Free Download Tensors for Data ScientistsPublished 8/2026
MP4 |
Video: h264, 1920x1080 |
Audio: AAC, 44.1 KHz, 2 Ch
Language: English |
Duration: 1h 32m |
Size: 372.85 MB
Tensors for Data Scientists: Deep Learning Data Structures with NumPy & TensorFlow
Understand tensors, ranks, shapes, and tensor operations that form the mathematical foundation of modern deep learning.
Explain how neural networks learn using weights, activations, forward propagation, loss functions, and backpropagation.
Apply tensor operations, reshaping, broadcasting, and masking using NumPy, PyTorch, and TensorFlow.
Interpret tensor flow, data shapes, and GPU acceleration in CNNs, Transformers, and real-world deep learning models.
RequirementsNo prior experience in AI, machine learning, or programming is required. Basic high school mathematics is helpful but not mandatory. A computer with internet access and a willingness to learn are all you need.
DescriptionArtificial Intelligence is transforming every industry-but before you can build powerful AI models, you need to understand the concepts that make them work.
This course is designed to help complete beginners develop a strong conceptual foundation in deep learning by mastering tensors, neural networks, tensor operations, and the mathematics that powers modern AI. Instead of jumping straight into code, you'll first build an intuitive understanding of how data flows through neural networks and why frameworks like PyTorch and TensorFlow work the way they do.
You'll begin by learning what tensors are, how ranks and shapes represent multidimensional data, and why tensors are considered the language of deep learning. From there, you'll explore how neural networks process information using weights, biases, activation functions, forward propagation, loss functions, and backpropagation.
As your understanding grows, you'll dive deeper into tensor operations such as reshaping, broadcasting, matrix multiplication, boolean masking, and batch processing. You'll also discover how tensors are used inside Convolutional Neural Networks (CNNs), Transformers, attention mechanisms, and embedding layers.
The course includes practical demonstrations using NumPy, PyTorch, and TensorFlow to reinforce concepts without overwhelming you with complex programming. Finally, you'll learn how GPUs accelerate deep learning by performing tensor computations in parallel, giving you a complete picture of how modern AI systems are built and trained.
What you'll learn- Understand tensors, tensor ranks, shapes, and multidimensional data representation.
- Learn how neural networks transform data through layers using weights and activation functions.
- Master tensor operations such as reshaping, broadcasting, matrix multiplication, and masking.
- Build an intuitive understanding of forward propagation, loss functions, and backpropagation.
- Explore tensor flow in CNNs, Transformers, embeddings, and attention mechanisms.
- Work with tensors using NumPy, PyTorch, and TensorFlow.
- Understand how GPUs accelerate deep learning through parallel tensor computation.
Who this course is for- Beginners with little or no background in AI or deep learning.
- Students pursuing Artificial Intelligence, Machine Learning, or Data Science.
- Python developers who want to understand the concepts behind deep learning frameworks.
- Anyone curious about how modern AI models work under the hood.
By the end of this course, you'll have the conceptual knowledge needed to confidently move on to building and understanding real-world deep learning models, making it an ideal foundation before tackling advanced AI, machine learning, computer vision, or natural language processing courses.
Who this course is forThis course is for beginners, students, developers, and AI enthusiasts who want to build a strong conceptual foundation in tensors, neural networks, deep learning, and modern AI frameworks before moving to advanced machine learning.
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