Deep Learning Step-by-Step : Guide for Students, Entrepreneurs, Business Leaders & the Curious
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
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 networks
- Neurons, weights & biases
- Activation functions (Sigmoid, Tanh, ReLU)
- Network architectures (CNNs, RNNs, LSTMs)
- Learning processes (gradient descent, backpropagation)
- Depth & complexity
- Overfitting & underfitting
- Regularization techniques
- Cross-validation
- Optimization algorithms (SGD, Adam, RMSprop)
- Frameworks (e.g., Python, PyTorch, TensorFlow)
- Data quality
- Data collection strategies
- Datasets (MNIST, CIFAR, ImageNet)
- Data augmentation
- Large-scale data management
- Image recognition & classification
- Object detection
- Natural Language Processing (NLP)
- Sentiment analysis
- Machine translation
- Speech recognition
- Music generation
- Time series forecasting
- Generative Adversarial Networks (GANs)
- Reinforcement Learning (RL)
- Healthcare (diagnostics, personalized medicine)
- Finance (algorithmic trading, asset management)
- Retail and E-commerce (recommendation systems, personalization)
- Manufacturing & Supply Chain
- Internet of Things (IoT) (smart devices, energy management, security)
- Data privacy & security
- Bias and fairness
- Misinformation & deepfakes
- Surveillance and privacy concerns
- Social implications of AI
- AI governance & regulation
- Explainable AI (XAI)
- Deep learning for social good
- Emerging roles in deep learning
- Skills for deep learning careers
- Deep learning's impact on jobs
- Monetizing deep learning (licensing, AIaaS)
- Funding deep learning startups
- Deep learning & corporate strategy
- Marketing deep learning innovations
- Trends & emerging technologies
- Quantum computing & AI
- Next-generation AI models & agentic deep learning
- Sub-fields beyond deep learning