the system.
The letter also requested Tesla to share inputs on:
The impression on driver engagement alert timing and sorts and its integration with the present engagement technique.
Recoverable information components pointing to its effect both through the automobile’s onboard storage or telemetry.
Impression on driver recoverable and alerting information if the motive force chooses not to share information from the digital camera with Tesla.
The Division of Motor Automobiles (DMV) in California accused Tesla earlier this month of operating pretend claims about its autopilot and full self-driving options. The company stated that Tesla wrongly implies that automobiles outfitted with autopilot can function autonomously.
Tesla has now responded, asking the California DMV for a hearing to present an opposition to the claims that it has misled potential prospects. The California DMV filed two separate complaints, alleging Tesla made deceptive claims about the autonomous driving capabilities of its automobiles.
Roskomnadzor, Russia’s internet watchdog, is creating a neural network called Oculus that will use artificial intelligence (AI) to scan websites for banned information.
The automatic scanner will analyze images, videos, chats, and URLs on forums, social media, websites, and even messenger channels to detect material that should be taken down or redacted.
Information targeted by Oculus includes misinformation that discredits official state and army sources, homosexuality propaganda and instructions on manufacturing weapons or drugs. The system will also look for expressions of disrespect for the state, calls for mass protests, and signs of terrorism and extremism.
Oculus’ real-time scanning capacity will be about 2.3 images per second or 200,000 images per day. For this, Eksikyushn RDC LLC will utilize 48 servers with solid GPUs. Oculus will be integrated onto a network of monitoring systems, the Unified Analysis Module, which is currently under development. The aim is to give the government a grip on controlling information flow.
According to Kommersant, Oculus will cost $965,000 (Russia 57.7 million rubles) and must be completed by December 2022. However, experts in the field suggest that the amount is unlikely to cover the cost of achieving such an aspiring project. Roskomnadzor will likely have to allocate more funds along the way.
Introducing Oculus will result in users either giving up on taking part in online discussions or using anonymization tools like the Tor network, a VPN. People may also resort to chatting apps that do not require PII when registering or logging user data.
Deep Learning (DL) and Artificial Intelligence (AI) have made the future of self-driving automobiles and virtual assistants a reality. The innovations of DL can be found everywhere, on our smartphones, streaming services like Netflix, in virtual reality games, and more. The power of deep learning to make computers think, act, and behave like humans is remarkable. Given the rapid growth of computers and technology, newcomers and old professionals seek to learn this new domain of deep learning. As a result, many people opt for this field and try to contribute to the future. A career in deep learning will benefit young innovative minds to grow personally and professionally. Now, let’s learn what deep learning is and some of the best books for deep learning.
What is Deep Learning?
Deep learning is a subset of machine learning and artificial intelligence. This domain allows computers to process classification tasks directly from data like texts, images, and sounds. It is based on artificial neural networks in which multiple layers are processed, thus, called deep learning to extract higher-level features from data. Deep learning is the process of leveraging data analytics and the latest gains in computing power to work even faster than human minds. Studying deep learning can be hectic if you are not on the right track and don’t have the right resources. Many books have focused on deep learning in the last few years, but which one to pick? Here is a list of top deep learning books that may help you start with deep learning.
List of top Deep Learning books
The Hundred-Page Machine Learning Book by Andriy Burkov
To get into deep learning, you need to know about machine learning. And the best way to learn machine learning is by reading & understanding the algorithms and implementing them. Now, several books for deep learning & machine learning are out in the market, as the field of AI is vast, and so is the variety of books. Also, many things overlap in ML & DL. Thus, you want to grasp a good understanding from the beginning. The book ‘The Hundred-Page Machine Learning Book’, written by Andriy Burkov, an ML expert, is a practical guide to getting started with ML. The first few chapters focus on ML formulation, notations, and key terminologies. Thus, beginners and newcomers in the field can opt for this book. Then the coming chapters analyze the most important algorithms in ML and more advanced topics. Though this book contains only one chapter about neural networks, it indeed serves as a building block for DL.
