Explore self-driving car technology using deep learning and artificial intelligence techniques and libraries such as TensorFlow, Keras, and OpenCV Key Features Build and train powerful neural network models to build an autonomous car Implement computer vision, deep learning, and AI techniques to create automotive algorithms Overcome the challenges faced while automating different aspects of driving using modern Python libraries and architectures Book DescriptionThanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving. By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries. What you will learn Implement deep neural network from scratch using the Keras library Understand the importance of deep learning in self-driving cars Get to grips with feature extraction techniques in image processing using the OpenCV library Design a software pipeline that detects lane lines in videos Implement a convolutional neural network (CNN) image classifier for traffic signal signs Train and test neural networks for behavioral-cloning by driving a car in a virtual simulator Discover various state-of-the-art semantic segmentation and object detection architectures Who this book is forIf you are a deep learning engineer, AI researcher, or anyone looking to implement deep learning and computer vision techniques to build self-driving blueprint solutions, this book is for you. Anyone who wants to learn how various automotive-related algorithms are built, will also find this book useful. Python programming experience, along with a basic understanding of deep learning, is necessary to get the most of this book.

Build a Career in Data Science is the top guide to help readers get their first data science job, then quickly becoming a senior employee. Industry experts Jacqueline Nolis and Emily Robinson lay out the soft skills readers need alongside their technical know-how in order to succeed in the field. Key Features * Creating a portfolio to show off your data science projects * Picking the role that's right for you * Assessing and negotiating an offer * Leaving gracefully and moving up the ladder * Interviews with professional data scientists about their experiences This book is for readers who possess the foundational technical skills of data science, and want to leverage them into a new or better job in the field. About the technology From analyzing drug trials to helping sports teams pick new draftees, data scientists utilize data to tackle the big questions of a business. But despite demand, high competition and big expectations make data science a challenging field for the unprepared to break into and navigate. Alongside their technical skills, the successful data scientist needs to be a master of understanding data projects, adapting to company needs, and managing stakeholders. Jacqueline Nolis is a data science consultant and co-founder of Nolis, LLC, with a PhD in Industrial Engineering. Jacqueline has spent years mentoring junior data scientists on how to work within organizations and grow their careers. Emily Robinson is a senior data scientist at Warby Parker, and holds a Master's in Management. Emily's academic background includes the study of leadership, negotiation, and experiences of underrepresented groups in STEM.

Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms Key Features Ideal for those getting started with machine learning for the first time A step-by-step machine learning tutorial with exercises and activities that help build key skills Structured to let you progress at your own pace, on your own terms Use your physical print copy to redeem free access to the online interactive edition Book DescriptionYou already know you want to understand supervised learning, and a smarter way to do that is to learn by doing. The Supervised Learning Workshop focuses on building up your practical skills so that you can deploy and build solutions that leverage key supervised learning algorithms. You'll learn from real examples that lead to real results. Throughout The Supervised Learning Workshop, you'll take an engaging step-by-step approach to understand supervised learning. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend learning how to predict future values with auto regressors. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Supervised Learning Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your book. Fast-paced and direct, The Supervised Learning Workshop is the ideal companion for those with some Python background who are getting started with machine learning. You'll learn how to apply key algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead. What you will learn Get to grips with the fundamental of supervised learning algorithms Discover how to use Python libraries for supervised learning Learn how to load a dataset in pandas for testing Use different types of plots to visually represent the data Distinguish between regression and classification problems Learn how to perform classification using K-NN and decision trees Who this book is forOur goal at Packt is to help you be successful, in whatever it is you choose to do. The Supervised Learning Workshop is ideal for those with a Python background, who are just starting out with machine learning. Pick up a Workshop today, and let Packt help you develop skills that stick with you for life.

Traditional machining has many limitations in today's technology-driven world, which has caused industrial professionals to begin implementing various optimization techniques within their machining processes. The application of methods including machine learning and genetic algorithms has recently transformed the manufacturing industry and created countless opportunities in non-traditional machining methods. Significant research in this area, however, is still considerably lacking. Machine Learning Applications in Non-Conventional Machining Processes is a collection of innovative research on the advancement of intelligent technology in industrial environments and its applications within the manufacturing field. While highlighting topics including evolutionary algorithms, micro-machining, and artificial neural networks, this book is ideally designed for researchers, academicians, engineers, managers, developers, practitioners, industrialists, and students seeking current research on intelligence-based machining processes in today's technology-driven market.

Fun and exciting projects to learn what artificial minds can create Key Features Code examples are in TensorFlow 2, which make it easy for PyTorch users to follow along Look inside the most famous deep generative models, from GPT to MuseGAN Learn to build and adapt your own models in TensorFlow 2.x Explore exciting, cutting-edge use cases for deep generative AI Book DescriptionMachines are excelling at creative human skills such as painting, writing, and composing music. Could you be more creative than generative AI? In this book, you'll explore the evolution of generative models, from restricted Boltzmann machines and deep belief networks to VAEs and GANs. You'll learn how to implement models yourself in TensorFlow and get to grips with the latest research on deep neural networks. There's been an explosion in potential use cases for generative models. You'll look at Open AI's news generator, deepfakes, and training deep learning agents to navigate a simulated environment. Recreate the code that's under the hood and uncover surprising links between text, image, and music generation. What you will learn Export the code from GitHub into Google Colab to see how everything works for yourself Compose music using LSTM models, simple GANs, and MuseGAN Create deepfakes using facial landmarks, autoencoders, and pix2pix GAN Learn how attention and transformers have changed NLP Build several text generation pipelines based on LSTMs, BERT, and GPT-2 Implement paired and unpaired style transfer with networks like StyleGAN Discover emerging applications of generative AI like folding proteins and creating videos from images Who this book is forThis is a book for Python programmers who are keen to create and have some fun using generative models. To make the most out of this book, you should have a basic familiarity with math and statistics for machine learning.

Gain expertise in advanced deep learning domains such as neural networks, meta-learning, graph neural networks, and memory augmented neural networks using the Python ecosystem Key Features Get to grips with building faster and more robust deep learning architectures Investigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorch Apply deep neural networks (DNNs) to computer vision problems, NLP, and GANs Book DescriptionIn order to build robust deep learning systems, you'll need to understand everything from how neural networks work to training CNN models. In this book, you'll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application. You'll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you'll focus on variational autoencoders and GANs. You'll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You'll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you'll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you'll understand how to apply deep learning to autonomous vehicles. By the end of this book, you'll have mastered key deep learning concepts and the different applications of deep learning models in the real world. What you will learn Cover advanced and state-of-the-art neural network architectures Understand the theory and math behind neural networks Train DNNs and apply them to modern deep learning problems Use CNNs for object detection and image segmentation Implement generative adversarial networks (GANs) and variational autoencoders to generate new images Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models Understand DL techniques, such as meta-learning and graph neural networks Who this book is forThis book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.

Apply deep learning techniques and neural network methodologies to build, train, and optimize generative network models Key Features Implement GAN architectures to generate images, text, audio, 3D models, and more Understand how GANs work and become an active contributor in the open source community Learn how to generate photo-realistic images based on text descriptions Book DescriptionWith continuously evolving research and development, Generative Adversarial Networks (GANs) are the next big thing in the field of deep learning. This book highlights the key improvements in GANs over generative models and guides in making the best out of GANs with the help of hands-on examples. This book starts by taking you through the core concepts necessary to understand how each component of a GAN model works. You'll build your first GAN model to understand how generator and discriminator networks function. As you advance, you'll delve into a range of examples and datasets to build a variety of GAN networks using PyTorch functionalities and services, and become well-versed with architectures, training strategies, and evaluation methods for image generation, translation, and restoration. You'll even learn how to apply GAN models to solve problems in areas such as computer vision, multimedia, 3D models, and natural language processing (NLP). The book covers how to overcome the challenges faced while building generative models from scratch. Finally, you'll also discover how to train your GAN models to generate adversarial examples to attack other CNN and GAN models. By the end of this book, you will have learned how to build, train, and optimize next-generation GAN models and use them to solve a variety of real-world problems. What you will learn Implement PyTorch's latest features to ensure efficient model designing Get to grips with the working mechanisms of GAN models Perform style transfer between unpaired image collections with CycleGAN Build and train 3D-GANs to generate a point cloud of 3D objects Create a range of GAN models to perform various image synthesis operations Use SEGAN to suppress noise and improve the quality of speech audio Who this book is forThis GAN book is for machine learning practitioners and deep learning researchers looking to get hands-on guidance in implementing GAN models using PyTorch. You'll become familiar with state-of-the-art GAN architectures with the help of real-world examples. Working knowledge of Python programming language is necessary to grasp the concepts covered in this book.

As environmental issues remain at the forefront of energy research, renewable energy is now an all-important field of study. And as smart technology continues to grow and be refined, its applications broaden and increase in their potential to revolutionize sustainability studies. This potential can only be fully realized with a thorough understanding of the most recent breakthroughs in the field. Research Advancements in Smart Technology, Optimization, and Renewable Energy is a collection of innovative research that explores the recent steps forward for smart applications in sustainability. Featuring coverage on a wide range of topics including energy assessment, neural fuzzy control, and biogeography, this book is ideally designed for advocates, policymakers, engineers, software developers, academicians, researchers, and students.

