Gain a theoretical understanding to the most popular libraries in deep reinforcement learning (deep RL). This new edition focuses on the latest advances in deep RL using a learn-by-coding approach, allowing readers to assimilate and replicate the latest research in this field.
New agent environments ranging from games, and robotics to finance are explained to help you try different ways to apply reinforcement learning. A chapter on multi-agent reinforcement learning β¦
ΠΠ½Π³Π»ΠΈΠΉΡΠΊΠΈΠΉ ZIP (pdf+epub)Π‘ΠΎΠ±Π΅ΡΠ΅Π΄ΠΎΠ²Π°Π½ΠΈΡ ΠΏΠΎ ΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΡΡΠ΅ΠΌ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ — ΡΠ°ΠΌΡΠ΅ ΡΠ»ΠΎΠΆΠ½ΡΠ΅. ΠΡΠ»ΠΈ Π½ΡΠΆΠ½ΠΎ ΠΏΠΎΠ΄Π³ΠΎΡΠΎΠ²ΠΈΡΡΡΡ ΠΊ ΡΠ°ΠΊΠΎΠΌΡ, ΠΊΠ½ΠΈΠ³Π° ΡΠΎΠ·Π΄Π°Π½Π° ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎ Π΄Π»Ρ Π²Π°Ρ.
Π’Π°ΠΊΠΆΠ΅ ΠΎΠ½Π° ΠΏΠΎΠΌΠΎΠΆΠ΅Ρ Π²ΡΠ΅ΠΌ, ΠΊΡΠΎ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΡΠ΅ΡΡΡ ΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΡΠΈΡΡΠ΅ΠΌ ΠΠ, Π±ΡΠ΄Ρ ΡΠΎ Π½ΠΎΠ²ΠΈΡΠΊΠΈ ΠΈΠ»ΠΈ ΠΎΠΏΡΡΠ½ΡΠ΅ ΠΈΠ½ΠΆΠ΅Π½Π΅ΡΡ.
Π§ΡΠΎ Π²Π½ΡΡΡΠΈ?
ΠΠ½ΠΈΠ³Π° ΡΠ°ΡΡΠΊΠ°Π·ΡΠ²Π°Π΅Ρ ΠΎ ΠΏΡΠΎΠ΄Π²ΠΈΠ½ΡΡΡΡ ΠΏΡΠΈΡΠΌΠ°Ρ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΈ Π½Π°ΡΠΊΠΈ ΠΎ Π΄Π°Π½Π½ΡΡ (data science) Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ Π·Π°Π΄Π°Ρ, ΡΠ΅ΡΠ°Π΅ΠΌΡΡ Π½Π° Π²ΡΠ΅ΠΌΠΈΡΠ½ΠΎ ΠΈΠ·Π²Π΅ΡΡΠ½ΠΎΠΉ ΠΏΠ»Π°ΡΡΠΎΡΠΌΠ΅ Kaggle. ΠΠΎΠΊΠ°Π·Π°Π½ΠΎ (Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ ΡΠ²Π»Π΅ΠΊΠ°ΡΠ΅Π»ΡΠ½ΡΡ ΠΈΠ½ΡΠ΅ΡΠ²ΡΡ Ρ Kaggle-Π³ΡΠΎΡΡΠΌΠ΅ΠΉΡΡΠ΅ΡΠ°ΠΌΠΈ), ΠΊΠ°ΠΊ ΡΡΡΡΠΎΠ΅Π½Π° ΠΏΠ»Π°ΡΡΠΎΡΠΌΠ° Kaggle ΠΈ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠΌΡΠ΅ Π½Π° Π½Π΅ΠΉ ΡΠΎΡΠ΅Π²Π½ΠΎΠ²Π°Π½ΠΈΡ. ΠΠ·Π»ΠΎΠΆΠ΅Π½Π½ΡΠΉ ΠΌΠ°ΡΠ΅ΡΠΈΠ°Π» ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΡΠ°Π·Π²ΠΈΡΡ Π½Π΅ΠΎΠ±Ρ ΠΎΠ΄ΠΈΠΌΡΠ΅ Π½Π°Π²ΡΠΊΠΈ ΠΈ ΡΠΎΠ±ΡΠ°ΡΡ ΠΏΠΎΡΡΡΠΎΠ»ΠΈΠΎ ΠΏΠΎ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠΌΡ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, Π°Π½Π°Π»ΠΈΠ·Ρ Π΄Π°Π½Π½ΡΡ , ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ΅ Π΅ΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠ·ΡΠΊΠ°, ΡΠ°Π±ΠΎΡΠ΅ Ρ ΠΌΠ½ΠΎΠΆΠ΅ΡΡΠ²Π°ΠΌΠΈ. ΠΠΎΠ΄ΠΎΠ±ΡΠ°Π½ ΡΠ½ΠΈΠΊΠ°Π»ΡΠ½ΡΠΉ ΠΏΡΠ» Π·Π°Π΄Π°Ρ, ΠΎΡ Π²Π°ΡΡΠ²Π°ΡΡΠΈΡ ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠΎΠ½Π½ΡΠ΅ ΠΈ ΠΎΡΠ΅Π½ΠΎΡΠ½ΡΠ΅ β¦
Π ΡΡΡΠΊΠΈΠΉ DJVUΠΠ½ΠΈΠ³Π° ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ ΠΎΠΊΠΎΠ»ΠΎ 200 Π·Π°Π΄Π°Ρ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, ΡΠ°ΠΊΠΈΡ ΠΊΠ°ΠΊ Π·Π°Π³ΡΡΠ·ΠΊΠ° ΠΈ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠ° ΡΠ΅ΠΊΡΡΠΎΠ²ΡΡ ΠΈΠ»ΠΈ ΡΠΈΡΠ»ΠΎΠ²ΡΡ Π΄Π°Π½Π½ΡΡ , ΠΎΡΠ±ΠΎΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΈ ΠΌΠ½ΠΎΠ³ΠΈΠ΅ Π΄ΡΡΠ³ΠΈΠ΅. Π Π°ΡΡΠΌΠΎΡΡΠ΅Π½Π° ΡΠ°Π±ΠΎΡΠ° Ρ ΡΠ·ΡΠΊΠΎΠΌ Python, Π±ΠΈΠ±Π»ΠΈΠΎΡΠ΅ΠΊΠ°ΠΌΠΈ pandas ΠΈ scikit-learn. ΠΠΎΠ΄Ρ ΠΏΡΠΈΠΌΠ΅ΡΠΎΠ² ΠΌΠΎΠΆΠ½ΠΎ Π²ΡΡΠ°Π²Π»ΡΡΡ, ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½ΡΡΡ ΠΈ Π°Π΄Π°ΠΏΡΠΈΡΠΎΠ²Π°ΡΡ, ΡΠΎΠ·Π΄Π°Π²Π°Ρ ΡΠΎΠ±ΡΡΠ²Π΅Π½Π½ΠΎΠ΅ ΠΏΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΠ΅. ΠΡΠΈΠ²Π΅Π΄Π΅Π½Ρ ΡΠ΅ΡΠ΅ΠΏΡΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ: Π²Π΅ΠΊΡΠΎΡΠΎΠ², ΠΌΠ°ΡΡΠΈΡ ΠΈ ΠΌΠ°ΡΡΠΈΠ²ΠΎΠ²; Π΄Π°Π½Π½ΡΡ ΠΈΠ· CSV, JSON, SQL, Π±Π°Π· Π΄Π°Π½Π½ΡΡ , ΠΎΠ±Π»Π°ΡΠ½ΡΡ Ρ ΡΠ°Π½ΠΈΠ»ΠΈΡ ΠΈ Π΄ΡΡΠ³ΠΈΡ ΠΈΡΡΠΎΡΠ½ΠΈΠΊΠΎΠ²; ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΠΈ Π΄Π°Π½Π½ΡΡ , ΡΠ΅ΠΊΡΡΠ°, ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ, Π΄Π°Ρ ΠΈ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ; ΡΠΌΠ΅Π½Ρ-ΡΠ΅Π½ΠΈΡ β¦