Written by François Chollet, the creator of Keras and a Google AI researcher, ‘Deep Learning with Python’ explains the concepts of DL using the Python language and Keras library. It is one of the best deep learning books that provide a good understanding of the concepts through intuitive explanations and practical examples. This book encourages beginners and intermediate programmers to understand DL in-depth through extensive descriptions of implementing convolutional neural networks (CNNs). In overview, this book is divided into two parts, first, the fundamentals of DL, and two, DL in practice. The fundamentals cover high-level crucial concepts in DL, and practice mostly covers applications such as DL for computer vision, text & sequences, advanced DL practice, and generative DL. By finishing this book, you’ll have the hands-on skills to apply deep learning models in your projects. You can buy this neural network bookonline.
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow by Aurélien Géron
‘Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow’ will guide you through acquiring basic concepts of DL so that anyone can use simple and efficient tools to implement programs capable of learning from data. Written by Aurélien Géron, a machine learning consultant, this deep learning book comprises concrete examples with minimal theory and two production-ready Python frameworks, Sklearn and TensorFlow, to master the use of DL. This book provides an intuitive understanding of the concepts & tools for building intelligent systems using Scikit & Tensorflow.
You need prior programming knowledge to apply what you learn from this book. The exercises range from simple linear regression to processing deep neural networks, including CNN and transfer learning. This book on deep learning helps you to explore ML, particularly neural networks, and other training models like support vector machines (SVM), decision trees, and ensemble methods. Also, you learn the neural network architectures of CNN, recurrent neural network (RNN), and deep reinforcement learning. Then, you can use Sklearn to track end-to-end ML projects and TensorFlow to build & train the neural networks. The book retails at ₹2,600 for the second updated edition.
Deep Learning from Scratch: Building with Python from First Principles by Seth Weidman
‘Deep Learning from Scratch: building with Python from First Principles’ is a handbook to build your foundation of deep learning. The author Seth Weidman is a data scientist who has a unique way of explaining the concepts with a visual representation of the working of the algorithm, a mathematical explanation of why the algorithm works, and a pseudocode implementation of the algorithm. It is one of the best books on deep learning that teaches how to apply multiplayer neural networks and convolutional networking. Also, it provides a comprehensive introduction to DL for data scientists & software engineers. It focuses on how neural networks work using the first principles hence, the name. The book starts with DL basis and then moves to extensive details of important advanced networks of CNN & RNN. It has a dedicated chapter on extensions and PyTorch, explaining loss function, momentum & weight initialization, etc, and how to implement DL models with PyTorch & unsupervised learning, respectively.
Deep Learning (Adaptive Computation and Machine Learning series) by Ian Goodfellow, Yoshua Bengio, Aaron Courville, Francis Bach
‘Deep Learning (Adaptive Computation and Machine Learning series)’ is on the list of top books for deep learning that presents an in-depth understanding of deep learning, written together by four computer scientists and deep learning enthusiasts. Ian J. Goodfellow, a research scientist in DeepMind, who invented generative adversarial networks (GANs). Yoshua Bengio is one of the leading experts in AI, a professor at the Université de Montréal & head of the Montreal institute for learning algorithms. Aaron Courville is an Associate professor at the Université de Montréal & member of the Mila-Quebec Artificial Intelligence Institute. Francis Bach is a world-renowned ML expert and researcher at the National institute for research in digital science and technology (INRIA). The books combine a wide range of concepts and topics in deep learning. It is divided into three parts, first, applied math & ML basics; second, modern practices in DL, and third, DL research. The first part has a firm mathematical foundation and covers linear algebra, probability theory, information theory, and numerical computation. In the second part, the book explains deep feedforward networks, regularization, optimization, CNN, sequence modeling, and applications. In the third and final part, the book offers insight into linear factor models, autoencoders, representation learning, Monte Carlo methods, structured probabilistic models, confronting partition function, and deep generative models. This book is an excellent addition to deep learning books, which is available online.
‘Grokking Deep Learning’ talks about the science behind DL by explaining the building and training of neural networks. The author Andrew W. Trask, a PhD student at Oxford University and a research scientist at DeepMind, focused on unveiling the science under the hood so that you understand every detail of training a neural network. This book emphasizes using Python and NumPy to train neural networks to see & understand images, translate text into different languages, etc, to master the working of DL frameworks. Beginners can see this neural network and deep learning textbook as a mentor, as it walks through every aspect of the why, what, and how of deep learning models. In the end, you get a chapter, ‘Where to go from here’ in which the factors of DL are explained and how DL will be a promising career for you.
