Abhilash Nelson has launched a new course called "Deep Learning & Neural Networks with Python: For Dummies." This course aims to provide a balance between learning the basics of deep learning and implementing the Keras Library functions using Python. The course starts with theory sessions that cover the differences between deep learning and machine learning, the history of neural networks, and their applications and workflow.
The next session addresses whether to choose deep learning or machine learning for an upcoming AI project. Factors and scenarios that help in making this decision are discussed. The Python environment is set up by installing the Anaconda platform and all necessary dependencies. Basic Python syntax, including flow control and data structures, is also covered. Libraries such as Keras, TensorFlow, and Theano are installed for deep learning projects.
The course then delves into Multi-Layer Perceptrons (MLP), which are fundamental to deep-learning neural networks. The terminology and major steps in training a neural network are explained in detail. Several datasets are used throughout the course for practical implementation. These include a classification model for predicting iris flower species, a regression model for estimating Boston house prices, and a model for predicting diabetes based on health data.
Checkpointing is introduced as a tool to save progress and prevent loss of work during interruptions. Overfitting, a major issue in deep learning, is addressed using dropout regularization to control the model's learning rate. Convolutional Neural Networks (CNN) are explored through experiments involving handwritten digit recognition using the MNIST dataset and object recognition in photographs using the CIFAR-10 dataset.
By completing this course, students can gain valuable skills in deep learning and neural networks in high demand in the technology industry. An experience certificate is also provided as proof of skills gained during the course. This course offers a comprehensive introduction to thinking machines and can be a great starting point for those looking to jumpstart their careers in this field.
