In this module, we will lay the foundation for understanding deep learning by covering essential topics such as artificial neural networks, activation functions, and bias. We will also explore the role of data, various applications, models, loss functions, and learning algorithms crucial for model performance.

In this module, we will provide a crash course on the basics of Python programming, essential for deep learning. You will learn how to install and use Jupyter Notebook and Google Colab, understand data types, containers, control statements, and implement functions and classes in Python.

In this module, we will delve into Python libraries crucial for data science. You will learn how to handle arrays with NumPy, manipulate data using Pandas, and visualize data with Matplotlib. We will cover topics from basic data structures to advanced data cleaning and plotting techniques.

In this module, we will explore the MP Neuron model, also known as the McCulloch-Pitts model. You will gain an understanding of the data intuition, learn how to find parameters, and develop a mathematical intuition for this fundamental concept in neural networks.

In this module, we will focus on implementing the MP Neuron model in Python. You will learn how to import datasets, apply train-test split, and modify data. By the end of this section, you will have created an MP Neuron class from scratch and practiced with an assignment.

In this module, we will summarize the key concepts and practical implementation of the MP Neuron model. We will review the important points and ensure you have a solid understanding through a recap and evaluation assignments.

In this module, we will cover the Perceptron model, discussing its representation, loss function, and parameter updates. You will understand how the update rule works and see its practical implementation in programs.

In this module, we will implement the Perceptron model in Python. You will learn to program the model and visualize its accuracy and performance with increasing epochs, enhancing your practical skills in deep learning.

In this module, we will transition from Perceptron to Sigmoid Neuron. You will learn about the limitations of the Perceptron, the benefits of the Sigmoid Neuron, and gain insights into gradient descent for model optimization.

In this module, we will implement the Sigmoid Neuron using Python. You will learn to download and standardize datasets, and create a class for the Sigmoid activation function, solidifying your understanding through practical assignments.

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.

In this module, we will explore deep neural networks. You will learn why they are important, and through practical programming, understand the concept of linear separation of data, preparing you for more complex deep learning models.

In this module, we will delve into the Universal Approximation Theorem. You will learn its significance, confirm its effectiveness with practical examples, and discuss the challenges of building deep neural networks from scratch.

In this module, we will focus on TensorFlow 2.x for deep learning. You will learn to build, train, and evaluate neural networks using TensorFlow, with a recap of deep learning concepts and a summary to prepare for more advanced topics.

In this module, we will cover activation functions in deep learning. You will learn about different activation functions provided by TensorFlow and understand common network configurations used in deep learning tasks.

In this module, we will apply deep learning concepts. You will transition from shallow to deep learning, understand Keras basics, solve classification and regression problems, and explore advanced TensorFlow techniques and subclassing methods.