Deep Learning and Neural Networks Python Keras For Dummies
 
In today's technology-driven world, "Machine Learning" and "Deep Learning" reign supreme, permeating various facets of our daily existence, from personalized Google suggestions to tailored movie recommendations. The demand for skills in "Deep Learning," "Machine Learning," and Data Science is fervent in today's tech landscape.

Despite the widespread adoption of these technologies, a misconception persists that mastering them necessitates a deep dive into complex algorithms and mathematics. However, akin to driving a car where intricate knowledge of the internal combustion engine is not obligatory, a grasp of the basics suffices. Similarly, this course balances theory and practical application, leveraging user-friendly Python libraries like Keras.

The course begins with an introduction to Deep Learning and neural networks and distinguishes between Deep Learning and Machine Learning. It then guides students through setting up the Python environment, covering Python fundamentals, and installing crucial libraries such as Theano, Tensorflow, and Keras.

The curriculum encompasses Multi-Layer Perceptrons, neural network training, and fundamental concepts like Overfitting and dropout regularization. Students use real-world datasets, including Pima Indians Onset of Diabetes, Iris Flowers Classification, and Sonar Returns, to construct models and assess accuracy.

Advanced topics such as checkpointing, model training history, and management of learning rates are also addressed. Additionally, students explore Convolutional Neural Networks (CNNs) and tackle intricate projects like hand-written digit recognition and object recognition in photographs utilizing datasets like MNIST and CIFAR-10.

Upon completion, students are awarded an experience certificate, enriching their career prospects in the rapidly expanding realm of Deep Learning and Neural Networks. As the technological landscape progresses towards sentient machines, mastery of these skills is imperative for remaining pertinent and competitive.