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We're here to help you find itThe Mastering in Deep Learning course is a comprehensive program designed for learners to gain an in-depth understanding of deep learning and its applications. Starting with Module 1, students get grounded in Machine Learning Fundamentals, covering essential concepts and algorithms. Transitioning into Module 2, the course introduces TensorFlow 2.0, leveraging Google Colab for hands-on experiences.
As learners progress through the meticulously structured modules, they delve into the core of Deep Learning, exploring its advantages, limitations, and real-life use cases. The course covers various deep neural network architectures, including CNNs, RNNs, and GANs, providing a solid foundation in understanding and implementing these networks.
With a focus on practical skills, the course guides students through Neural network training, Hyperparameter tuning, Regularization, and Optimization algorithms. By the end of the course, participants will be well-equipped with the knowledge and skills for mastering deep learning, empowering them to tackle complex problems and advance their careers in the field of AI.
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♱ Excluding VAT/GST
Classroom Training price is on request
You can request classroom training in any city on any date by Requesting More Information
To ensure that you have the best learning experience in the Mastering in Deep Learning course, here are the minimum required prerequisites:
These prerequisites are intended to provide a foundation for the course material and ensure you can follow along with the technical content. If you are unfamiliar with any of these areas, we recommend reviewing relevant introductory materials before starting the course.
The Mastering in Deep Learning course is tailored for professionals seeking advanced knowledge in AI and machine learning technologies.
This comprehensive course equips learners with a deep understanding of machine learning and deep learning principles, enabling them to design, train, and optimize advanced neural networks for real-world applications.
Optimization algorithms are methods used to find the best solution or outcome in a given scenario, often dealing with minimizing or maximizing certain functions. In technology, these algorithms solve problems where you need to make the best decision, such as reducing costs, improving efficiency or achieving higher precision. They play a crucial role in areas like machine learning, where they adjust model parameters to minimize prediction errors. Such algorithms also underpin various business and engineering tasks, helping to optimize performance, resource allocation, and operational strategies efficiently.
Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data. Similar to how we learn from experience, deep learning algorithms perform a task repeatedly, each time tweaking it slightly to improve the outcome. This technology is behind many advanced applications, such as facial recognition, voice-enabled devices, and self-driving cars. Mastering deep learning involves understanding these complex models to solve real-world problems by processing data with a level of accuracy and refinement that mimics human decision-making.
Neural network training is the process of teaching a computer to make decisions and predictions based on data. It involves inputing large amounts of data into a neural network, a system inspired by the human brain, and adjusting it repeatedly to minimize errors in its predictions or decisions. This is done using algorithms that optimize the network's performance, allowing it to learn from and adapt to the data provided. Over time, the network becomes better at accurately identifying patterns and making decisions, a key component in mastering deep learning.
Hyperparameter tuning is a critical process in mastering deep learning, where you adjust the settings of an algorithm before training to optimize performance. These settings, called hyperparameters, dictate how the algorithm learns and can include things like learning rate or number of hidden layers. Tuning involves experimenting with different values to find the best combination that allows the model to make the most accurate predictions. Unlike model parameters, which are learned automatically, hyperparameters are set manually, making this process an essential skill for improving model efficacy.
Regularization is a technique in deep learning used to prevent the model from overfitting. Overfitting occurs when a model learns the training data too well, including its noise and inaccuracies, leading to poor performance on new, unseen data. Regularization works by adding a penalty on the larger weights of the model, encouraging it to develop simpler patterns. This helps in enhancing the model's ability to generalize, making it perform better on new inputs. Techniques like L1 and L2 regularization are common methods used to achieve this effect.
Machine Learning Fundamentals involve teaching computers to learn and make decisions from data without being explicitly programmed. It uses algorithms that analyze data, learn from it, and then apply what they’ve learned to make informed decisions. Essentially, it's about creating models that can process complex data sets to predict outcomes or understand patterns. This is foundational for applications like recommendation systems, speech recognition, and more advanced tech such as mastering deep learning, which focuses on larger, more complex datasets and structures to mimic human neural networks.
TensorFlow 2.0 is an open-source library developed by Google for running machine learning and deep learning processes. It simplifies model building with easy-to-use APIs, enabling both beginners and experts to develop powerful machine learning models. TensorFlow 2.0 supports various computing platforms like CPUs, GPUs, and TPUs, optimizing computational resources for performance. It's widely used in various applications ranging from voice recognition and text-based applications to complex tasks like mastering deep learning algorithms. This version focuses on simplicity and ease of use, with updates like improved model saving and loading, and a more intuitive way to develop custom models.
The Mastering in Deep Learning course is tailored for professionals seeking advanced knowledge in AI and machine learning technologies.
This comprehensive course equips learners with a deep understanding of machine learning and deep learning principles, enabling them to design, train, and optimize advanced neural networks for real-world applications.