Π ΡΡΡΠΊΠΈΠΉ PDFΠΠ°Π½Π½ΡΠΉ ΠΊΠ»Π°ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΡΡΡΠ΄ ΡΠΎΠ΄Π΅ΡΠΆΠΈΡ ΠΎΠ±ΡΡΠΎΡΡΠ΅Π»ΡΠ½ΠΎΠ΅ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠ΅ Π²Π²Π΅Π΄Π΅Π½ΠΈΠ΅ Π² ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅ (Π²ΠΊΠ»ΡΡΠ°Ρ Π³Π»ΡΠ±ΠΎΠΊΠΎΠ΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅), ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΠΌΠΎΠ΅ ΡΠΊΠ²ΠΎΠ·Ρ ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½ΡΡΡΡΡ ΠΏΡΠΈΠ·ΠΌΡ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΠΎΠ³ΠΎ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ Π±Π°ΠΉΠ΅ΡΠΎΠ²ΡΠΊΠΎΠΉ ΡΠ΅ΠΎΡΠΈΠΈ ΠΏΡΠΈΠ½ΡΡΠΈΡ ΡΠ΅ΡΠ΅Π½ΠΈΠΉ. ΠΠΊΠ»ΡΡΠ΅Π½ Π±Π°Π·ΠΎΠ²ΡΠΉ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΠΉ Π°ΠΏΠΏΠ°ΡΠ°Ρ (Π² Ρ. Ρ. ΡΠ»Π΅ΠΌΠ΅Π½ΡΡ Π»ΠΈΠ½Π΅ΠΉΠ½ΠΎΠΉ Π°Π»Π³Π΅Π±ΡΡ ΠΈ ΡΠ΅ΠΎΡΠΈΠΈ ΠΎΠΏΡΠΈΠΌΠΈΠ·Π°ΡΠΈΠΈ), ΠΎΡΠ½ΠΎΠ²Ρ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ Ρ ΡΡΠΈΡΠ΅Π»Π΅ΠΌ (Π²ΠΊΠ»ΡΡΠ°Ρ Π»ΠΈΠ½Π΅ΠΉΠ½ΡΡ ΠΈ Π»ΠΎΠ³ΠΈΡΡΠΈΡΠ΅ΡΠΊΡΡ ΡΠ΅Π³ΡΠ΅ΡΡΠΈΡ ΠΈ Π³Π»ΡΠ±ΠΎΠΊΠΈΠ΅ Π½Π΅ΠΉΡΠΎΠ½Π½ΡΠ΅ ΡΠ΅ΡΠΈ), Π° ΡΠ°ΠΊΠΆΠ΅ Π±ΠΎΠ»Π΅Π΅ ΡΠ»ΠΎΠΆΠ½ΡΠ΅ ΡΠ΅ΠΌΡ (Π² Ρ. Ρ. ΠΏΠ΅ΡΠ΅Π½ΠΎΡ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΠΈ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅ Π±Π΅Π· ΡΡΠΈΡΠ΅Π»Ρ). Π£ΠΏΡΠ°ΠΆΠ½Π΅Π½ΠΈΡ Π² ΠΊΠΎΠ½ΡΠ΅ Π³Π»Π°Π² ΠΏΠΎΠΌΠΎΠ³ΡΡ ΡΠΈΡΠ°ΡΠ΅Π»ΡΠΌ β¦
Π ΡΡΡΠΊΠΈΠΉ PDFΠΠ½ΠΈΠ³Π° ΡΠ²Π»ΡΠ΅ΡΡΡ ΠΏΠ΅ΡΠ²ΡΠΌ ΡΠΎΠΌΠΎΠΌ ΠΊ ΠΊΠ½ΠΈΠ³Π΅ "ΠΠ΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΠΎΠ΅ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅. ΠΠΎΠΏΠΎΠ»Π½ΠΈΡΠ΅Π»ΡΠ½ΡΠ΅ ΡΠ΅ΠΌΡ: ΠΏΡΠ΅Π΄ΡΠΊΠ°Π·Π°Π½ΠΈΠ΅, ΠΏΠΎΡΠΎΠΆΠ΄Π΅Π½ΠΈΠ΅, ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΈΠ΅, Π΄Π΅ΠΉΡΡΠ²ΠΈΠ΅".
