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Accelerating Deep Learning with GPUs Cognitive Class Exam Quiz Answers

Accelerating Deep Learning with GPUs Cognitive Class Certification Answers

Question 1: Which are applications of deep learning in the industry?

  • In Security: face recognition and video surveillance
  • In Media: entertainment and news
  • In Communications: internet service and mobile phones industries
  • All of the above

Question 2: Which is NOT one of the main phases of a deep learning pipeline?

  • Preprocessing input data
  • Feature selection
  • Training the deep learning model
  • Inference and Deployment of the model

Question 1: Which one of the following statements is NOT TRUE about GPU?

  • The GPU parallelism feature reduces the computation time of the dot product of big matrices.
  • GPUs are faster than CPUs in loading small chunks of data.
  • GPUs are very good where the same code runs on different sections of the same array.
  • GPUs are the proper use for parallelism operations on matrices.

Question 2: “GPUs have many cores, sometimes up to 1000 cores, so they can handle many computations in parallel.” Is this statement TRUE or FALSE?

  • TRUE
  • FALSE

Question 1: Which are the most popular hardware accelerators in use today?

  • FPGAs (programmable or customizable hardware)
  • AMD cards with OpenCL software
  • Tensorflow Processing Units (TPUs)
  • NVIDIA GPUs with CUDA software
  • All of the above

Question 2: “In some situations, your data might be very huge in terms of volume and computation in such a way that you need a really large computational system to handle it. In this case, you need a cluster of GPUs to distribute the whole computational workload.” Is this statement TRUE or FALSE?

  • TRUE
  • FALSE

Question 3: Which of the following statement is TRUE about deep learning in the cloud?

  • Building cloud-based deep learning could be really costly if you need to train models for more than 1000 hours.
  • You need to analyze your data on-premise, when your data is sensitive and you may not feel comfortable to upload it into public clouds.
  • If your data is big, use a fast enough single GPU to do experiments with sample data to verify many things before going full scale.
  • All of the above.

Question 1: “The main objective of Distributed Deep Learning is to distribute the workload of deep learning on multiple GPUs on a node.” Is this statement TRUE or FALSE about DDL?

  • TRUE
  • FALSE

Question 2: Which of the following statement is TRUE about using DDL (Distributed Deep Learning) for training a model?

  • DDL is comprised of a bunch of software algorithms that provide the parallelization of computation across hundreds of GPU accelerators attached to dozens of servers.
  • DDL distributes deep learning training across large numbers of servers.
  • DDL reduces training times for large models with large data sets.
  • All of the above.

Question 3: What are the four workspaces in IBM PowerAI Vision?

  • My Data Sets, My DL Tasks, My Trained Models, My Web APIs
  • My Pictures, My Documents, My Downloads, My Data Sets
  • My Computer, My Desk, My Chair, My Tasks
  • My DL Tasks, My Trained Models, My Pictures, My Website

Question 4: After training and deploying your Object Recognition model, what format is the response of your web API?

  • TXT
  • JSON
  • JPEG
  • PNG

Question 5: What are the two types of Deep Learning tasks that IBM PowerAI Vision provides?

  • Voice Recognition, Natural Language Processing
  • Regression, Time Series Forecasting
  • Clustering, Image Segmentation
  • Classification, Object Detection

Question 1: Which statement is NOT one of the main reasons for the increased popularity of deep learning today?

  • The dramatic increases in computer processing capabilities.
  • The increase in the quality of images.
  • The availability of massive amounts of data for training computer systems.
  • The advances in machine learning algorithms and research.

Question 2: What is the problem with traditional approaches for image classification?

  • Extending the features to other types of images is not easy.
  • The feature selection process is very ineffective.
  • The process of selecting and using the best features is a time-consuming task.
  • All of the above.

Question 3: Which one of the following characteristics of Convolutional Neural Network is the most important in Image Classification?

  • No need to find or select features.
  • Working with sound data.
  • Low number of layers.
  • All of the above.

