
Explore convolutional neural networks for computer vision by examining a grayscale 28x28 image, its 784 pixels, and how pixel intensity values from 0 to 255 define darkness and brightness.
Explore how rgb images store red, green, and blue values per pixel, unlike grayscale, and see splitting rgb images into separate channels with 255 normalization, illustrated by a tv image.
Explore how convolutional filters operate on grayscale and RGB images, applying a 3x3 kernel to a 6x6x3 input with stride one and zero padding to produce a 4x4 feature map.
Explore how convolutional layers in neural networks transform input images into feature maps using filters, edge detection, pooling, and activations, leading to a fully connected layer.
Explain max pooling in convnets, using 2x2 windows with stride two (and 3x3 examples), showing how to pick maximum values and padding effects.
Explore image classification with a convolutional neural network in Keras, using a 60,000-image 28 by 28 digits dataset, with normalization, one-hot encoding, dropout, and softmax, via Adam optimizer.
Shows you how to build a convolutional neural network in TensorFlow for mnist (28x28, 10 classes) with convolution, max pooling, dropout, and dense layers, trained softmax cross-entropy and Adam optimizer.
Explore autoencoders in TensorFlow that compress 28 by 28 MNIST images from 784 to 128 dimensions and decode to reconstruct the originals. Understand the encoder-decoder workflow and the latent state.
Explore generative adversarial networks by building a generator and discriminator in TensorFlow to generate 28x28 images from random noise and distinguish real from fake.
Anyone interested in Deep Learning
Students who have at least high school knowledge in math and who want to start learning Deep Learning
Any intermediate level people who know the basics of Machine Learning or Deep Learning, including the classical algorithms like linear regression or logistic regression and more advanced topics like Artificial Neural Networks, but who want to learn more about it and explore all the different fields of Deep Learning
Anyone who is not that comfortable with coding but who is interested in Deep Learning and wants to apply it easily on datasets
Any students in college who want to start a career in Data Science
Any data analysts who want to level up in Deep Learning
Any people who are not satisfied with their job and who want to become a Data Scientist
Any people who want to create added value to their business by using powerful Deep Learning tools
Any business owners who want to understand how to leverage the Exponential technology of Deep Learning in their business
Any Entrepreneur who wants to create disruption in an industry using the most cutting edge Deep Learning algorithms
Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning. Learning can be supervised, semi-supervised or unsupervised.
Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, convolutional neural networks and Transformers have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance