What you'll get
- 6+ Hours
- 4 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
Synopsis
- Understand the inner workings of Deep Learning beyond diagrams and "black box" code.
- Explore how neural networks are constructed from fundamental components, starting with the neuron.
- Build and implement a neural network from the ground up using Python and NumPy.
- Develop neural networks with Google's TensorFlow framework
- Identify various neural network architectures and the types of problems each is suited to solve
- Derive the backpropagation algorithm from first principles
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Project on Deep Learning - Convolutional Neural Network | 1h 06m | ✔ | View Curriculum |
| Project on Deep Learning - Artificial Neural Network | 2h 29m | ✔ | View Curriculum |
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Project on Deep Learning: Stock Price Prognostics | 2h 17m | ✔ | View Curriculum |
| Project on Deep Learning: Handwritten Digits Recognition | 1h 02m | ✔ | View Curriculum |
Description
The field of artificial intelligence is advancing at an unprecedented pace. Self-driving cars are covering millions of miles, IBM Watson is diagnosing patients with remarkable accuracy, and Google DeepMind's AlphaGo has defeated world champions in a game that relies heavily on intuition. As AI tackles increasingly complex challenges, Deep Learning has emerged as the key technology capable of solving them, making it a central component of modern AI.
Deep Learning is reshaping technology and society alike. It powers innovations ranging from autonomous vehicles and medical diagnostics to facial recognition, deepfake creation, language translation, and music generation. Yet, its influence extends beyond cutting-edge applications. Deep Learning is increasingly adopted as a core technique in machine learning, data science, and statistical modeling. Startups use it for data mining and dimensionality reduction, governments deploy it to detect tax fraud, and researchers apply it to uncover patterns in experimental data.
Today, Deep Learning impacts nearly every sector of technology, business, and entertainment—and its significance continues to grow year by year.
Requirements
- Basic math: calculus, matrices, probability
- Python and NumPy setup
- TensorFlow installation covered in lectures
- Prior knowledge of logistic regression concepts (cross-entropy, gradient descent, neurons, XOR, donut) is helpful
Target Audience
- Machine learning students will gain key insights to succeed in neural networks.
- Professionals will learn to apply advanced models and understand their limitations.
