Unlock the power of deep learning and elevate your machine learning skills with our comprehensive deep neural networks course. This hands-on program covers deep learning fundamentals, including artificial neural networks, activation functions, bias, data, and loss functions. Learn Python basics focused on data science, and master tools like Matplotlib, NumPy, and Pandas for data cleaning and visualization. Progress from the MP Neuron model to the Perceptron, Sigmoid Neuron, and Universal Approximation Theorem, exploring ReLU and SoftMax activation functions. Gain practical experience with TensorFlow 2.x, creating and training deep neural networks, evaluating their performance, and fine-tuning for optimal results. By the course's end, you'll be on your way to becoming a deep-learning expert. This beginner-friendly course is perfect for students and professionals aiming to stay updated on AI. A basic understanding of programming is recommended but not required, as foundational Python skills are covered in the course.
Probability Distribution Table
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Skills You'll Learn
Artificial Intelligence and Machine Learning (AI/ML), Data Manipulation, Data Processing, Regression Analysis, Keras (Neural Network Library), Machine Learning Methods, NumPy, Machine Learning, Artificial Intelligence, Artificial Neural Networks, Deep Learning, TensorFlow, Pandas (Python Package), Network Model, Data Science, Matplotlib, Python Programming
From the lesson
Basic Probability
In this module, we will cover basic probability concepts. You will learn about random variables, their importance, types, and probability distribution tables, as well as the concept of entropy loss in the context of deep learning.
Taught By
Packt - Course Instructors