Leverage Python source code to revolutionize your short selling strategy and to consistently make profits in bull, bear, and sideways markets Key Features Understand techniques such as trend following, mean reversion, position sizing, and risk management in a short-selling context Implement Python source code to explore and develop your own investment strategy Test your trading strategies to limit risk and increase profits Book DescriptionIf you are in the long/short business, learning how to sell short is not a choice. Short selling is the key to raising assets under management. This book will help you demystify and hone the short selling craft, providing Python source code to construct a robust long/short portfolio. It discusses fundamental and advanced trading concepts from the perspective of a veteran short seller. This book will take you on a journey from an idea ("buy bullish stocks, sell bearish ones") to becoming part of the elite club of long/short hedge fund algorithmic traders. You'll explore key concepts such as trading psychology, trading edge, regime definition, signal processing, position sizing, risk management, and asset allocation, one obstacle at a time. Along the way, you'll will discover simple methods to consistently generate investment ideas, and consider variables that impact returns, volatility, and overall attractiveness of returns. By the end of this book, you'll not only become familiar with some of the most sophisticated concepts in capital markets, but also have Python source code to construct a long/short product that investors are bound to find attractive. What you will learn Develop the mindset required to win the infinite, complex, random game called the stock market Demystify short selling in order to generate alpa in bull, bear, and sideways markets Generate ideas consistently on both sides of the portfolio Implement Python source code to engineer a statistically robust trading edge Develop superior risk management habits Build a long/short product that investors will find appealing Who this book is forThis is a book by a practitioner for practitioners. It is designed to benefit a wide range of people, including long/short market participants, quantitative participants, proprietary traders, commodity trading advisors, retail investors (pro retailers, students, and retail quants), and long-only investors. At least 2 years of active trading experience, intermediate-level experience of the Python programming language, and basic mathematical literacy (basic statistics and algebra) are expected.

Master advanced techniques and algorithms for deep learning with PyTorch using real-world examples Key Features Understand how to use PyTorch 1.x to build advanced neural network models Learn to perform a wide range of tasks by implementing deep learning algorithms and techniques Gain expertise in domains such as computer vision, NLP, Deep RL, Explainable AI, and much more Book DescriptionDeep learning is driving the AI revolution, and PyTorch is making it easier than ever before for anyone to build deep learning applications. This PyTorch book will help you uncover expert techniques to get the most out of your data and build complex neural network models. The book starts with a quick overview of PyTorch and explores using convolutional neural network (CNN) architectures for image classification. You'll then work with recurrent neural network (RNN) architectures and transformers for sentiment analysis. As you advance, you'll apply deep learning across different domains, such as music, text, and image generation using generative models and explore the world of generative adversarial networks (GANs). You'll not only build and train your own deep reinforcement learning models in PyTorch but also deploy PyTorch models to production using expert tips and techniques. Finally, you'll get to grips with training large models efficiently in a distributed manner, searching neural architectures effectively with AutoML, and rapidly prototyping models using PyTorch and fast.ai. By the end of this PyTorch book, you'll be able to perform complex deep learning tasks using PyTorch to build smart artificial intelligence models. What you will learn Implement text and music generating models using PyTorch Build a deep Q-network (DQN) model in PyTorch Export universal PyTorch models using Open Neural Network Exchange (ONNX) Become well-versed with rapid prototyping using PyTorch with fast.ai Perform neural architecture search effectively using AutoML Easily interpret machine learning (ML) models written in PyTorch using Captum Design ResNets, LSTMs, Transformers, and more using PyTorch Find out how to use PyTorch for distributed training using the torch.distributed API Who this book is forThis book is for data scientists, machine learning researchers, and deep learning practitioners looking to implement advanced deep learning paradigms using PyTorch 1.x. Working knowledge of deep learning with Python programming is required.

One-stop solution for NLP practitioners, ML developers, and data scientists to build effective NLP systems that can perform real-world complicated tasks Key Features Apply deep learning algorithms and techniques such as BiLSTMS, CRFs, BPE and more using TensorFlow 2 Explore applications like text generation, summarization, weakly supervised labelling and more Read cutting edge material with seminal papers provided in the GitHub repository with full working code Book DescriptionRecently, there have been tremendous advances in NLP, and we are now moving from research labs into practical applications. This book comes with a perfect blend of both the theoretical and practical aspects of trending and complex NLP techniques. The book is focused on innovative applications in the field of NLP, language generation, and dialogue systems. It helps you apply the concepts of pre-processing text using techniques such as tokenization, parts of speech tagging, and lemmatization using popular libraries such as Stanford NLP and SpaCy. You will build Named Entity Recognition (NER) from scratch using Conditional Random Fields and Viterbi Decoding on top of RNNs. The book covers key emerging areas such as generating text for use in sentence completion and text summarization, bridging images and text by generating captions for images, and managing dialogue aspects of chatbots. You will learn how to apply transfer learning and fine-tuning using TensorFlow 2. Further, it covers practical techniques that can simplify the labelling of textual data. The book also has a working code that is adaptable to your use cases for each tech piece. By the end of the book, you will have an advanced knowledge of the tools, techniques and deep learning architecture used to solve complex NLP problems. What you will learn Grasp important pre-steps in building NLP applications like POS tagging Use transfer and weakly supervised learning using libraries like Snorkel Do sentiment analysis using BERT Apply encoder-decoder NN architectures and beam search for summarizing texts Use Transformer models with attention to bring images and text together Build apps that generate captions and answer questions about images using custom Transformers Use advanced TensorFlow techniques like learning rate annealing, custom layers, and custom loss functions to build the latest DeepNLP models Who this book is forThis is not an introductory book and assumes the reader is familiar with basics of NLP and has fundamental Python skills, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra. The readers who can benefit the most from this book include intermediate ML developers who are familiar with the basics of supervised learning and deep learning techniques and professionals who already use TensorFlow/Python for purposes such as data science, ML, research, analysis, etc.

