Understanding Deep Learning The Rise of Deep Learning How Deep Learning Works Key Components of Deep Learning Systems Applications Across Industries Opportunities Created by Deep Learning Challenges and Limitations Ethical and Societal Implications The Future of Deep Learning Understanding Deep Learning Deep learning is a subset of...

Introduction to Machine Learning. Why is machine learning such a hot technology? What machine learning is and why it's so effective. There are 3 kinds of machine learning. What is supervised and unsupervised? What is reinforcement learning? Differences between supervised and unsupervised Learning. Applications of Machine...

Deep Learning How do Spark and TensorFlow Simplify Deep Learning Large-Scale Feature Engineering and Data Preprocessing Two use cases and explain how you can use Spark Hyperparameter Tuning How do you choose the Right Hyperparameter? Monitoring and Debugging Distributed Deep Learning Jobs Deploying the Model and scale ...

Introduction to Logistic Regression Difference Between Linear and Logistic Regression Sigmoid Function Explained Binary Classification with Logistic Regression Assumptions and Conditions Model Training and Evaluation Metrics Implementation in Python Multiclass Logistic Regression Advantages and Limitations Applications...

Introduction to Multilayer Perceptron History of Multilayer Perceptron Multi-layer Perceptron in TensorFlow Compressed lesson On Multi-Layer Perceptron Neural Networks For additional subtleties on the MLP, see the post Multi-layer perceptron work Stepwise Implementation Multi-layer Perceptron Neural Networks Examples in Business ...

Introduction to Keras Beginning with Keras Keras Principles Keras layers Keras use and Application What makes Keras exceptional? Keras client experience How Keras support the case of being multi-backend and multi-stage? Keras with Deep Learning Frameworks Highlights of Keras library Multi-input and multi-yield models Keras Backend Benefits of...

Introduction to confusion Matrix Confusion Matrix - Not so confusing! Have you been in a situation where you expected your machine learning model to perform really well, but it shows poor accuracy? You've done all the hard work - so where did the classification model go wrong? How can you fix it? There are lots of ways to measure the...

Bagging Vs Boosting: We all use decision tree techniques in daily life to make decisions. Organisations use these supervised machine learning techniques such as decision trees to make better decisions and generate more surplus and profit. Ensemble methods combine different decision trees to produce better predictive results, the latter using...

Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a...

Support Vector Machine” (SVM) is a supervised machine learning algorithm that can be used for both classification or regression challenges. Support Vectors are simply the coordinates of individual observation. The SVM classifier is a frontier that best segregates the two classes (hyper-plane/ line). [contact-form-7 id="76417" title="Subscriber...