Deep learning is an artificial intelligence (AI) method that teaches computers to process data in a way inspired by the human brain. Deep Learning Training models can recognize complex patterns in images. Text, sounds, and other data to generate accurate insights and predictions. So, You can use deep learning methods. To automate tasks that typically require human intelligence, such as describing images or transcribing an audio file into text. Deep learning algorithms are neural networks that are modeled after the human brain. For example, the human brain contains millions of interconnected neurons that work together to learn and process information.
Similarly, deep learning neural networks, or artificial neural networks, are composed of many layers of artificial neurons working together within a computer. Artificial neurons are software modules called nodes that use mathematical calculations to process data. So, Artificial neural networks are deep learning algorithms that use these nodes to solve complex problems. The Deep learning is a subset of machine learning. Deep Learning Training algorithms emerged in an attempt to make traditional machine learning techniques more efficient.
- Manually label hundreds of thousands of animal images.
- Have machine learning algorithms process these images.
- Test these algorithms on a set of unknown images.
- Find out why some results are inaccurate.
- Refine the dataset by labeling new images to improve the accuracy of the results.
Deep Learning Course Details
Deep Learning Training is a fundamental program that will enable you to participate in the creation of leading-edge AI technology by helping you understand the capabilities, challenges, and repercussions of deep learning.
You will create and train neural network designs such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Transformers in this Specialization, as well as learn how to improve them with tactics such as Dropout, BatchNorm, Xavier/He initialization, and more. Prepare to tackle real-world problems such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more by learning theoretical ideas and their industry applications using Python and TensorFlow.
Benefits of Deep Learning Training
Many industries are being transformed by AI. The Deep Learning Specialization provides the road for you to take the next step in the field of AI by supporting you in gaining the information and abilities needed to advance your career.
Project for Applied Learning
You’ll be able to by the end
• Develop and train deep neural networks, use vectorized neural networks, find architecture parameters, and apply deep learning to your applications.
• Employ best practices to train and generate test sets and analyze bias/variance while developing DL applications, as well as conventional NN methodologies, optimization algorithms, and TensorFlow to implement a neural network.
• Construct a Convolutional Neural Network, apply it to visual detection and identification tasks, utilize neural style transfer to make art, and apply these techniques to image, video, and other 2D/3D data. • Employ strategies for reducing errors in ML systems.
• Develop and train Recurrent Neural Networks and their variants (GRUs, LSTMs), use RNNs for character-level language modeling, collaborate with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers for Named Entity Recognition and Question Answering.
BITS is an IT Institute provides resources for a wide range of learning requirements, including learning materials, self-paced and live training, and educator programs. Individuals, teams, companies, instructors, and students may now find all they need to expand their skills in AI, accelerated computing, accelerated data science, graphics and simulation, and more.
Instructor-led live Deep Learning (DL) training classes, available online or on-site, demonstrate the basics and applications of Deep Learning through hands-on practice and cover topics such as deep machine learning, deep structured learning, and hierarchical learning.
- Module1: What is Deep Learning
- Module 2 Neural Networks
- Module 3 Convolutional Neural Networks (CNN)
- Module 4 Convolutional neural network with Python
- Module 5 Representation learning and Generative learning
Module 6 The Deep Learning applications for Reinforcement Learning and NLP
- Weights and Activation functions
- Data Preprocessing
- Image augmentation in OpenCL
- Neural Networks
- Loss function
- MNIST example for Neural Networks
- What are convolutional neural network and tensor flow
- Convolutional layer
- Pooling layer
- How to create the layers in Python
- Convolutional neural network with Python
- Transfer learning
- RCNN (Fast RCNN, Faster RCNN, Mask RCNN)
- Auto encoder
- Generative adversarial Networks
- Simple example with MNIST dataset
- Limitation of GAN and Deep Convolutional GANs
- Key elements of Reinforcement Learning
- Open AI Gym Toolkit
- How is a Deep Learning applied to RL?
- Robotic Manipulation using Deep RL
- Application of the Deep learning to Natural Language Processing
- Automatic Language Translation
- Automatic Text Classification
Duration: 1 Month
Timing: 9AM-11AM, 11AM-1PM, 1PM-3PM, 3PM-5PM, 5PM-7PM, 7PM-9PM