Deep Learning Step-by-Step : Guide for Students, Entrepreneurs, Business Leaders & the Curious (Step By Step Subject Guides) by Mitchell Ng
English | April 23, 2024 | ISBN: N/A | ASIN: B0D2KL8732 | PDF | 2.01 Mb
Demystifying Deep Learning: Your Step-by-Step Guide to AI's Most Powerful Technology
Unlock the potential of deep learning and join the AI revolution with this comprehensive and accessible guide, perfect for students and beginners, entrepreneurs, business leaders, and curious minds alike.
"Deep Learning Step by Step" takes you on a journey from the mechanics, foundation, and concepts of neural networks to cutting-edge applications transforming industries like healthcare, finance, and manufacturing.
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Here's what you'll discover:The Core Concepts: Understand the mechanics, foundations, and concepts behind deep learning, from neurons and activation functions to various network architectures like CNNs and RNNs.Data as the Foundation: Learn how to collect, prepare, and augment data to fuel your deep learning models for optimal performance.Training Secrets Revealed: Explore optimization algorithms, regularization techniques, and best practices for training models.Real-World Applications: Image recognition, natural language processing (NLP), speech recognition & more.Business Applications: Enhance customer experience & optimize operations.Ethical Considerations: Data privacy, algorithmic bias, and the societal impact of AI.Building a Career in AI: Explore emerging roles in deep learning and acquire essential skills (e.g., Python, PyTorch).Frontiers of Deep Learning: Trends like self-supervised learning, agentic models, and quantum computing.__
Concepts & Topics Covered
1. Deep Learning Fundamentals:Neural networksNeurons, weights & biasesActivation functions (Sigmoid, Tanh, ReLU)Network architectures (CNNs, RNNs, LSTMs)Learning processes (gradient descent, backpropagation)Depth & complexityOverfitting & underfittingRegularization techniquesCross-validationOptimization algorithms (SGD, Adam, RMSprop)Frameworks (e.g., Python, PyTorch, TensorFlow)2. Data for Deep Learning
ata qualityData collection strategiesDatasets (MNIST, CIFAR, ImageNet)Data augmentationLarge-scale data management3. Deep Learning Applications:Image recognition & classificationObject detectionNatural Language Processing (NLP)Sentiment analysisMachine translationSpeech recognitionMusic generationTime series forecastingGenerative Adversarial Networks (GANs)Reinforcement Learning (RL)4. Deep Learning in Industries:Healthcare (diagnostics, personalized medicine)Finance (algorithmic trading, asset management)Retail and E-commerce (recommendation systems, personalization)Manufacturing & Supply ChainInternet of Things (IoT) (smart devices, energy management, security)5. Deep Learning Ethics & Society
ata privacy & securityBias and fairnessMisinformation & deepfakesSurveillance and privacy concernsSocial implications of AIAI governance & regulationExplainable AI (XAI)Deep learning for social good6. Career & Business Aspects:Emerging roles in deep learningSkills for deep learning careersDeep learning's impact on jobsMonetizing deep learning (licensing, AIaaS)Funding deep learning startupsDeep learning & corporate strategyMarketing deep learning innovations7. Future of Deep Learning:Trends & emerging technologiesQuantum computing & AINext-generation AI models & agentic deep learningSub-fields beyond deep learning
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