Deep Learning with PyTorch by Eli Stevens, Luca Antiga, Thomas Viehmann
‘Deep Learning with PyTorch’ is among the most popular machine learning and deep learning books. This practical book dynamically gets you to build real-world projects from scratch. The authors are Eli Stevens, a software engineer & CTO of a startup company building software for radiology, Luca Antiga, the co-founder & CEO of an AI engineering company and a constant contributor to PyTorch, and Thomas Viehmann, a core PyTorch core developer and an ML & PyTorch specialist trainer & consultant. The book teaches you how to create neural networks & DL systems with PyTorch. It covers some of the best practices for DL pipeline and basics and takes you to larger projects. The highlight of this book is an elaborated neural network designed for cancer detection. This is a whole package for deep learning books where you discover ways to train networks with limited inputs and then focus on the diagnosis to fix problems in the network. Eventually, you will learn ways to improve the network & architecture, perform fine-tuning, and the results with augmented data.
Researchers at Stanford University, MIT’s Computer and AI lab, and the Autodesk AI Lab have created a deep learning framework to construct 3D objects. The Manual-to-Executable-Plan Network (MEPNet) framework was tested on Lego sets generated by computers. The training included genuine Lego set instructions and Minecraft-style voxel building plans.
Existing methods of rendering 3D objects are simple but computationally expensive and not very good at handling unseen shapes. Additionally, a few problems surface when existing AI techniques interpret 2D instructions to transform them into 3D. Visual instructions like Lego sets consist entirely of images; hence, identifying differences between 2D and 3D can become complex because they are usually assembled.
The researchers said, “This increases the difficulty for machines to interpret Lego manuals: it requires inferring 3D poses of unseen objects composed of seen primitives.”
MEPNet combines the existing upsides and new 3D rendering techniques by starting with a 3D model of components, Lego set, and 2D manual images. It predicts a set of 2D keypoints and masks each component.
After masking, 2D keypoints are “back-projected to 3D by finding possible connections between the base shape and the new components.” The team said the combination “maintains the efficiency of learning-based models.”
All you have to do is interpret MEPNet’s 3D renderings, which would hopefully be easier than flat-pack furniture instructions. You can test MEPNet here if you are familiar with PyTorch.
Google's PaLM-SayCan robots use AI language models to understand that picking up a sponge is useful someone who needs help with a spilled drink.
Credit: Stephen Shankland
Imagine you have come home after hitting the gym, you want to have a smoothie, but you are too tired to make one. You suddenly remember you have to wash utensils from the previous meal, vacuum the floor and cook for dinner–but you are still tired and sore from your intensive workout routine at the gym. Well, Google can help you with it! Google has revealed that it is working on an artificial intelligence (AI) system that can pick up on human communication styles and innately carry out human wishes. Google has also published a robot that is in development that is outfitted with this AI as per its paper ‘Do As I Can, Not As I Say: Grounding Language in Robotic Affordances.’
You may hire a butler like Batman’s Alfred to help you with daily chores (if you can afford one) or ask your robot butler. The problem with robots is that though they are adept at carrying out short hard-coded instructions systematically, they may fail in comprehending ambiguous requests. For instance, if you mention that you are hungry to a robot, it may acknowledge that yet do not know what to do next. However, a robot from Everyday Robots Project, a group under its experimental X labs, can offer you a bag of Doritos from your Kitchen counter without receiving instructions from you about the same. Through the use of millions of web-scraped text pages, the robot’s control software has developed the ability to convert spoken words into a series of physical movements.
With the use of the technology and Google’s AI language model, a robot can now decipher ambiguous human commands and put together a series of responses. That contrasts sharply with the carefully programmed tasks that the majority of robots carry out under strictly regulated conditions, such as fixing windshields on a vehicle manufacturing line. This proves that we are closer to witnessing robots straight out of science fiction.
Google reveals that, unlike virtual assistants like Alexa or Siri, a person doesn’t need to deliver orders using a certain set of previously approved wake-up words for this AI robot. The robot would try to fetch you something to drink if you say “I’m thirsty,” and it should return with a sponge if you say, “Whoops, I just spilled my drink.” This technological feat has been made possible with the use of the most powerful large language model developed by Google. Dubbed the Pathways Language Model (PaLM), this large language model is a dense decoder-only Transformer model with 540 billion parameters that was trained using the Pathways technology, allowing Google to effectively train a single model across several TPU v4 Pods.
PaLM was trained using a combination of English and multilingual datasets, including GitHub code, high-quality web publications, articles from Wikipedia, and chats. Additionally, Google had developed a “lossless” vocabulary that breaks numbers into separate tokens, one for each digit, splits non-vocabulary Unicode characters into bytes, and maintains all whitespace (which is crucial for coding). At the time of its announcement, Google claimed that PaLM performs impressively on a variety of BIG-bench tests for natural language processing and creation. The model, for instance, can recognize cause and effect, comprehend conceptual combinations in certain settings, and even identify a movie from an emoji.
The robot butler was developed by Google researchers using new software that takes advantage of PaLM’s text processing skills to transform a spoken command or phrase into a series of relevant actions that the robot may carry out, such as “open drawer” or “pick up chips.” Google has christened the resulting system PaLM-SayCan, a catchphrase that describes how the model blends the language comprehension skills of LLMs (“Say”) with the “dynamic capabilities grounding” of its robots (that’s “Can” – processing instructions via various actions).
The robot has a glowing green rim around their faces to signify when it is active. It changes color or switches off in other circumstances. Credit: Stephen Shankland
In order for the robot to independently explore a location and recognize objects and locations relevant to a command, it also has hearing and optical sensors.
Per the Everyday Robots, by incorporating a multitude of machine learning algorithms such as reinforcement learning, collaborative learning, and learning from demonstration, the robots have progressively improved their knowledge of their environment and their aptitude for doing common tasks.
An octet of Google PaLM-StayCan Robots practices manipulations like opening drawers and grabbing objects. Credit: Stephen Shankland
Through a separate training phase, where people remotely operated the PaLM-SayCan robot to demonstrate how to perform things like picking up objects, the robot learned its library of physical activities. It can only carry out a certain number of activities inside its surroundings, which helps avoid language model ambiguities from manifesting as wayward behavior. Google claims that this technology is ready to go mainstream as the company researchers have accomplished research undertaking. Instead of testing it in a more controlled lab setting, Google has been trialing it in the employee kitchen area so as to create robots that can be useful in the unexpected turmoil of our everyday lives. This exemplifies the potential for butler robots to adjust to the uncertainty of the real world. The ability of robots to browse the internet and fulfill purchases is already progressing as Google Research, and Everyday Robots collaborate together to integrate the finest language models with robot learning.
Google PaLM-SayCan robot retrieves a bag of chips from a drawer in an employee kitchen area. Credit: Stephen Shankland
However, due to the assistants’ limited ability to respond to orders contextually and the fact that the announcement merely served as a preview of possible capabilities, the robotic butlers are not yet suitable for commercial deployment.
Meanwhile, although Google claims to be pursuing research responsibly, fears about robots becoming surveillance machines or possessing technology that might respond in an inappropriate manner could ultimately cause adoption to stagnate. Google reassures individuals who worry that things can go wrong that they take a proactive approach to this research and adhere to Google’s AI Principles while building helper robots.
Google PaLM-SayCan Robot drops a Pepsi can into a recycle bin. Credit: Stephen Shankland
According to Google, by incorporating PaLM-SayCan into their robots, the robots were able to chart the right actions to 101 user instructions 84% of the time and carry them out 74% of the time. Despite the fact that these statistics are impressive, the data should be interpreted cautiously. Since we don’t have access to all 101 commands, it’s unclear how limited these directives were. Were these 101 instructions tailored to grasp the complexity of language that a true robot butler would be able to understand? Can they understand complex commands or wishes like, ‘I want an orange soda instead of lime,’ ‘Can you organize the closet,’ or ‘Would you julienne the tomatoes instead of dicing them.’ Can their actions align with human expectations every time? For instance, when asked to ‘put on the TV,’ would it switch on the TV (human intent) or put the TV at some place (machine logical reasoning)?
Some skeptics believe that once an AI system reaches a certain level of complexity and reacts to its surroundings in a manner resembling that of a human, we should consider it to be aware and, maybe, to have rights. The recent controversy around permitting AI-powered robots to be a part of daily human life was brought up during the Moscow Chess Open competition when a chess-playing robot went rogue and assaulted its 7-year-old opponent for not waiting for the robot to make its move.
From an architectural perspective, the majority of contemporary AI systems focus on one job or a narrow band of tasks at a time. In contrast, PaLM-SayCan will be expected to understand the human conversation and deliver expected results that span multiple tasks, including fetching you a bag of chips. Not only that, but PaLM-SayCan must also differentiate between logically carrying out human commands and cognitive-ethical reasoning. If asked to feed chocolate to a dog, would it promptly follow the instructions or use the fact that dogs are allergic to chocolate to refrain from following human commands?
The NVIDIA Graduate Fellowship Program is accepting applications from Ph.D. students regarding their research projects until September 9th. This is the 22nd year consecutively that NVIDIA is inviting Ph.D. students to showcase their academic achievements and research. The program began in 2002 and has gained momentum, with as many as 185 grants worth over US$5.9m.
The program seeks doctoral students in 1st year and poses an award of US$50,000 for exceptional research projects. All recipients will have access to NVIDIA products and mentorship opportunities throughout the program. A mandatory internship will follow the program after the fellowship year ends.
Experts will evaluate research projects on several criteria like recommendations, academic achievements, relevance, and domains catering to NVIDIA’s primary research.
The program is a great way to support academia and research in the pursuit of technological innovation and to expand NVIDIA’s employer base by introducing it as an ideal avenue for future experts. Per NVIDIA’s press release, this program “allows us to demonstrate our commitment to academia in supporting research that spans all areas of computing innovation.”
This year’s itinerary will emphasize artificial intelligence, deep neural networks, natural language processing, robotics, and related technologies. Billy Dally, senior research VP and chief scientist at NVIDIA, said the fellowship program would help participants develop relationships with industry experts and pave their way forward.
Meta AI is releasing Implicitron, a modular framework within the PyTorch3D library, to advance 3D neural representations. Implicitron will provide implementations and abstractions to render 3D components.
With rapid advances in neural representations, more windows on possibilities are opening up, leading to an unclear method of choice. This new research is based on a new computer vision technique that seamlessly integrates natural and virtual objects in augmented reality. After NeRF techniques came into the picture, over 50 variants of this method have been released for synthesizing views of complex scenes. It is yet in its infancy, with new variants surfacing frequently.
Implicitron makes it possible to evaluate combinations, variations, and modifications with a standard codebase without any 3D graphics expertise. The modular architecture enables people to use it as a user-friendly state-of-the-art method while extending NeRF with a trainable view. Meta has successfully created composable versions of several generic neural reconstruction components to create real-time photorealistic renderings.
Additionally, Meta has also curated additional components for experimentation and extensibility. They have included a plug-in system to allow user-specified implementation and flexible configuration. It also comes with a training class that utilizes PyTorch Lightning for launching new experiments.
Implicitron aspires to function as a cornerstone for research in the area of neural implicit representation and rendering, just like Meta’s Detectron2 has become the go-to framework for constructing and assessing object detection methods on a range of data sets.
Meta aims to provide users of the framework with a way to quickly install and import components from Implicitron into their projects without recompiling or copying the code.
Hyundai Motor Group bought a controlling share in Boston Dynamics AI institute as it invested over US$400m, giving the former 80% shareholding. The new AI facility will be a research-driven organization focused on “solving the most important and difficult challenges facing the creation of advanced robots.”
Both the companies aim to advance artificial intelligence fundamentals in this new institute led by Marc Raibert, the founder of Boston Dynamics. Hyundai will invest its resources in technical areas and their development with its staff while partnering with many corporate research labs.
This AI institute will be centered in the Kendall Square research community, Cambridge, Massachusetts, and will host many software and hardware experts. Several more AI, robotics, ML, and engineering experts will work together to develop robotic technologies and enhance their capabilities for several new use cases.
Raibert said, “Our mission is to create future generations of advanced robots and intelligent machines that are smarter, more agile, perceptive, and safer than anything that exists today.”
This is not the only investment Hyundai Group is taking; it also announced plans to establish a Global Software Center to enhance its software technologies and SDVs (software-defined vehicles). The center will focus on 42dot, autonomous driving technology, and mobility software.
