terradue’s Post

🔍 Unsupervised Deep Learning: Dive into Auto-Encoders and Their Application for Clustering Image Data! 📊 In my latest Medium article, I explore how Deep Auto-Encoders can enhance clustering analysis for high-dimensional datasets, such as images, by learning lower-dimensional representations of the data. Starting with a basic overview of the architecture and training process, I guide you through implementing Auto-Encoders in PyTorch - a state-of-the-art deep learning library. Discover how to achieve more accurate clustering results on the high-dimensional MNIST dataset 📊💡 Read more here: https://lnkd.in/eStJfrHx #MachineLearning #DeepLearning #DataScience #Clustering #AutoEncoders #PyTorch #ContinuousLearning

The use cases of TensorFlow highlight the fact that it is one of the best tools for software developers who are focusing on deep learning algorithms.

Improving Deep Learning Model Performance through Data Augmentation 💥💥 GET FULL SOURCE CODE AT THIS LINK 👇👇 👉 https://lnkd.in/dEUNCVMB Deep learning models, particularly convolutional neural networks (CNNs), have shown remarkable performance in various domains, especially image classification. However, achieving high accuracy involves addressing several challenges, including lack of labeled data and overfitting. Among effective solutions to these problems is data augmentation, which synthesizes new data from the existing dataset, helping to diversify and enrich the training data. This approach enhances the model's ability to generalize, thus improving overall performance. In this post, we will discuss data augmentation techniques, their implementation using popular deep learning libraries like TensorFlow, and suggestions for further reading to delve deeper into this topic. Data augmentation techniques include cropping, flipping, rotation, shearing, and changing color channels. These methods modulate the input data slightly, ensuring the model adaptively learns from multiple variations of the original images. Let's explore an example using the popular MNIST dataset for handwritten digit recognition. First, we initialize our TensorFlow model: ```python import tensorflow as tf mnist = tf.keras.datasets.mnist (x_train, y_train), (x_test, y_test) = mnist.load_data() x_train = x_train / 255.0 x_test = x_test / 255.0 model = tf.keras.models.Sequential([ tf.keras.layers.Flatten(input_shape=(28, 28)), tf.keras.layers.Dense(128, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(64, activation='relu'), tf.keras.layers.Dropout(0.2), tf.keras.layers.Dense(10, activation='softmax') ]) ``` Next, we augment our data: ```python import tensorflow as tf from tensorflow.keras.preprocessing.image import ImageDataGenerator datagen = ImageDataGenerator(rotation_range=15, width_shift_range= #STEM #Programming #Technology #Tutorial #improving #deep #learning #model #performance #through #data #augmentation Find this and all other slideshows for free on our website: https://lnkd.in/dEUNCVMB #STEM #Programming #Technology #Tutorial #improving #deep #learning #model #performance #through #data #augmentation https://lnkd.in/dyPW57rV

Design an Easy-to-Use Deep Learning Framework

🎉 Excited to share that I recently completed a DataCamp course on PyTorch! 🎉 Making the switch from TensorFlow to PyTorch has been a fun and rewarding journey. 🚀 The hands-on exercises and comprehensive content in the course have deepened my understanding of deep learning and empowered me to tackle more complex projects. Looking forward to applying these new skills in real-world scenarios! 🧠💻 #PyTorch #DeepLearning #DataScience #MachineLearning #LifelongLearning #DataCamp

Implemented 10171259_credit_risk_prediction_based_on_DL_and_SMOTE Journal used for the implementation Journal title: Proposal of a model for credit risk prediction based on deep learning methods and SMOTE techniques for imbalanced dataset. This is an IEEE paper published in 2021 and this was implemented using neural network https://lnkd.in/dGyxWJvt

Design an Easy-to-Use Deep Learning Framework

I have completed DataCamp's Introduction to Deep Learning with Keras!

Deep dive into deep learning with Keras.🤿

Recently, I see and hear a lot about artificial intelligence, machine learning and deep learning. Talking about science and knowledge is very good and it makes me happy that the world community is interested in science, but what is important here is the scientific opinion. they criticize that they have no science and knowledge about even programming, let alone artificial intelligence and data science and deep learning. How beautiful it is if when we are interested in a field of knowledge, we eagerly go to learn it and become an expert in that field, then we talk about it and reject or accept the knowledge of other scientists. And if we are not experts, we should refer to experts and not talk about ourselves.