bridges ML and Optimisation; discusses optimisation techniques to improve ML algorithms for big data problems; identifies key research areas to solve large-scale machine learning problems; identifies recent research directions to solve major areas to tackle the challenge

Get to grips with building powerful deep learning models using PyTorch and scikit-learn Key Features Learn how you can speed up the deep learning process with one-shot learning Use Python and PyTorch to build state-of-the-art one-shot learning models Explore architectures such as Siamese networks, memory-augmented neural networks, model-agnostic meta-learning, and discriminative k-shot learning Book DescriptionOne-shot learning has been an active field of research for scientists trying to develop a cognitive machine that mimics human learning. With this book, you'll explore key approaches to one-shot learning, such as metrics-based, model-based, and optimization-based techniques, all with the help of practical examples. Hands-On One-shot Learning with Python will guide you through the exploration and design of deep learning models that can obtain information about an object from one or just a few training samples. The book begins with an overview of deep learning and one-shot learning and then introduces you to the different methods you can use to achieve it, such as deep learning architectures and probabilistic models. Once you've got to grips with the core principles, you'll explore real-world examples and implementations of one-shot learning using PyTorch 1.x on datasets such as Omniglot and MiniImageNet. Finally, you'll explore generative modeling-based methods and discover the key considerations for building systems that exhibit human-level intelligence. By the end of this book, you'll be well-versed with the different one- and few-shot learning methods and be able to use them to build your own deep learning models. What you will learn Get to grips with the fundamental concepts of one- and few-shot learning Work with different deep learning architectures for one-shot learning Understand when to use one-shot and transfer learning, respectively Study the Bayesian network approach for one-shot learning Implement one-shot learning approaches based on metrics, models, and optimization in PyTorch Discover different optimization algorithms that help to improve accuracy even with smaller volumes of data Explore various one-shot learning architectures based on classification and regression Who this book is forIf you're an AI researcher or a machine learning or deep learning expert looking to explore one-shot learning, this book is for you. It will help you get started with implementing various one-shot techniques to train models faster. Some Python programming experience is necessary to understand the concepts covered in this book.

An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithms Key Features Covers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithm Learn how to implement algorithms with code by following examples with line-by-line explanations Explore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrations Book DescriptionWith significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit. In addition to exploring RL basics and foundational concepts such as Bellman equation, Markov decision processes, and dynamic programming algorithms, this second edition dives deep into the full spectrum of value-based, policy-based, and actor-critic RL methods. It explores state-of-the-art algorithms such as DQN, TRPO, PPO and ACKTR, DDPG, TD3, and SAC in depth, demystifying the underlying math and demonstrating implementations through simple code examples. The book has several new chapters dedicated to new RL techniques, including distributional RL, imitation learning, inverse RL, and meta RL. You will learn to leverage stable baselines, an improvement of OpenAI's baseline library, to effortlessly implement popular RL algorithms. The book concludes with an overview of promising approaches such as meta-learning and imagination augmented agents in research. By the end, you will become skilled in effectively employing RL and deep RL in your real-world projects. What you will learn Understand core RL concepts including the methodologies, math, and code Train an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI Gym Train an agent to play Ms Pac-Man using a Deep Q Network Learn policy-based, value-based, and actor-critic methods Master the math behind DDPG, TD3, TRPO, PPO, and many others Explore new avenues such as the distributional RL, meta RL, and inverse RL Use Stable Baselines to train an agent to walk and play Atari games Who this book is forIf you're a machine learning developer with little or no experience with neural networks interested in artificial intelligence and want to learn about reinforcement learning from scratch, this book is for you. Basic familiarity with linear algebra, calculus, and the Python programming language is required. Some experience with TensorFlow would be a plus.