Question 4: Which of the following definitions is what the “inference” part of the deep learning pipeline does?

  • Finding the best feature set for classification.
  • Using the trained model for classifying a new image based on its similarity to the trained model.
  • Feeding an untrained network with a big dataset of images.
  • Converting the images to a readable and proper format for the network.

Question 5: What is/are the main reason/s for the deep learning pipeline being so slow?

  • Training a Deep Neural Network is basically a slow process.
  • Building a deep neural network is an iterative process for data scientists, that is, it needs optimization and tuning and data scientists need to run it many times to make it ready to be used.
  • The trained model needs to get updated sometimes, for example, because new data is added to the training set.

Question 6: Why is acceleration of the deep learning pipeline very desirable for data scientists?

  • It reduces the number of pixels that the kernel should add.
  • It makes the inference part of the deep learning pipeline much faster.
  • It causes better feature extraction and selection.
  • Data scientists can train a model more times and make it much more accurate.
  • None of the above.

Question 7: Why is “training” of deep learning the most time-consuming part of the pipeline?

  • There are many matrix multiplications in the process.
  • Neural Networks have usually many weights, which should get updated in each iteration, and it involves expensive computations.
  • Training is an iterative process.
  • All of the above.

Question 8: Which one of the following statements is NOT TRUE about CPU?

  • CPU is not the proper use for high parallelism.
  • CPU is good at fetching big amounts of data from memory quickly.
  • CPU runs tasks sequentially.
  • CPU is responsible for executing a sequence of stored instructions, for example, multiplications.

Question 9: Which statement best describes GPU?

  • A solution for running a Recurrent Neural Network in deep learning.
  • Part of a computer system that is known as the processor or microprocessor.
  • A chip (processor) traditionally designed and specialized for rendering images, animations and video for the computer’s screen.
  • The core of a CPU.

Question 10: What is NOT TRUE about GPUs?

  • GPUs have many cores, sometimes up to 1000 cores.
  • x86 is one of the prevalent GPUs by Intel.
  • GPUs can handle many computations.
  • GPUs are good at fetching large amounts of memory.

Question 11: Why is GPU much better for deep learning than CPU?

  • CPUs are not optimized and not the proper use for fetching high dimensional matrices.
  • Deep Neural Networks need a heavy matrix for multiplication, and GPUs can do it in parallel.
  • A Deep Neural Network needs to fetch input images as matrices from main memory, and GPUs are good at fetching big chunks of memory.
  • All of the above.

Question 12: “NVIDIA is one of the main vendors of GPU offered with CUDA software” True or False?

  • TRUE
  • FALSE

Question 13: What is CUDA?

  • A high-level language, which helps you write programs for NVIDIA GPU
  • A software on top of AMD cards to make it faster
  • A accelerating hardware that have recently succeeded in reducing the training time by several times over.
  • All of the above

Question 14: Which one is NOT a hardware accelerator for training of deep learning?

  • FPGAs
  • AMD cards
  • Tensorflow Processing Units (TPUs)
  • NVIDIA GPUs
  • OpenCL

Question 15: “Tensorflow Processing Units (TPUs) are Google’s hardware accelerator solution developed specifically for TensorFlow and Google’s open-source machine learning framework.” TRUE or FALSE?

  • TRUE
  • FALSE

Question 16: What is TRUE about the limitations of using GPUs as hardware accelerators for deep learning? (Select one or more)

  • GPUs are not very fast for data parallelism, which is a must in deep neural networks.
  • GPUs have a limited memory capacity (currently up to 16 GB) so this is not practical for very large datasets.
  • You cannot easily buy GPUs and embed them into your local machine because of hardware dependencies and incompatibilities.
  • GPUs are not compatible with CPUs.

Question 17: What are the options out there as hardware accelerators for deep learning?

