Deep learning : a practitioner's approach

By:

Patterson, Josh

Contributor(s):

Gibson, Adam

Material type: Text

9789352136049

Subject(s):

Genre/Form:

Electronic books.

Additional physical formats: No titleDDC classification:

006.31 23 PAT-D

LOC classification:

Q325.5

Online resources:

Access eNYPL

Contents:

A review of machine learning -- Foundations of neural networks and deep learning -- Fundamentals of deep networks -- Major architecture of deep networks -- Building deep networks -- Tuning deep networks -- Tuning specific deep network architectures -- Vectorization -- Using deep learning and DL4J on Spark -- What is artificial intelligence? -- RL4J and reinforcement learning -- Numbers everyone should know -- Neural networks and backpropagation: a mathematical approach -- Using the ND4J API -- Using DataVec -- Working with DL4J from source -- Setting up DL4J projects -- Setting up GPUs for DL4J projects -- Troubleshooting DL4J installations.

Tags from this library: No tags from this library for this title. Log in to add tags.

Average rating: 0.0 (0 votes)

Holdings
Item type	Current library	Collection	Call number	Status	Date due	Barcode	Item holds
Books	IIITD General Stacks	Computer Science and Engineering	006.31 PAT-D (Browse shelf(Opens below))	Available		008723
Books	IIITD Reference	Computer Science and Engineering	REF 006.31 PAT-D (Browse shelf(Opens below))	Checked out	23/07/2025	008722

Total holds: 0

Browsing IIITD shelves, Shelving location: General Stacks, Collection: Computer Science and Engineering Close shelf browser (Hides shelf browser)

Previous								Next
Previous	006.31 MOL-C Causal inference and discovery in python : unlock the secrets of modern causal machine learning with dowhy, econML, pytorch and more	006.31 MOR-A AI and machine learning for on-device development :	006.310 NIE-E Essential math for data science : take control of your data with fundamental linear algebra, probability, and statistics	006.31 PAT-D Deep learning : a practitioner's approach	006.31 PAT-H Hands-on unsupervised learning using python : how to build applied machine learning solutions from unlabeled data	006.31 PRO-A Applied machine learning and AI for engineers : solve business problems that can't be solved algorithmically	006.31 PRU-P Practicing trustworthy machine learning : consistent, transparent, and fair AI pipelines	Next

Includes bibliographical references and index.

There are no comments on this title.

to post a comment.