What Does MVP Stand For? It’s Not What You Think.
October 7, 2024
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This course is part of Deep Learning with Real-World Projects Specialization
Instructor: Packt - Course Instructors
Included with
Recommended experience
Intermediate level
Designed for tech professionals and students with programming basics. Prior knowledge of linear algebra, calculus, and probability is recommended.
Recommended experience
Intermediate level
Designed for tech professionals and students with programming basics. Prior knowledge of linear algebra, calculus, and probability is recommended.
Understand the concepts of perceptrons and multi-layer neural networks.
Apply training techniques, including backpropagation and regularization.
Analyze convolutional neural networks for image and video analysis.
Evaluate and create deep learning projects using frameworks like TensorFlow and Keras.
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September 2024
3 assignments
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Embark on a journey through the intricate world of deep learning and neural networks. This course starts with a foundation in the history and basic concepts of neural networks, including perceptrons and multi-layer structures. As you progress, you'll explore the mechanics of training neural networks, covering activation functions and the backpropagation algorithm.
The course then advances to artificial neural networks and their real-world applications, drawing inspiration from the human brain's architecture. You'll gain practical insights into input and output layers, the Sigmoid function, and key datasets like MNIST. Specialized topics such as feed-forward networks, backpropagation, and regularization techniques, including dropout strategies and batch normalization, are thoroughly covered. You'll also be introduced to powerful frameworks like TensorFlow and Keras. The course concludes with an in-depth study of convolutional neural networks (CNNs), focusing on their applications and principles for image and video analysis. This course is ideal for tech professionals and students with a basic understanding of programming and mathematics, particularly linear algebra, calculus, and basic probability.
In this module, we will introduce the basic concepts of deep learning and neural networks. We will explore the history, fundamental structures like perceptrons, and the process of training neural networks. Additionally, we'll cover important concepts such as activation functions and representations.
10 videos2 readings
In this module, we will delve into the intricacies of artificial neural networks. We'll explore how the human brain inspires these networks, the detailed workings of perceptrons, and the layers that constitute neural networks. Additionally, we'll cover the sigmoid function and understanding MNIST data.
18 videos
In this module, we will focus on feed-forward networks, their operation modes, and the dimensions involved. We'll break down the pseudocode required for batch processing and introduce vectorized methods to optimize neural network training.
7 videos1 assignment
In this module, we will dive deep into backpropagation, a crucial method for training neural networks. We'll introduce the loss function, break down the backpropagation process into multiple parts, and cover associated concepts such as the sigmoid function and stochastic gradient descent (SGD).
17 videos
In this module, we will cover regularization techniques to enhance neural network performance. We'll explore dropout methods, batch normalization in multiple parts, and introduce tools like TensorFlow and Keras that facilitate these processes.
8 videos
In this module, we will explore Convolutional Neural Networks (CNNs) and their applications. We'll discuss the ideas behind CNNs, analyze how they process image and video data, and implement essential operations like convolution, stride, padding, and pooling. We'll also cover combining networks for complex tasks.
15 videos1 reading2 assignments
Packt helps tech professionals put software to work by distilling and sharing the working knowledge of their peers. Packt is an established global technical learning content provider, founded in Birmingham, UK, with over twenty years of experience delivering premium, rich content from groundbreaking authors on a wide range of emerging and popular technologies.
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Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.
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