  • A cluster of GPUs on-premise
  • GPU services provided by cloud providers
  • A cluster of GPUs in the cloud
  • Personal computers with an embedded GPU
  • All of the above

Question 18: Is this statement about using personal computers with an embedded GPU for deep learning problems TRUE or FALSE? “A laptop with a recent NVIDIA GPU is not usually enough to solve real deep learning problems. In this case, you need to scale down the dataset or the model, which often delivers bad results.”

  • FALSE
  • TRUE

Question 19: What is the problem with using GPUs provided by cloud providers?

  • They are properly used only for experiments with sample data to verify many scenarios before going full scale.
  • You need to upload all your data on the cloud and you may not feel comfortable uploading it into public clouds.
  • You cannot find services that offer multi-GPU access.
  • They cannot run as fast as personal computers.

Question 20: Which statement is NOT TRUE about PowerAI:

  • On the PowerAI platform, NVLink connections between GPUs reduce GPU wait time.
  • PowerAI handles Big Data by transfering all data into GPUs.
  • On the PowerAI platform, full NVLink connectivity between CPU and GPU allows a faster way to “reload” data into GPU.
  • PowerAI takes advantage of NVLink for faster GPU-GPU communication.

Introduction to Accelerating Deep Learning with GPUs

Accelerating Deep Learning with GPUs is a pivotal aspect of modern AI research and applications. This fusion of deep learning algorithms with Graphics Processing Units (GPUs) has revolutionized the field by significantly enhancing training speeds and model complexities.

At its core, deep learning involves training complex neural networks on vast amounts of data to recognize patterns and make predictions. However, traditional CPUs, while versatile, often lack the processing power required for such intensive computations. This limitation led to the adoption of GPUs, originally designed for rendering graphics in video games, as a powerhouse for parallel processing tasks.

Here’s a structured introduction to accelerating deep learning with GPUs:

  1. Why GPUs?:
    • GPUs excel in handling parallel computations, making them ideal for the matrix and vector operations common in deep learning.
    • Unlike CPUs, which prioritize sequential processing, GPUs can execute numerous tasks simultaneously, significantly speeding up training times.
  2. Parallelism:
    • Deep learning models involve millions to billions of computations, which can be executed simultaneously.
    • GPUs leverage thousands of cores to distribute computations across multiple threads, enabling efficient parallel processing.
  3. CUDA and CuDNN:
    • NVIDIA’s CUDA (Compute Unified Device Architecture) platform provides a framework for programming GPUs for general-purpose computing.
    • CuDNN (CUDA Deep Neural Network library) offers optimized routines for deep learning tasks, further enhancing performance.
  4. Frameworks and Libraries:
    • Popular deep learning frameworks like TensorFlow, PyTorch, and Keras have native support for GPU acceleration.
    • These frameworks seamlessly integrate with GPUs, allowing developers to leverage their power without extensive modifications to their code.
  5. Model Training:
    • During model training, GPUs accelerate the computation of gradients, backpropagation, and optimization algorithms such as stochastic gradient descent.
    • Large-scale datasets can be efficiently processed in parallel, reducing training times from weeks or months to mere hours or days.
  6. Inference:
    • In addition to training, GPUs also expedite the inference phase, where trained models make predictions on new data.
    • Real-time applications, such as image and speech recognition, benefit from the rapid inference capabilities of GPUs, enabling responsive user experiences.
  7. Cloud Computing:
    • Cloud service providers offer GPU instances, allowing researchers and businesses to access powerful computing resources on-demand.
    • This democratizes access to GPU acceleration, particularly for smaller organizations or individuals with limited hardware resources.
  8. Future Directions:
    • Advancements in GPU architecture, such as NVIDIA’s Tensor Cores and AMD’s RDNA architecture, continue to push the boundaries of deep learning performance.
    • Research into specialized hardware, like TPUs (Tensor Processing Units), aims to further optimize deep learning tasks for specific applications.

In summary, accelerating deep learning with GPUs unlocks unprecedented computational power, enabling researchers and practitioners to tackle increasingly complex AI challenges with greater efficiency and speed.

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