Keras & Neural Networks

A project-based course where you will build and train a bidirectional LSTMs neural network model to recognize named entities in text data using Keras with a TensorFlow backend. This is a key tool for information extraction and a preprocessing step for other NLP applications.

For those who want to go beyond the basics, this course covers advanced deep learning topics using Keras. You'll learn about functional AP Is, custom loss functions, and how to build more complex models.

Visual Guide to Transformer Neural Networks - (Episode 1) Position Embeddings

Stanford University course on deep learning for computer vision. Learn to implement, train and debug CNNs and gain understanding of cutting-edge research.

THIS IS A COMPLETE NEURAL NETWORKS & DEEP LEARNING TRAINING WITH TensorFlow & KERAS IN PYTHON!It is a full 7-Hour Python TensorFlow & Keras Neural Network & Deep Learning Boot Camp that will help you learn basic machine learning, neural networks and deep learning using two of the most important Deep Learning frameworks- TensorFlow and Keras. HERE IS WHY YOU SHOULD ENROLL IN THIS COURSE:This course is your complete guide to practical machine & deep learning using the TensorFlow & Keras framework in Python.. This means, this course covers the important aspects of Keras and TensorFlow (Google's powerful Deep Learning framework) and if you take this course, you can do away with taking other courses or buying books on Python TensorFlow and Keras based data science. In this age of big data, companies across the globe use Python to sift through the avalanche of information at their disposal and advent of TensorFlow and Keras is revolutionizing Deep Learning... By gaining proficiency in Keras and and TensorFlow, you can give your company a competitive edge and boost your career to the next level.THIS IS MY PROMISE TO YOU: COMPLETE THIS ONE COURSE & BECOME A PRO IN PRACTICAL KERAS & TensorFlow BASED DATA SCIENCE!But first things first. My name is Minerva Singh and I am an Oxford University MPhil (Geography and Environment) graduate. I recently finished a PhD at Cambridge University (Tropical Ecology and Conservation). I have several years of experience in analyzing real life data from different sources using data science related techniques and producing publications for international peer reviewed journals. Over the course of my research I realized almost all the Python data science courses and books out there do not account for the multidimensional nature of the topic and use data science interchangeably wit

Data Scientist has been ranked the number one job on Glassdoor and the average salary of a data scientist is over $120,000 in the United States according to Indeed! Data Science is a rewarding career that allows you to solve some of the world’s most interesting problems!This course is designed for ML practitioners who want to enhance their skills and move up the ladder with Deep Learning!This course is made to give you all the required knowledge at the beginning of your journey so that you don’t have to go back and look at the topics again at any other place. This course is the ultimate destination with all the knowledge, tips, and tricks you would require to work in the Deep Learning space.It gives a detailed guide on TensorFlow and Keras along with in-depth knowledge of Deep Learning algorithms. All the algorithms are covered in detail so that the learner gains a good understanding of the concepts. One needs to have a clear understanding of what goes behind the scenes to convert a good model to a great model. This course will enable you to develop complex deep-learning architectures with ease and improve your model performance with several tips and tricks.Deep Learning Algorithms Covered:1. Feed Forward Networks (FFN)2. Convolutional Neural Networks (CNNs)3. Recurring Neural Networks (RNNs)4. Long Short-Term Memory Networks (LSTMs)5. Gated Recurrent Unit (GRUs)6. Autoencoders7. Transfer Learning8. Generative Adversarial Networks (GANs)Our exotic journey will include the concepts of:1. The most important concepts of TensorFlow and Keras from very basic.2. The two ways of model building i.e. Sequential and Functional API.3. All the building blocks of Deep Learning models are explained in detail to enable students to make decisions while training their model and improving model performance.4. Hands-on learning of Deep Learning algorithms from the beginner

Welcome to a game-changing learning experience with "ChatGPT for Deep Learning using Python Keras and TensorFlow". This unique course combines the power of ChatGPT with the technical depth of Python, Keras, and TensorFlow to offer you an innovative approach to tackling complex Deep Learning projects. Whether you're a beginner or a seasoned Data Scientist, this course will significantly enhance your skill set, making you more proficient and efficient in your work.Why This Course?Deep learning and Artificial Intelligence are revolutionizing industries across the globe, but mastering these technologies often requires a significant time investment (for theory and coding). This course cuts through the complexity, leveraging ChatGPT to simplify the learning curve and expedite your project execution. You'll learn how to harness the capabilities of AI to streamline tasks from data processing to complex model training, all without needing exhaustive prior knowledge of the underlying mathematics and Python code.Comprehensive Learning Objectives By the end of this course, you will be able to apply the most promising ChatGPT prompting strategies and techniques in real-world scenarios:ChatGPT Integration: Utilize ChatGPT effectively to automate and enhance various stages of your Data Science projects, including coding, model development, and result analysis.Data Management: Master techniques for loading, cleaning, and visualizing data using Python libraries like Pandas, Matplotlib, and Seaborn.Deep Learning Modeling: Gain hands-on experience in constructing and fine-tuning Neural Networks for tasks such as Image Recognition with CNNs, Time Series prediction with RNNs and LSTMs, and classification and regression with Feedforward Neural Networks (FNN), using ChatGPT as your assistant.<strong

This course will guide you through how to use Google's TensorFlow framework to create artificial neural networks for deep learning and also the basics of Machine learning! This course aims to give you an easy to understand guide to the complexities of Google's TensorFlow framework in a way that is easy to understand and its application . Data Scientists enjoy one of the top-paying jobs, with an average salary of $120,000 according to Glassdoor and Indeed. That's just the average! And it's not just about money - it's interesting work too! If you've got some programming or scripting experience, this course will teach you the techniques used by real data scientists and machine learning practitioners in the tech industry - and prepare you for a move into this hot career path. This is a comprehensive course with very crisp and straight forward intent. This course covers a variety of topics, including Neural Network Basics TensorFlow detailed,Keras,Sonnet etc Artificial Neural Networks Types of Neural network Feed forward network Radial basis network Kohonen Self organizing maps Recurrent neural Network Modular Neural networks Densely Connected Networks Convolutional Neural Networks Recurrent Neural Networks Machine Learning Deep Learning Framework comparisons There are many Deep Learning Frameworks out there, so why use TensorFlow? TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the grap