FeedForward Neural Network using TensorFlow, Keras

A Feed Forward Neural Network is an artificial neural network in which the connections between nodes do not form a cycle. The opposite of a feed-forward neural network is a recurrent neural network in which certain pathways are cycled. The feed-forward model is the simplest form of a neural network, as information is only processed in one direction. While the data may pass through multiple hidden nodes, it always moves in one direction and never backward. We introduce several new concepts associated with the general problem of classification involving more than two classes. This is sometimes referred to as multinomial regression or softmax regression when the number of classes is more than two. Specifically, we will see how to classify hand-written digits from the MNIST dataset using a feed-forward Multilayer Perceptron (MLP) network. Topics Covered ✅Load and Split the MNIST Dataset ✅Dataset Preprocessing ✅Model Architecture ✅Model Implementation ✅Model Evaluation ❓FAQ How do neural networks extrapolate: from feedforward to graph neural networks? What is a feedforward neural network? Can feedforward neural networks handle the rotation of images? How is a deep feedforward neural network made in keras? How does a feedforward neural network work? How to determine weights in a feedforward neural network? How to train a feedforward neural network? What are feedforward neural networks used for? What are single-hidden layer feedforward neural networks? ⭐️ Time Stamps:⭐️ 0:00-00:57: Introduction 00:57-01:35: MNIST Datasets for training and tests 01:35-02:56: Representing Image data as set of features 02:56-03:21: Image Labels 03:21-04:32: Label Encoding 05:00-05:50: Integer Encoding and One-hot Encoding 05:50-07:20: Network Architecture 07:20-07:45: Softmax Function 07:45-08:04: Argmax Function 08:04-08:55: Loss Function 08:55-10:01: Implementing Model in Keras 10:01-10:19: Output Layer 10:19-10:44: Model Summary 10:44-12:00: Compiling the Model 12:00-12:48: Training the Model 12:48-14:02: Results 14:02-14:40: Accuracy Plots 14:40-15:47: Model Evaluation 15:47-17:31: Confusion Matrix 17:31-18:25: Examples 18:25-20:13: Summary Resources: 📚 Blog post Link: https://learnopencv.com/image-classification-using-feedforward-neural-network-in-keras/ 🖥️ On our blog - https://learnopencv.com we also share tutorials and code on topics like Image Processing, Image Classification, Object Detection, Face Detection, Face Recognition, YOLO, Segmentation, Pose Estimation, and many more using OpenCV(Python/C++), PyTorch, and TensorFlow. 🤖 Learn from the experts on AI: Computer Vision and AI Courses YOU have an opportunity to join the over 5300+ (and counting) researchers, engineers, and students that have benefited from these courses and take your knowledge of computer vision, AI, and deep learning to the next level.🤖 https://opencv.org/courses #️⃣ Connect with Us #️⃣ 📝 Linkedin: https://www.linkedin.com/in/satyamallick/ 📱 Twitter: https://twitter.com/LearnOpenCV 🔊 Facebook: https://www.facebook.com/profile.php?id=100064001437329 📸 Instagram: https://www.instagram.com/learnopencv/ 🔗 Reddit: https://www.reddit.com/user/spmallick 🔖Hashtags🔖 #keras #tensorflow #machinelearning #neuralnetwork #objectdetection #deeplearning #computervision #learnopencv #opencv #tutorial #kerastutorial #tensorflowtutorial