Deep learning systems : algorithms, compilers, and processors for large-scale production / Andres Rodriguez

Von:

Rodriguez-Perez, Andres Felipe, 1980- [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Buch (Online)Sprache: Englisch Reihen: Synthesis lectures on computer architecture ; #53Verlag: San Rafael : Morgan & Claypool Publishers, 2020Beschreibung: 1 Online-Ressource (267 p)ISBN:

1681739674
9781681739670

Schlagwörter:

Andere physische Formen: 9781681739687. | 9781681739663. | Erscheint auch als: Deep Learning Systems : Algorithms, Compilers, and Processors for Large-Scale Production. Druck-Ausgabe San Rafael : Morgan & Claypool Publishers,c2020DDC-Klassifikation:

006.3/1

LOC-Klassifikation:

Q325.5

Online-Ressourcen:

Zugang im Netz des KIT

Zusammenfassung: 1. Introduction -- 1.1. Deep learning in action -- 1.2. AI, ML, NN, and DL -- 1.3. Brief history of neural networks -- 1.4. Types of learning -- 1.5. Types of topologies -- 1.6. Training and serving a simple neural network -- 1.7. Memory and computational analysis -- 1.8. Hardware design considerations -- 1.9. Software stack -- 1.10. NotationZusammenfassung: 2. Building blocks -- 2.1. Activation functions -- 2.2. Affine -- 2.3. Convolution -- 2.4. Pooling -- 2.5. Recurrent units -- 2.6. Normalization -- 2.7. Embeddings -- 2.8. Attention -- 2.9. DropoutZusammenfassung: 3. Models and applications -- 3.1. Recommender systems topologies -- 3.2. Computer vision topologies -- 3.3. Natural language processing topologies -- 3.4. Reinforcement learning algorithmsZusammenfassung: 4. Training a model -- 4.1. Generalizing from training to production datasets -- 4.2. Weight initialization -- 4.3. Optimization algorithms: minimizing the cost -- 4.4. Backpropagation -- 4.5. Training techniques -- 4.6. Transfer learning via fine-tuning. -- 4.7. Training with limited memoryZusammenfassung: 5. Distributed training -- 5.1. Data parallelism -- 5.2. Model parallelism -- 5.3. Federated learning -- 5.4. Collective communication primitivesZusammenfassung: 6. Reducing the model size -- 6.1. Numerical formats -- 6.2. Quantization methodology -- 6.3. Pruning and compression -- 6.4. Knowledge distillationZusammenfassung: 7. Hardware -- 7.1. Moore, Dennard, and Amdahl -- 7.2. Memory and bandwidth -- 7.3. Roofline modeling -- 7.4. Processor designs -- 7.5. High-performance interconnects -- 7.6. Processors in Production -- 7.7. Platforms strengths and challenges -- 7.8. Evaluating devices and platformsZusammenfassung: 8. Compiler optimizations -- 8.1. Language types -- 8.2. Front-end, middle-end, and back-end compilation phases -- 8.3. LLVM -- 8.4. Hardware-independent optimizations -- 8.5. Hardware-dependent optimizationsZusammenfassung: 9. Frameworks and compilers -- 9.1. Frameworks -- 9.2. TensorFlow -- 9.3. PyTorch -- 9.4. TVM -- 9.5. PlaidML -- 9.6. Glow -- 9.7. XLA -- 9.8. MLIR -- 9.9. OthersZusammenfassung: 10. Opportunities and challenges -- 10.1. Machine learning for DL systems -- 10.2. Democratizing DL platforms -- 10.3. Security -- 10.4. Interpretability -- 10.5. Society impact -- 10.6. Concluding remarks.Zusammenfassung: This book describes deep ...PPN: PPN: 175655496XPackage identifier: Produktsigel: BSZ-4-NLEBK-KAUB | ZDB-4-NLEBK

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