Python is one of the most popular and basic interpreted programming languages. The reason why most people favor Python is that the usability of Python is limitless. It can be applied to any programming task and used for web development, machine learning, or complex data analysis. With its easy-to-learn and understand property, Python comes on top of a coder’s arsenal. The popularity of Python is only increasing daily, which makes newcomers opt to learn Python. There are many resources for learning the Python programming language, including books, video tutorials, online courses, etc. Here, we have a list of top Python programming books one can pick and start coding.
Python Crash Course – Eric Matthes
‘Python Crash Course’ by Eric Matthes is a Python programming book for beginners with a comprehensive and project-based introduction to Python language. This book provides the fundamentals of Python, including Python elements and data structures. This book remarks that it concentrates on the basics and the application of everything you learn. The book has two sections; the first covers different data types & how to work with each data type, logical (if & while) statements, then follows dictionaries, functions, classes, file handling, and code testing & debugging.
The second section contains three major projects and some fun and clever implementations of the knowledge you gained in the previous section. The first project is an Alien invasion game, specifically the space invaders arcade game, where you develop the game using pygame package. The second project is based on data visualizations, including working with matplotlib & pygal packages, random walks, rolling dice, graphs & charts, and a bit of statistical analysis. This project aims for you to interact with web APIs, deal with various data formats, and retrieve & visualize data from GitHub. The third project is a simple creation of a web application using Django. Step by step, this project guides you through installing Django, designing models, creating an admin interface, setting up user accounts, managing access controls, styling your entire application, and deploying it to Heroku. Well, it seems a lot, but this book makes it easier with well-written and organized content & exercises.
Head First Python: A Brain-Friendly Guide – Paul Barry
‘Head First Python: A Brain-Friendly Guide’ by Paul Barry uses an easy-to-learn approach that is a visually rich format to engage the mind, based on the latest research in cognitive science and learning theory. This multi-sensory learning approach focuses on how your brain works, making the book very engaging and easy to read. The book starts with basic concepts of lists and how to use & manage them. Then discuss modules, errors, and file handling with a unifying project of building a dynamic website for a school athletic coach through a common gateway interface (CGI). It covers a range of Python tasks using data structures and built-in functions, making the learning process more accessible, painless, and effective. This book will teach you to build your web application, database management, and exception handling with other fundamentals.
Python Programming: Using Problem Solving Approach – Reema Thareja
‘Python Programming: Using Problem Solving Approach’ by Reema Thareja is a detailed Python textbook designed to fulfill a first-level course in Python programming. The book contains 12 in-depth chapters related to Python and more. The first two chapters introduce computers and problem-solving strategies, and object-oriented programming. The highlight of the book is that the concepts include illustrations for easy depiction and understanding, and numerous examples are provided with the outputs to help students grasp the art of writing code. Further, the chapters review the basics of Python programming, decision control statements, functions, Python strings revisited, data structures, and classes and objects. The unique point of this book is the notes and programming tip markups that indicate the important concepts and help readers avoid common errors while coding. The last three chapters of the book discuss the other topics in Python scripting, including inheritance and polymorphism, operator overloading, and error and exception handling.
Automate the Boring Stuff with Python – Al Sweigart
‘Automate the Boring Stuff with Python’ teaches Python 3 is for all types of learners, from beginners to experienced programmers. This book doesn’t need the learner to have any prior knowledge of Python or programming, anyone can learn from it. It is one of the best-selling Python programming books that help you master the fundamentals of Python. The book explores several library modules for the use case of tasks like data scraping, reading PDF & word documents, and automating clicking & typing. It covers the fundamentals and core concepts of Python in the first eleven chapters and then dives into web scraping, working with excel spreadsheets, google spreadsheets, PDF & word documents, etc. The level of tasks is divided so that you can create Python programs once you master Python programming basics. Then perform automation exercises to search for text in file/files, create, update, move or rename files, search the web & download online content, etc. The book focuses on the jobs that take hours to complete when done manually, so by using Python, you can automate the process and perform the same task in minutes.
Python Programming for Beginners: 2 Books in 1 – The Ultimate Step-by-Step Guide To Learn Python Programming Quickly with Practical Exercises (Computer Programming) – Mark Reed
‘Python Programming for Beginners: 2 books in 1 – The Ultimate Step-by-Step Guide To Learn Python Programming Quickly with Practical Exercises’ is one of the Python programming books for beginners that delivers what it promises in the title itself. The book is written by Mark Reed, which is a very short and concise book with a step-to-step guide. In this fast-paced world, the learning process seems lengthy and boring, but this book aims to make the process of learning Python programming simpler and faster. The book is intralinked, meaning the previous chapters hold importance for the upcoming chapters following an overall step-to-step approach. The chapters in this book consist of algorithm and information processing, working with Python strings, math functions in Python, file processing, and more. Along with theories and explanations of Python, the book comes with a dynamic and interactive guide to executing the codes learned.