ΠΠΎΠΏΠΎΠ»Π½ΡΡ ΡΠ°Π½Π΅Π΅ ΠΈΠ·Π΄Π°Π½Π½ΡΡ ΠΊΠ½ΠΈΠ³Ρ «ΠΠ΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΠΎΠ΅ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅. ΠΠ²Π΅Π΄Π΅Π½ΠΈΠ΅», ΡΡΠΎΡ ΠΊΠ»Π°ΡΡΠΈΡΠ΅ΡΠΊΠΈΠΉ ΡΡΡΠ΄ Π·Π½Π°ΠΊΠΎΠΌΠΈΡ ΡΠΈΡΠ°ΡΠ΅Π»Ρ Ρ Π΄Π΅ΡΠ°Π»ΡΠΌΠΈ ΡΠ°ΠΌΡΡ Π°ΠΊΡΡΠ°Π»ΡΠ½ΡΡ ΡΠ΅ΠΎΡΠΈΠΉ ΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, Π²ΠΊΠ»ΡΡΠ°Ρ Π³Π»ΡΠ±ΠΎΠΊΠΈΠ΅ ΠΏΠΎΡΠΎΠΆΠ΄Π°ΡΡΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ, Π³ΡΠ°ΡΠΎΠ²ΡΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ, Π±Π°ΠΉΠ΅ΡΠΎΠ²ΡΠΊΠΈΠΉ Π²ΡΠ²ΠΎΠ΄, ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅ Ρ ΠΏΠΎΠ΄ΠΊΡΠ΅ΠΏΠ»Π΅Π½ΠΈΠ΅ΠΌ ΠΈ ΠΏΡΠΈΡΠΈΠ½Π½ΠΎΡΡΡ. ΠΠ»ΡΠ±ΠΎΠΊΠΎΠ΅ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅ ΠΈΠ·Π»Π°Π³Π°Π΅ΡΡΡ Π² ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ΅ Π±ΠΎΠ»Π΅Π΅ ΡΠΈΡΠΎΠΊΠΎΠ³ΠΎ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡΠ°, Π° ΠΏΠΎΠ΄Ρ ΠΎΠ΄Ρ ΠΊ Π³Π»ΡΠ±ΠΎΠΊΠΎΠΌΡ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ ΡΠ½ΠΈΡΠΈΡΠΈΡΠΎΠ²Π°Π½Ρ Ρ ΠΏΠΎΠ΄Ρ ΠΎΠ΄Π°ΠΌΠΈ ΠΊ Π²Π΅ΡΠΎΡΡΠ½ΠΎΡΡΠ½ΠΎΠΌΡ ΠΌΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΠΈ Π²ΡΠ²ΠΎΠ΄Ρ.
This book presents a unified theory of random matrices for applications in machine learning, offering a large-dimensional data vision that exploits concentration and universality phenomena. This enables a precise understanding, and possible improvements, of the core mechanisms at play in real-world machine learning algorithms. The book opens with a thorough introduction to the theoretical basics of random matrices, which serves as a support to a wide scope of applications ranging β¦
ΠΠ½Π³Π»ΠΈΠΉΡΠΊΠΈΠΉ PDFMachine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles.
This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you'll learn how β¦
ΠΠ½Π³Π»ΠΈΠΉΡΠΊΠΈΠΉ EPUBServing patterns enable data science and ML teams to bring their models to production. Most ML models are not deployed for consumers, so ML engineers need to know the critical steps for how to serve an ML model.
This book will cover the whole process, from the basic concepts like stateful and stateless serving to the advantages and challenges of each. Batch, real-time, and continuous model serving techniques will also β¦
ΠΠ½Π³Π»ΠΈΠΉΡΠΊΠΈΠΉ EPUBΠΠ½ΠΈΠ³Π° ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»ΡΠ΅Ρ ΡΠΎΠ±ΠΎΠΉ Π²Π²Π΅Π΄Π΅Π½ΠΈΠ΅ Π² ΠΊΠ»Π°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΠΎΠ΅ Π·ΡΠ΅Π½ΠΈΠ΅. ΠΠ²ΡΠΎΡ ΠΏΠΎΠΊΠ°Π·ΡΠ²Π°Π΅Ρ, ΠΊΠ°ΠΊ ΠΌΠΎΠΆΠ½ΠΎ ΡΠ°Π·Π»ΠΎΠΆΠΈΡΡ Π½Π° ΡΠ°ΡΡΠΈ ΠΈ ΡΠ΅ΡΠΈΡΡ ΡΠ»ΠΎΠΆΠ½ΡΠ΅ Π·Π°Π΄Π°ΡΠΈ Π² ΡΡΠΎΠΉ ΡΡΠ΅ΡΠ΅ Ρ ΠΏΠΎΠΌΠΎΡΡΡ Π²ΡΠ΅Π³ΠΎ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΡ ΠΏΡΠΎΡΡΡΡ ΡΡΡΠΎΠΊ ΠΊΠΎΠ΄Π°.
Machine Vision Toolbox for MATLAB – ΠΎΡΠΊΡΡΡΠΎΠ΅ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ½ΠΎΠ΅ ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠ΅Π½ΠΈΠ΅, ΠΊΠΎΡΠΎΡΠΎΠ΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΠ΅Ρ ΡΠΈΡΠ°ΡΠ΅Π»Ρ Π»Π΅Π³ΠΊΠΎ ΠΏΡΠΈΠΌΠ΅Π½ΡΡΡ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΊΠΎΠ½ΡΠ΅ΠΏΡΠΈΠΈ Π½Π° ΠΏΡΠ°ΠΊΡΠΈΠΊΠ΅ ΠΈ ΡΠ°Π±ΠΎΡΠ°ΡΡ Ρ Π½Π΅ΡΡΠΈΠ²ΠΈΠ°Π»ΡΠ½ΡΠΌΠΈ Π·Π°Π΄Π°ΡΠ°ΠΌΠΈ. Π Π°ΡΠΊΡΡΠ²Π°ΡΡΡΡ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΎΡΠ½ΠΎΠ²Ρ Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ², Π° ΠΌΠ½ΠΎΠ³ΠΎΡΠΈΡΠ»Π΅Π½Π½ΡΠ΅ ΠΏΡΠΈΠΌΠ΅ΡΡ ΠΊΠΎΠ΄Π° ΠΈΠ»Π»ΡΡΡΡΠΈΡΡΡΡ Π΅Π³ΠΎ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅.
