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Reading list

What is Deep Learning? DL vs ML vs AI Why Deep Learning is So Popular?
Applications of Deep Learning Multi Layer Perceptron Visualizing the Neural Network Understanding Decision Boundary Forward and Backward Propagation Intuition
Applications of Deep Learning Multi Layer Perceptron Visualizing the Neural Network Understanding Decision Boundary Forward and Backward Propagation Intuition
Gradient Descent Algorithm Variants of Gradient Descent Algorithm
Introduction to Loss Function Binary and Categorical Cross Entropy
Why Do We Need Activation Functions? Linear Activation Function Sigmoid and Tanh Function Softmax How to Select Right Activation Function?
Perceptron Weights in Perceptron Introduction to Artificial Neural Network
Understanding Forward Propagation Mathematically Understand Backward Propagation Mathematically Implementing Neural Networks in Python
Problems with Gradient Descent Gradient Descent with Momentum Adagrad and Adadelta
Introduction to Learning Rate Schedulers
Overview of Deep Learning Frameworks Implementing Neural Networks using Keras Functional API in Keras Implementing Neural Networks using Keras Hyperparameter Tuning of MLP in Keras
Understanding Early stopping Understanding Dropout Vanishing and Exploding Gradients Weights Initialization Techniques Implementing Weight Initializing Techniques Batch Normalization Image Augmentation Techniques Image Generator and Fit Generator Model Checkpointing Implementing Model Checkpointing Dealing with Class Imbalance Ensemble Deep Learning
Introduction to GPU and TPU TensorRT
Introduction to Unsupervised Learning How to Solve Unsupervised Learning Problems? Introduction to Autoencoders Implementing Autoencoders
A Beginners Guide to Codeless Deep Learning
TensorFlow Serving Build Deep Learning Models for Android
Introduction to PyTorch and Tensors Mathematical and Matrix Operations in PyTorch Important PyTorch Modules Implement CNN in PyTorch Transfer Learning in PyTorch Working with Text Data in PyTorch Building a RNN model in PyTorch Autoencoders in PyTorch

AMD Introduces Ryzen Pro 8000 and 8040 Series CPUs for Business Computing

K.C. Sabreena Basheer
K.C. Sabreena Basheer Last Updated : 18 Apr, 2024
2 min read

AMD expands its processor lineup with the Ryzen Pro 8000 and 8040 series of CPUs. The 8000 series caters to the needs of business users, while the Ryzen 8040 series offers enhanced AI capabilities. These new CPUs come with features tailored for professional use, aiming to elevate productivity and performance in corporate environments. They mark a significant advancement in business computing solutions, over both both desktop and mobile.

Also Read: Google Unveils Custom Arm-based CPUs for Cloud Computing

AMD Ryzen Pro 8000 and 8040 series, designed for business computing and enhanced AI capabilities.

AMD’s Latest Offering for Business Clients

AMD announces the Ryzen Pro 8000 series CPUs, including the Ryzen Pro 8040 series, designed to meet the demands of modern business computing. These processors introduce advanced features such as NPUs (Neural Processing Units) for accelerated AI processing tasks. This enhances productivity and efficiency for business users.

Enhanced AI Focus

A significant highlight of the Ryzen Pro 8000 series is its AI focus, featuring NPUs for superior AI performance. With up to 39 TOPS (Trillion Operations Per Second) for desktop variants and up to 16 TOPS for mobile variants, AMD aims to deliver cutting-edge AI capabilities for business applications.

Also Read: Intel’s Gaudi 3: Setting New Standards with 40% Faster AI Acceleration than Nvidia H100

Key Features and Performance

The Ryzen Pro 8000 series boasts Zen 4 CPU cores and RDNA 3 graphics architecture, ensuring robust performance across various workloads. Notably, some entry-level models may lack the Ryzen AI feature, catering to different customer requirements.

AMD Ryzen Pro 8000 and 8040 series CPUs performance

Implications for Business Computing

Leading PC manufacturers like Lenovo and HP are set to integrate Ryzen Pro 8000 series processors into their laptops and workstations. The inclusion of NPUs enhances productivity and efficiency, making these CPUs suitable for a wide range of business applications.

Also Read: Game Changer Alert: NVIDIA GeForce RTX 40 SUPER Series GPUs

Future Outlook and Industry Response

AMD’s introduction of the Ryzen Pro 8000 series signifies a step forward in catering to the evolving needs of business users. As the industry embraces digital transformation, AMD’s commitment to innovation positions it as a key player in shaping the future of business computing. With the Ryzen Pro 8000 and 8040 series, AMD continues to redefine productivity standards, empowering businesses with advanced computing solutions.

Our Say

AMD’s Ryzen Pro 8000 and 8040 series CPUs represent a significant advancement in business computing technology. With their enhanced AI capabilities and performance-driven features, these processors are poised to revolutionize the way businesses operate in the digital age. As organizations strive for greater efficiency and productivity, AMD’s Ryzen Pro series stands out as a reliable and innovative solution for meeting the demands of modern business computing.

Follow us on Google News to stay updated with the latest innovations in the world of AI, Data Science, & GenAI.

K.C. Sabreena Basheer
K.C. Sabreena Basheer

Sabreena Basheer is an architect-turned-writer who's passionate about documenting anything that interests her. She's currently exploring the world of AI and Data Science as a Content Manager at Analytics Vidhya.

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