Learn and Build Deep Learning Models Today !

Welcome to the Mastering Deep Learning Fundamentals with Python Book!

The Book is comprised of 10 chapters and 5 Python projects. Each project you build will be an opportunity to prove your skills and demonstrate what you’ve learned in your lessons. Building a project is one of the best ways both to test the skills you've acquired and to demonstrate your newfound abilities to future employers.

Educational Objectives: Become an expert in neural networks, and learn to implement them from scratch and using frameworks like Keras and TensorFlow.

Prerequisite Knowledge: Make sure to set aside adequate time on your calendar for focused Learn. In order to succeed in this book. The first chapters of the book will give you all the basic knowledge of probability, statistic, calculus (multivariable derivatives) and linear algebra (matrix multiplication needed for mastering Deep Learning. Please, take time to learn them.

Contact Info: While going through the program, if you have questions about anything, you can reach us at [email protected].

What’s Inside This Book?

Part I : Fundamentals of Deep learning

• Fundamentals of Probability
• Fundamentals of Statistics
• Fundamentals of Linear Algebra
• Introduction to Machine Learning and Deep Learning
• Fundamentals of Machine Learning
• Fundamentals of Neural Networks and Deep Learning
• Deep Learning Parameters and Hyper-parameters
• Deep Neural Networks Layers
• Deep Learning Activation Functions
• Deep Learning Loss Functions
• Deep Learning Optimization Algorithms
• Convolutional Neural Network
• Recurrent Neural Networks
• LSTM
• Recursive Neural Networks
• Bonus Course
• Conclusion

Part II : Deep Learning in Practice (In Jupyter notebooks)

• Project 0: Introduction to Python for AI
• Project 1: Introduction to Keras and OpenCV
• Project 2: Simple Linear Regression in Python
• Project 3: Multivariate Regression in Python
• Project 4: Binary Classification for image recognition using CNN
• Project 5: Multiclass Classification for image recognition using CNN
• Project 6: Text Classification using RNN

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