Π ΡΠΈΡΠ»Π΅ ΡΠ°ΡΡΠΌΠ°ΡΡΠΈΠ²Π°Π΅ΠΌΡΡ ΡΠ΅ΠΌ:
Machine learningβalso known as data mining or predictive analyticsβis a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information.
Machine Learning for Business Analytics: Concepts, Techniques, and Applications with Analytic Solver® Data Mining provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for β¦
ΠΠ½Π³Π»ΠΈΠΉΡΠΊΠΈΠΉ PDFComputer vision is constantly evolving, and this book has been updated to reflect new topics that have emerged in the field since the first edition’s publication. All code used in the book has also been fully updated.
This second edition features new material covering image manipulation practices, image segmentation, feature extraction, and object identification using real-life scenarios to help reinforce each concept. These topics are essential for building advanced computer β¦
ΠΠ½Π³Π»ΠΈΠΉΡΠΊΠΈΠΉ EPUBΠΡΡΠΎΡΠΈΡ ΠΌΠ°ΡΠΈΠ½Π½ΠΎΠ³ΠΎ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, ΠΎΡ ΡΠ΅ΠΎΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ 50-Ρ Π³ΠΎΠ΄ΠΎΠ² Π΄ΠΎ Π½Π°ΡΠΈΡ Π΄Π½Π΅ΠΉ, Π² ΠΈΠ·Π»ΠΎΠΆΠ΅Π½ΠΈΠΈ Π²Π΅Π΄ΡΡΠ΅Π³ΠΎ ΠΌΠΈΡΠΎΠ²ΠΎΠ³ΠΎ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠ° ΠΏΠΎ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ Π½Π΅ΠΉΡΠΎΡΠ΅ΡΠ΅ΠΉ ΠΈ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ° Π’Π΅ΡΡΠ΅Π½ΡΠ° Π‘Π΅ΠΉΠ½ΠΎΠ²ΡΠΊΠΈ.
ΠΠ²ΡΠΎΡ ΡΠ°ΡΡΠΊΠ°Π·ΡΠ²Π°Π΅Ρ ΠΎΠ±ΠΎ Π²ΡΠ΅Ρ ΠΊΠ»ΡΡΠ΅Π²ΡΡ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡΡ ΠΈ ΡΠΎΠ±ΡΡΠΈΡΡ , ΠΏΠΎΠ²Π»ΠΈΡΠ²ΡΠΈΡ Π½Π° ΡΠ°Π·Π²ΠΈΡΠΈΠ΅ ΡΡΠΎΠΉ ΡΠ΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ, Π½Π°ΡΠΈΠ½Π°Ρ Ρ ΠΏΠ΅ΡΠ²ΡΡ ΠΊΠΎΠ½Π³ΡΠ΅ΡΡΠΎΠ², ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π½ΡΡ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠΌΡ ΡΠ°Π·ΡΠΌΡ ΠΈ Π·Π°ΠΊΠ°Π½ΡΠΈΠ²Π°Ρ Π³Π»ΡΠ±ΠΎΠΊΠΈΠΌ ΠΎΠ±ΡΡΠ΅Π½ΠΈΠ΅ΠΌ ΠΈ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΡΠΌΠΈ, ΠΊΠΎΡΠΎΡΡΠ΅ ΠΎΠ½ΠΎ ΠΏΡΠ΅Π΄ΠΎΡΡΠ°Π²Π»ΡΠ΅Ρ ΡΠ°Π·ΡΠ°Π±ΠΎΡΡΠΈΠΊΠ°ΠΌ ΠΠ.
Π ΡΡΡΠΊΠΈΠΉ EPUB Go, the high-performance language from Google, is rapidly gaining traction in the machine learning world. Its speed, concurrency, and built-in features make it ideal for building efficient, scalable ML models. But where do you start?
This book is written by a seasoned developer and machine learning expert, providing you with practical, hands-on guidance based on real-world experience. After reading this book, you'll be equipped with the knowledge and β¦