Data Science Deep Learning and Neural Networks in Python Free download. Requirements:
- Possess basic math skills, including calculus derivatives, matrix arithmetic, and probability.
- Install Numpy and Python; TensorFlow installation will be covered in the lectures.
- Familiarity with the content of the logistic regression course (cross-entropy cost, gradient descent, neurons, XOR, donut) provides valuable context.
Description:
- Explore the foundations of AI technologies like OpenAI ChatGPT, GPT-4, DALL-E, Midjourney, and Stable Diffusion in this course.
- Begin building your first artificial neural network using deep learning techniques, extending from logistic regression concepts.
- Utilize Python and Numpy to construct non-linear neural networks, progressing from binary classification to multiple classes via the softmax function.
- Understand the crucial “backpropagation” training method, implementing it in Numpy for both efficiency and comprehension.
- Learn to code a neural network using Google’s TensorFlow library.
- Ideal for those aspiring to master deep learning, machine learning, and data science, offering practical examples and projects.
- Course projects involve predicting user actions on a website based on user data and using deep learning for facial expression recognition.
- Gain insights into the newest developments in neural networks and their applications.
- The course prioritizes hands-on learning, focusing on building and understanding rather than mere usage of machine learning models.
- Emphasizes the importance of implementation for true understanding, aligning with Richard Feynman’s philosophy.
- Prerequisites include calculus, matrix arithmetic, probability, Python coding skills, and familiarity with linear models like regression.
- The suggested order for taking courses is outlined in the “Machine Learning and AI Prerequisite Roadmap” lecture.
Who this course is for:
- Students interested in machine learning, provide essential knowledge for neural networks.
- Professionals aiming to incorporate neural networks into their machine learning and data science workflows, understanding both capabilities and limitations.
Data Science Deep Learning and Neural Networks in Python Download
Deep Learning:
Deep Learning is a subset of machine learning that involves neural networks with multiple layers, enabling the model to automatically learn hierarchical representations of data, leading to complex feature extraction and pattern recognition.
Neural Networks in Python:
Neural Networks in Python refer to the implementation of artificial neural networks using the Python programming language, leveraging libraries like Numpy and TensorFlow. These networks consist of interconnected nodes organized in layers, capable of learning and making predictions from data through iterative training processes.
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