
Learn what TensorFlow 2 is, why it powers deep neural network training, and how to install it on Windows or Mac using Anaconda and Jupyter Notebook.
Implement model evaluation using precision, recall, and confusion matrix with the metrics library. Print the confusion matrix and the classification report from training data to assess a decision tree model.
Explore gradient descent types batch, stochastic, and mini-batch, and learn how softmax converts neural network outputs into normalized probabilities for multi-class classification.
Build a neural network for digit classification with TensorFlow on a 28x28 grayscale dataset (60,000 training, 10,000 test images), using flattening, dense layers, and a softmax output.
Explore how to determine the optimal number of clusters using the elbow method, analyze error versus cluster count, and lay the groundwork for implementing k means.
Implement the k-means algorithm on a custom dataset X, specifying the number of clusters and random_state for centroid initialization, then obtain labels, predict points, and view cluster centers.
Train a cnn on cifar-10 to classify 10 color classes and observe limitations of standard techniques; plan to boost accuracy with architecture tweaks and transfer learning in the next video.
Undoubtedly, TensorFlow is one of the most popular & widely used open-source libraries for machine learning applications. Apart from it, TensorFlow is also heavily used for dataflow and differentiable programming across a range of tasks. Because of this and a lot of other promises, hundreds of individuals are keen on exploring TensorFlow for AI & ML, Data Science, text-based application, video detection & others.
In order to cater to all our student’s needs for learning TensorFlow, we have curated this exclusive practical guide. It will teach you Practical TensorFlow with more from a training perspective rather than just the theoretical knowledge.
What makes this course so unique?
It will help you in understanding both basics and the advanced concepts of TensorFlow along with the codes in a practical manner! Upon completing this course, you will be able to learn various essential aspects of this famous library. It will unfold with the basic introduction covering graphs, Keras, supervised learning and others.
In the later sections, you will learn more about AI & ML models like decision trees, linear regression & logistic regression along with evaluating models, gradient descent & digit classification. Concepts of CNN are also covered along with its architectures, layers, K-means algorithm, K-means implementation, facial recognition & others.
This course includes:
Section 1- TensorFlow 2.0, Graphs, Automatic Differentiation, Keras and TensorFlow, Intro to Machine Learning, Types of Supervised Learning.
Section 2- Decision Trees, Linear Regression, Logistic Regression, Model Evaluation.
Section 3- Gates and Forward Propagation, Complex Decision Boundaries, Backpropagation, Gradient Descent Type and Softmax, Digit Classification.
Section 4- CNN, Layers of CNN, Famous CNN Architectures.
Section 5- K-Means Algorithm, Centroid Initialization, K-Means ++, Number of Clusters, K-Means Implementation, Principal Component Analysis, Facial Recognition using PCA.
Searching for the online course that will teach you TensorFlow practically? Search no more!! Begin with this course today to get your hands dirty with TensorFlow!!