Updated 27 Aug
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A Beginner's Guide to Deep Learning
Gain insights into deep learning fundamentals, explore perceptron and advanced models, and discover practical coding in NumPy and Keras. Test your knowledge with quizzes and coding challenges.
4.9
63 lessons
2 projects
20h
Created by Educative
What's included
- 63 lessons across 9 modules
- 20 quizzes with instant feedback
- 2 projects in preconfigured environments
- AI Tutor on every lesson
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What You'll Learn
- Identify core deep learning terms, real-world applications, and how deep learning differs from ML
- Distinguish supervised, unsupervised, and reinforcement learning using regression and classification examples
- Implement perceptron forward propagation in NumPy using weighted sums, bias, and step/sigmoid activations
- Compute MSE and cross-entropy loss across datasets to assess model prediction error
- Train perceptrons with stochastic and batch gradient descent, tuning learning rate and momentum
- Build and train multilayer neural networks in NumPy using feedforward, backpropagation, and softmax output
- Develop, evaluate, save, and reload Keras models for regression and classification with validation and early stopping
Learning Roadmap
1.
✨Before We Begin
✨Before We Begin
Get familiar with the basics of deep learning and practical Python coding skills.
2.
✨Introduction to Deep Learning
✨Introduction to Deep Learning
Grasp the fundamentals of machine learning paradigms, deep learning principles, and neural networks.
3.
✨Simple Perceptron Models in NumPY
✨Simple Perceptron Models in NumPY
16 Lessons
16 Lessons
Work your way through perceptron models, coding, prediction methods, and optimization techniques using NumPy.
4.
✨Towards Deep Neural Networks in NumPY
✨Towards Deep Neural Networks in NumPY
8 Lessons
8 Lessons
Break down the steps to foundational concepts of neural networks and their training processes.
5.
🖥️ Project: Build a Letter Classification Model
🖥️ Project: Build a Letter Classification Model
8 Lessons
8 Lessons
Solve problems in building and training a letter classification model with deep learning.
6.
✨Building Deep Learning Models with Keras
✨Building Deep Learning Models with Keras
11 Lessons
11 Lessons
Tackle building, compiling, training, evaluating, and utilizing deep learning models using Keras.
7.
✨Fine-tune Keras Model
✨Fine-tune Keras Model
8 Lessons
8 Lessons
Master the steps to optimize, validate, and hypertune deep learning models for improved accuracy.
8.
🖥️ Project: Build a Digit Recognition Model
🖥️ Project: Build a Digit Recognition Model
4 Lessons
4 Lessons
Try out building and evaluating a digit recognition model using the MNIST dataset.
9.
✨Conclusion
✨Conclusion
2 Lessons
2 Lessons
Walk through the key points of ANNs, RNNs, and CNNs and their applications.
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