Think Python: How to Think Like a Computer Scientist – Allen B.Downey
‘Think Python: How to Think Like a Computer Scientist is one of the free Python programming books written by Allen B.Downey, an American computer scientist. It introduces Python programming and is designed to define all core terms and then develop each new concept in a logical progression. The book aims to teach how coders or expert programmers think about coding. This hands-on guide will walk you through a step-by-step process of beginning a programming journey, the basic framework, and the terminologies in programming, then move to advance topics of functions, recursion, data structures, and object-oriented design. This book covers a wide range of content that otherbooks on Python do not touch. These overlooked topics discussed in this book are operator overloading, polymorphism, analysis of algorithms, and mutability versus immutability. The latest edition, the book’s second edition, packs four major projects providing a practical case study for learners to understand and implement code. This book offers a hands-on programming experience as you learn them and is best suited for beginners, self-learners, and working professionals who have a zeal for Python programming.
Fluent Python: Clear, Concise, and Effective Programming – Luciano Ramalho
The name says it all, ‘Fluent Python: Clear, Concise and Effective Programming’ is a book that will make you fluent in Python programming language. The author, Luciano Ramalho, is a web developer and runs his own Python training company. When other Python programming books focus on details and working of a code script, this book has a basis on the Python core features & libraries and shows how to make your code shorter, faster, and more readable simultaneously. The book is focused on intermediate Python programmers who have a solid foundation and want to upskill to the next level and also the experienced programmers who work on other programming languages and want to learn Python to perform the same tasks. The highlight of this book, among other Python programming books, is the organization of topics, such as each section is independent and doesn’t need to cover prior chapters to go into it. The chapters are divided into six sections; prologue, data structures, functions as objects, object-oriented idioms, control flow, and metaprogramming. This book is exceptionally approachable as each chapter contains code examples and numbered call-outs linking lines providing helpful descriptions. It is a hands-on guide helping programmers to write Python code using the best features of the languages.
All the above mentioned Python programming books provide good knowledge of Python programming and a practical experience in coding. Apart from these, this bonus book is for Python programmers to practice and prepare for future opportunities.
Bonus book – Elements of Programming Interviews in Python: The Insiders’ Guide – Adnan Aziz, Amit Prakash & Tsung Hsien Lee
The book ‘Elements of Programming Interviews in Python: The Insiders’ Guide’ is to test what you know about Python. It contains a set of 250 challenging problems to test your Python skills, which are often asked at interviews at top software companies. The problems asked in the book have illustrated 200 figures, 300 tested programs, and 150 additional variants followed by detailed solutions. Further, the book summarizes theory-based interview tips and practice questions. This is one of the Python programming books that helps to brush up on your Python concepts, including data structures and algorithms.
BrainChip Holdings Ltd., a commercial supplier of neuromorphic AI, is leveraging its neuromorphic technology via the BrainChip University AI Accelerator Program. The program aims to share technical knowledge, promote cutting-edge discoveries, and guide students to become the next generation of tech innovators.
The program is a package of training, guidance, and hardware provision for students at higher education institutions with AI and engineering programs. BrainChip’s products will be available for students who enroll in the program to use in their projects for multiple use cases. The students will also have access to event-based technologies in real-world applications.
The AI accelerator program finished a pilot session at Carnegie Mellon University in the previous Spring semester. Five more universities are expected to join the sessions in the coming academic year.
Each session will demonstrate and educate students about a working environment for BrainChip’s AKD1000 on a Linux system, thus clubbing lecture-based tutoring with hands-on experiential learning. BrainChip’s Akida mimics the brain to analyze specific essential sensory inputs on the acquisition, data processing, and energy economy.
Prof John Paul Shen, Electrical and Computer Engineering Department at Carnegie, said, “Our students had a great experience in using the Akida development environment and analyzing results from the Akida hardware. We look forward to running and expanding this program in 2023.”
